Draft Agenda Planning Committee Meeting Highlights and... · Draft Agenda – Planning Committee...

Draft Agenda Planning Committee Meeting March 4, 2014 | 1:00―5:00 p.m. Central March 5, 2014 | 8:00 a.m.―12:00 p.m. Central Hyatt Regency St. Louis at The Arch 315 Chestnut Street St. Louis, MO 63102 NERC Antitrust Compliance Guidelines and Public Announcement Introductions Agenda Items

1. Administrative ― Secretary

a. Arrangements

b. Safety Briefing

c. Declaration of Quorum

d. Planning Committee (PC) Membership* - Welcome new members

e. Future Meeting Schedule

2014 Meeting Dates Time Location Hotel June 10 June 11

1PM to 5PM (Eastern) 8AM to 12PM (Eastern) Orlando, FL Hyatt Regency Orlando International Airport

September 16 September 17

1PM to 5PM (Pacific) 8AM to 12PM (Pacific) Vancouver, BC TBD

December 9 December 10

1PM to 5PM (Eastern) 8AM to 12PM (Eastern) Atlanta, GA Westin Buckhead

2. Consent Agenda item

a. Meeting Minutes – December 10-11, 2013 Atlanta, GA

b. March 2014 Meeting Agenda

3. Chair Remarks – 15 mins

a. RISC Update*

i. Analysis and Design Phase - Adaptation and Planning for Change*

ii. Analysis and Design Phase - Operational and System Models*

iii. RISC Triage – Infrastructure Maintenance*

iv. RISC Triage – Verification of Accuracy of Planning Models*

b. BOT Update

https://aws.passkey.com/event/10791753/owner/988/home

https://www.starwoodmeeting.com/StarGroupsWeb/booking/reservation?id=1307054206&key=F29B7

http://www.nerc.com/comm/PC/Agenda%20Highlights%20and%20Minutes%202013/Draft%20PC%20Meeting%20Minutes%20-%20December%2010-11,%202013.pdf

4. Regional Updates (Provided by RRO Representatives) – 60 mins

5. Brainstorming and Roundtable Discussion – 30 mins

6. Committee Business

a. Essential Reliability Services Task Force (ERSTF) – John Moura and Noha Abdel-Karim Objective: Essential Reliability Services (ERS) are the elemental ‘reliability building blocks’ from resources (generation and demand) necessary to maintain Bulk Power System (BPS) reliability. ERS are operational attributes from conventional generation, such as providing reactive power to maintain system voltages and physical inertia to maintain system frequency, necessary to reliably operate the BPS. In contrast, retirement of conventional generation in near future across many areas in North America, coupled with increasing variable generation installation can adversely impact the availability of ERS unless due considerations are given in planning and operations.

A joint PC-OC Task Force is being formed. The ERSTF has a multi-faceted purpose that includes developing a technical foundation of ERS; educating and informing industry, regulators, and the public about ERS; developing an approach for tracking and trending ERS; formulating recommendations to ensure the complete suite of ERS are provided and available; and providing roadmap necessary for operating and maintaining a reliable grid.

1. DRAFT Scope - Approval 2. DRAFT Workplan – Approval 3. ERS Whitepaper/Reference Document – Review and Provide Feedback

Reviewers of initial scope draft included Brian Evans-Mongeon, Noman Williams, John Feit, Serge Fortin, and Paul Kure. The PC and OC will be seeking representation and membership to support the activities of the joint task force. Presentation: Yes Duration: 30 mins Background Item:

1. DRAFT ERSTF Scope* 2. DRAFT ERSTF Workplan* 3. DRAFT Whitepaper (to be sent out to PC members prior to meeting)

Personal Notes:

b. Integration of Variable Generation Task Force (IVGTF) – John Moura and Michael Milligan Objective:

1. DRAFT IVGTF Task 1-6 Probabilistic Methods – Review and Provide Feedback • The objective of this report is to summarize the potential influence on power system operating and

planning decision problems associated with increased uncertainty caused by high variable generation penetration, and to describe the role that probabilistic methods can play in improving the basis on which the various decisions are made.

• PC Reviewers Requested 2. IVFTF Task 1-7 Follow-up – Solicitation for SME team to liaison with IEEE

• PC-approved report (December 2013) shows that distribution-connected generation—particularly solar photovoltaic—is expected to grow very fast over the next decade, potentially increasing installed capacity. A large amount of distribution-connected generation or distributed energy resources (DER) can have significant impact on the reliability of the bulk power system. Of particular concern to bulk system reliability in North America is the lack of disturbance tolerance requirements for DER, specifically frequency and/or voltage ride-through. With respect to disturbance tolerance, DER interconnection standards are inconsistent with the direction in which bulk system standards are evolving. A compounding issue is that existing DER interconnection standards contain “must-trip” provisions that raise the possibility of widespread loss of distributed generation during severe transmission contingencies.

• SME Team Requested

Draft Agenda – Planning Committee Meeting, March 2014 2

3. IVGTF Next Steps – Final Report with Consolidated Recommendations • The IVGTF task work has culminated in a numerious recommendations stemming from 12 tasks. Some

recommendations included the need for new and/or enhanced NERC Reliability Standards. A final report will be developed that summarizes the findings from the total IVGTF multi-task effort. The findings and recommendations provide a reference manual of best practices for planners, operators, regulators, decision-makers, and developers dealing with the reliability challenges associated with large amounts of variable generation. This report also highlights areas that require further development; particularly transmission impacts and essential reliability services.

Presentation: Yes Duration: 30 mins Background Item: 1. DRAFT IVGTF Task 1-6 Probabilistic Methods* 2. IVGTF Task 1-7 Disturbance Ride-Through Report

Personal Notes:

c. Performance Analysis Subcommittee – Melinda Montgomery, PAS Chair

Objective: 1. Approve the revised metric for ALR1-4 2. Approve the Severity Risk Index (SRI) enhancement whitepaper

• Revised based on feedback from OC/OC 3. Approve Retirement of KCMI. 4. Status update on ALR-CP1/compliance metric 5. GADS working group white paper

• In 2007, several wind industry representatives approached NERC with the request to develop standards and a voluntary reporting system for Wind. A team of industry representatives was assembled to develop a DRI for wind. In April 2011 the first GADS Wind DRI was posted on the NERC WEB site with voluntary report requirements. The industry has had two years to evaluate this document and identify additional needs or clarifications. As a result NERC reconvened the Wind GADS Sub-Team in early 2013. Substantial enhancements were identified; derates, delays, reserve shutdown, plant boundaries clarified, addition equipment codes, moving tables and examples to the appendix, modification of equations, multiple overlapping outage examples, data quality guidelines, data reporting levels, roll-up methods, revised figures and document history.

• Request for PC reviewers • Paper will be provided for review to OC/PC by March 25th

6. 2014 State of Reliability Report • Timeline and request for PC reviewers. • Schedule for OC/PC endorsement conference calls

Presentation: Yes Duration: 20 mins Background Item: 1. Revised metric templates for ALR 1-4.* 2. SRI Enhancement whitepaper* 5. DRAFT Wind GADS White Paper*

Personal Notes:

7. Discussion Items

a. Long-Term Reliability Assessment – Reliability Assessment Subcommittee – Layne Brown and Elliott Nethercutt

Objective: 1. Welcome new Chair – Layne Brown, WECC; solicitation for Vice Chair 2. Process Improvements – Presentation Only 3. Updates on Resource and Reserve Margin Categories and Definitions – Background and Presentation Only


http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/IVGTF17_PC_FinalDraft_December_clean.pdf

4. G-E Interdependencies Phase II Recommendations – Incorporation of gas availability in reliability assessment Presentation: Yes Duration: 20 mins Background Item: N/A Personal Notes:

b. Geomagnetic Disturbance Task Force – Frank Koza and Ken Donohoo Objective: Status report and information update on the Phase II GMD planning standard and approach for drafting the standard. Presentation: Yes Duration: 15 mins Background Item: N/A Personal Notes:

c. Southwest Outage Recommendation 16: Discrepancies Between RTCA and Planning Models – Amir Najafzadeh

Objective: Status update on implementation plan that addresses data change management processes that are part of the PC approved proposal to address discrepancies between RTCA and planning models. Presentation: Yes Duration: 15 mins Background Item: N/A Personal Notes

d. MOD Standards Update B – Bob Cummings, NERC Staff

Objective: Given its early recommendations and input, this agenda item summarizes the PC efforts and the success of the modeling data standards project (MOD B) over the last year. MOD B includes NERC Reliability Standard MOD-032 and MOD-033 as replacements for current MOD-010 and MOD-015. A short update provides the PC an opportunity to champion the work of SAMS and provide tangible evidence of how such input translated into real results. Highlight a “win” for the PC’s input in improving a set of standards. Presentation: Duration: 10 mins Background Item: N/A Personal Notes:

e. System Protection and Control Subcommittee Update – Bill Miller, SPCS Chair

Objective: 1. Special Protection System (SPS) Misoperation Reporting Template: Inform the Planning Committee of work by

ERO-RAPA and the SPCS to develop a common reporting template for SPS operations and misoperations, similar to the effort in 2010 to develop a common template for Protection Systems. The template will provide uniform reporting across the eight Regions. ERO-RAPA will work with NERC staff and SPCS to roll out the template to the Regions and stakeholdrs.

2. Order No. 754 Data Request on Protection System Single Points of Failure: Inform the Planning Committee on preliminary analysis of data for buses operated at 300 kV and higher, and present an overview of future activity.

Presentation: Yes Duration: 15 mins 1. Quarterly SPS Operation Reporting Form* Personal Notes:

f. FMDRAT Report on Demand Response in the Functional Model – John Moura, NERC Staff Objective: Update on PC subgroup's work with the Standards Committee, Functional Model Working Group, and the Fuctional Model Demand Response Advisory Team. Presentation: No Duration: 10 mins Background Item:

PC-Redlined Draft FMDRAT Report* PC-FMDRAT Meeting Notes*

Personal Notes:

g. DRAFT Eastern Interconnection Frequency Initiative White Paper – Troy Blalock


Objective: Frequency Response has been the focus of increased attention, analysis, development of standards, and deliberation by stakeholders. The foundation of this complex issue is the performance of governors. The starting point to address the issue is to have a firm understanding of current governor settings. The Resources Subcommittee believes the logical place to begin is to confirm generator data, make changes to settings where feasible and to share tools that can measure governor response. The Draf

The Resources Subcommittee (under the OC) is leading this generator characteristics survey request, but seeks endorsement from the PC and OC to increase survey participation. This activity is tied to the NERC Frequency Response Initiative -- Recommendation 1 to create guidelines for generators by type. The results of the voluntary governor survey will be use develop the guidelines and to augment the work already underway in correcting and enhancing the ERAG models. Presentation: Yes Duration: 10 mins Background Item:

DRAFT EI Frequency Initiative White Paper* Personal Notes:

h. MISO Update on Gas/Electric Interdependency Studies – Eric Thom, General Electric Objective: A MISO status report on ongoing activities concerning the Gas/Electric interdependencies. MISO work dovetails with NERC's Phase II Recommendations. Presentation: Yes Duration: 20 mins Background Item: N/A Personal Notes:

i. Environmental Regulations Update: Status on Proposed and Potential Rules -- Tentative Objective: Preliminary update on environmental initiatives and proposed rules for generation and its potential impact to Bulk Power System reliability. Presentation: Yes Duration: 15 Background Item: N/A Personal Notes:

j. SMS Update – Bob Cummings, NERC Staff Objective: Update Presentation: Duration: 5 mins Background Item: N/A

Personal Notes:

k. SW Outage Recommendations 5, 6, 7, & NERC2: Seasonal Assessments and Sub-100 kV Elements – Bill Harm, PJM Interconnection, L.L.C.

Objective: Proposal to address SW Outage Recommendations 5, 6, 7, & NERC2: Seasonal Assessments and Sub-100 kV Elements Presentation: Yes Duration: 20 mins Background Item: Document/Proposal Personal Notes:

8. Planning Committee and Subcommittee Project Queues Review – Planning Committee Work Plan

*Background items in the agenda package.


http://www.nerc.com/comm/PC/Related%20Files%202013/PC%20Work%20Plan.pdf

Antitrust Compliance Guidelines I. General It is NERC’s policy and practice to obey the antitrust laws and to avoid all conduct that unreasonably restrains competition. This policy requires the avoidance of any conduct that violates, or that might appear to violate, the antitrust laws. Among other things, the antitrust laws forbid any agreement between or among competitors regarding prices, availability of service, product design, terms of sale, division of markets, allocation of customers or any other activity that unreasonably restrains competition. It is the responsibility of every NERC participant and employee who may in any way affect NERC’s compliance with the antitrust laws to carry out this commitment. Antitrust laws are complex and subject to court interpretation that can vary over time and from one court to another. The purpose of these guidelines is to alert NERC participants and employees to potential antitrust problems and to set forth policies to be followed with respect to activities that may involve antitrust considerations. In some instances, the NERC policy contained in these guidelines is stricter than the applicable antitrust laws. Any NERC participant or employee who is uncertain about the legal ramifications of a particular course of conduct or who has doubts or concerns about whether NERC’s antitrust compliance policy is implicated in any situation should consult NERC’s General Counsel immediately. II. Prohibited Activities Participants in NERC activities (including those of its committees and subgroups) should refrain from the following when acting in their capacity as participants in NERC activities (e.g., at NERC meetings, conference calls and in informal discussions):

• Discussions involving pricing information, especially margin (profit) and internal cost information and participants’ expectations as to their future prices or internal costs.

• Discussions of a participant’s marketing strategies.

• Discussions regarding how customers and geographical areas are to be divided among competitors.

• Discussions concerning the exclusion of competitors from markets.

• Discussions concerning boycotting or group refusals to deal with competitors, vendors or suppliers.

NERC Antitrust Compliance Guidelines 2

• Any other matters that do not clearly fall within these guidelines should be reviewed with NERC’s General Counsel before being discussed.

III. Activities That Are Permitted From time to time decisions or actions of NERC (including those of its committees and subgroups) may have a negative impact on particular entities and thus in that sense adversely impact competition. Decisions and actions by NERC (including its committees and subgroups) should only be undertaken for the purpose of promoting and maintaining the reliability and adequacy of the bulk power system. If you do not have a legitimate purpose consistent with this objective for discussing a matter, please refrain from discussing the matter during NERC meetings and in other NERC-related communications. You should also ensure that NERC procedures, including those set forth in NERC’s Certificate of Incorporation, Bylaws, and Rules of Procedure are followed in conducting NERC business. In addition, all discussions in NERC meetings and other NERC-related communications should be within the scope of the mandate for or assignment to the particular NERC committee or subgroup, as well as within the scope of the published agenda for the meeting. No decisions should be made nor any actions taken in NERC activities for the purpose of giving an industry participant or group of participants a competitive advantage over other participants. In particular, decisions with respect to setting, revising, or assessing compliance with NERC reliability standards should not be influenced by anti-competitive motivations. Subject to the foregoing restrictions, participants in NERC activities may discuss:

• Reliability matters relating to the bulk power system, including operation and planning matters such as establishing or revising reliability standards, special operating procedures, operating transfer capabilities, and plans for new facilities.

• Matters relating to the impact of reliability standards for the bulk power system on electricity markets, and the impact of electricity market operations on the reliability of the bulk power system.

• Proposed filings or other communications with state or federal regulatory authorities or other governmental entities.

Matters relating to the internal governance, management and operation of NERC, such as nominations for vacant committee positions, budgeting and assessments, and employment matters; and procedural matters such as planning and scheduling meetings.

PLANNING COMMITTEE MEMBERSHIP 2013-2015

1 Elected to satisfy NEL ratio requirement for Canadian members. 2 Appointed, non-voting

Sector Member (Term)

1. Investor-owned utility Edward Scott, Duke Energy (15)

Kenneth Donohoo, Oncor Electric Delivery Company LLC (14)

2. State/municipal utility Stuart Nelson, Lower Colorado River Authority (14)

Vacant (1 position)

3. Cooperative utility Jay Farrington, PowerSouth Energy Cooperative (14)

Paul McCurley, National Rural Electric Cooperative Association (15)

4. Federal or provincial utility/Federal Power Marketing Administration

Serge Fortin, Hydro Québec TransÉnergie1 (14)

David Jacobson, Manitoba Hydro1 (14)

Bing Young, Hydro One Networks, Inc. (15)

Ian Grant, Tennessee Valley Authority (15)

5. Transmission dependent utility Brian Evans-Mongeon, Utility Services Inc. (14)

Tom Reedy, Florida Municipal Power Association (15)

6. Merchant electricity generator Robert Ramaekers, Tenaska, Inc. (14)

Kris Zadlo, Invenergy LLC (13)

7. Electricity marketer Steven Huber, Public Service Enterprise Group (14)

Jason Marshall, ACES Power Marketing (15)

8. Large end-use electricity customer Stacia Harper, Ohio Partners For Affordable Energy (15)

John Hughes, Electricity Consumers Resource Council (14)

9. Small end-use electricity customer Vacant (1Position))

Darryl Lawrence, Pennsylvania Office of Consumer Advocate (15)

10. Independent system operator/ regional transmission organization

Dan Rochester, IESO (14)

Mark Westendorf, Midwest ISO (15)

11. Regional reliability organization2 FRCC Pedro Modia, Florida Power & Light

MRO Dale Burmester, American Transmission Company, LLC

NPCC Phil Fedora, NPCC

RFC Paul Kure, ReliabilityFirst Corporation

SERC Clay Young, South Carolina Electric & Gas, Co.

SPP Noman Williams, Sunflower Electric Power Corporation

TRE Blake Williams, CPS Energy

WECC Branden Sudduth, WECC

12. State government Parveen Baig, Iowa Utilities Board (15)

John Feit, Public Service Commission of Wisconsin (14)

Officers Chairman: Ben Crisp, SERC Reliability Corporation (15)

Vice Chairman: David Weaver, PECO, An Exelon Company (15)

Government representatives2

U.S. federal government

Canadian federal government

Provincial government

Kent Davis, FERC

Vacant (1 position)

Vacant (1 position)

Vacant (1 position)

NERC Staff Coordinator2 John Moura, NERC

ERO Top Priority Reliability Risks 2014-2017 January 16, 2014 Executive Summary NERC staff has collected input from the Reliability Issues Steering Committee (RISC), the leadership of the Standing Committees, and other stakeholders and NERC staff to develop a set of ten top priority reliability risks for consideration in the development of the 2014-2017 ERO Enterprise Strategic Plan. These risks warrant additional focus. In rank order, the top priority reliability risks are: Changing Resource Mix, Resource Planning, Protection System Reliability, Uncoordinated Protection Systems, Extreme Physical Events, Availability of Real-Time Tools and Monitoring, Protection System Misoperations, Cold Weather Preparedness, Right-of-Way Clearances and 345-kV Breaker Failures. Recommendations for action and measures of success are included. Summary of Top Priority Reliability Risks NERC reviewed and assembled information from various committee reports and stakeholder inputs to develop a set of ten top priority reliability risks for use in the development of the 2014-2017 ERO Enterprise Strategic Plan. Starting with the RISC’s gap analyses1 presented to the Board of Trustees in August, 2013, staff undertook further review and analysis to identify any additional reliability risk areas of strategic importance for the ERO. Next, qualitative estimates of probability, consequence, and current level of risk management were prepared for each of the identified reliability risks within the chosen areas. These were used to identify ten top priority reliability risks requiring increased attention or additional activity. Following this analysis, recommendations were developed based on previous committee discussions; industry dialogue at the Reliability Leadership Summit; and past committee work products, such as the Long Term Reliability Assessment, the State of Reliability Report, and various special reports and assessments. These recommendations include a number of different approaches based on the various tools NERC has available to influence reliability (such as Guidelines, Information Requests, Training, Standards, and others). Listed below are the ten high priority reliability risks intended to focus ERO enterprise program areas, including training and education, standards setting, and compliance. Some of these priorities represent conclusions based on experience from reviewing actual system events (topics 3, 4, 6, 7, 8, 9 and 10) while others are more forward looking based on analysis, assessments, and forecasts (topics 1, 2, and 5). These priority risks will be considered in the development of the 2014-2017 ERO Enterprise Strategic Plan, which will in turn lead into the development of the business plan and budget, ultimately aligning resources across the ERO enterprise and program areas to help ensure the most efficient and effective approaches are undertaken to improve or maintain reliability. The list is in rank order. Detailed profiles for each reliability risk are provided after the list. 1. Changing Resource Mix. As the generation and load on the power system changes (e.g. integrated variable

resources, increased dependence on natural gas, increased demand-side management, new technologies deployed), the system is being brought into states that are significantly different than those considered when the system was designed and planned, exposing new vulnerabilities not previously considered. Fundamental operating characteristics and behaviors are no longer a certainty. Absent focused action to respond, this risk will increase.

1 See http://www.nerc.com/comm/RISC/Related%20Files%20DL/RISC_Priority_Recommendations-Jul_26_2013.pdf for the complete report.

http://www.nerc.com/comm/RISC/Related%20Files%20DL/RISC_Priority_Recommendations-Jul_26_2013.pdf

2. Resource Planning. Plant retirements (largely due to implemented environmental regulations; increased uncertainty in future resources due to other potential environmental regulations; and lower natural gas prices, which significantly affect power plant economics) are leading to cases where resources may be inadequate to ensure firm demand is served at all times. As the system continues to change, some regional assessments identify concerns with insufficient reserve margins as early as 2014 and 2015 in the ERCOT and Midcontinent ISOs.

3. Protection System Reliability. A fault accompanied by a failure of any Protection System component could in some cases result in instability, violation of applicable thermal or voltage ratings, unplanned or uncontrolled loss of demand or curtailment of firm transfers, or cascading outages. Such cases should be identified and addressed.

4. Uncoordinated Protection Systems. A lack of protection system coordination has the potential to increase the size and magnitude of events due to unnecessary trips. Uncoordinated protection systems were identified as contributing to the September 8, 2011 and August 14, 2003 events. Ensuring protection system coordination should be a priority for the ERO.

5. Extreme Physical Events. While the probability of physical events (such as physical attack, geomagnetic disturbance, or severe weather) that lead to extensive damage is low, the potential consequences are high enough that risk avoidance (reducing the probability) is insufficient as a sole risk management strategy. Risk mitigation efforts (reducing the potential consequence) are also underway, but additional focus is needed to address this risk and minimize both the magnitude and duration of the consequences of an extreme physical event.

6. Availability of Real-Time Tools and Monitoring. Not having the right tools and monitoring available to manage reliability in real time is a latent problem waiting for the right combination of events. Such events occurred August 14, 2003, and September 8, 2011, resulting in significant blackouts. Reducing the probability of entities not having key capabilities is essential.

7. Protection System Misoperations. NERC’s 2012 and 2013 State of Reliability Reports identified protection system misoperations as a significant threat to BPS reliability. Additional activities are needed to ensure this risk is managed adequately.

8. Cold Weather Preparedness. Lack of generator preparedness for cold weather extremes may result in forced outages, de-ratings, and failures to start. Insufficient availability of intra-regional generation and limits on import transfer capability may result in insufficient generation to serve forecasted load, resulting in load shedding.

9. Right-of-Way Clearances. Transmission Owners and applicable Generation Owners may have established incorrect ratings based on design documents, rather than on the actual facilities built. Managing to stay within SOL and IROL limits that are based on incorrect ratings may be inadequate to prevent equipment damage and/or cascading, instability, or separation.

10. 345-kV Breaker Failures. NERC has identified a potential trend of 345 kV SF6 puffer type breakers failing. Circuit breaker failures, in conjunction with another fault, may lead to more BES Facilities removed from service than required to clear the original fault. This poses a risk to the reliability of the BES.

Alignment with RISC Priority Recommendations The matrix below illustrates the alignment of these ten priority reliability risks with the broader risk areas recommended by the RISC. As reported by the RISC at the November 2013 Board Meeting, many of the risk areas they identified are in the process of being addressed and are on track for being well managed (see FOOTNOTE 1 and FOOTNOTE 2). However, a key priority area identified in the RISC report, emphasized at the 2013 Reliability Leadership Summit, and reported verbally at the November Board of Trustees meeting was the need to adapt and plan for change. Accordingly, this is reflected in the top two priority risks (nos. 1 and 2) identified in this document. While protection systems continue to be a focus area for the ERO (and several aspects have been addressed), NERC can use additional tools to improve performance in this area. As such, three of the ten top priority projects (nos. 3, 4, and 7)

ERO Top Priority Reliability Risks 2014-2017 - January 16, 2014 2

address protection system performance. One top priority (no. 6) deals with availability of real-time tools and monitoring, which was highlighted at the Leadership Summit as well. While activities are ongoing in this area, NERC can do more to address this risk. The four remaining top priority risks (nos. 5, 8, 9, and 10) are ones that NERC has concluded deserve additional attention, as explained in NOTE 3, NOTE 4, NOTE 5, and NOTE 6.

Alignment between ERO Top Priority Reliability Risks and RISC Priority Reliability Risk Areas

NOTE 1 – Current activities related to Cyber Attack are appropriately scoped and moving forward, and do not require additional ERO focus at this time. NOTE 2 – Current activities related to Workforce Capability and Human Error are appropriately scoped and moving forward, and do not require additional ERO focus at this time. NOTE 3 - The RISC recommended that Coordinated Attack on Multiple Facilities be treated as a medium priority, and that other risks involving physical damage (Geomagnetic Disturbance, Extreme Weather/Acts of Nature, Localized Physical Attack, and Electromagnetic Pulse) be treated as low priority. Their priority decisions were based in part on the “all-hazards planning” approach used by utilities when planning systems. However, this issue was discussed at some length at the 2013 Reliability Leadership Summit, and NERC management has concluded it deserves additional attention. NOTE 4 - The RISC recommended that Generator Availability and Equipment Maintenance and Management be treated as medium priority. NERC management has concluded that Cold Weather Preparedness is still a risk that needs further attention before it can be considered adequately managed, especially given the recent challenges experienced in January 2014. NOTE 5 - The RISC recommended that and Equipment Maintenance and Management be treated as medium priority, and that Transmission Right-of-Way be treated as a low priority. Until such time as the Facility Ratings Alert tasks are completed and the data indicates the risk has been adequate managed, NERC management has concluded this risk should continue to be a top priority. NOTE 6 - The RISC recommended that and Equipment Maintenance and Management be treated as medium priority. However, because of the potentially wide-ranging consequences of this issue and the relative ease of correcting the problem, NERC management has concluded this risk should continue to be a top priority.

Blue shading indicates alignmentbetween an ERO Top Priority and a RISC High Priority Area

Long Term Planning and

System Analysis

Resource and Transmission

Adequacy

Integration of New

Technologies and

Operations

Changing Resource Mix

Resource Planning

Protection System ReliabilityUncoordinated Protection SystemsExtreme Physical EventsSEE NOTE 3

Availability of Real-Time Tools and Monitoring Protection System MisoperationsCold Weather PreparednessSEE NOTE 4

Right of Way ClearancesSEE NOTE 5

345-kV Breaker FailuresSEE NOTE 6

High Priority Areas from the July 26, 2013 RISC Report "ERO Priorities: RISC Updates and Recommendations"

Cyber AttackSEE NOTE 1

Workforce Capability

and Human Error

SEE NOTE 2

Protection Systems

Monitoring and

Situational Awareness

Adaptation and Planning for Change

ERO

Top

Prio

rity

Relia

bilit

y Ri

sks 2

014-

2017


Cyber Attack, Workforce Capability and Human Error, and Other Considerations Both Cyber Attack and Workforce Capability and Human Error were identified by the Reliability Issues Steering Committee as high-priority areas of reliability risk. However, they have not been highlighted in this report as priorities, as the current activities related to each area are appropriately scoped and moving forward. Cyber Attack is a threat that is constantly evolving. As such, the ERO has made it a priority to build a framework that can be responsive to various attacks. This includes the establishment of the ES-ISAC, ongoing efforts to improve information sharing and analytic capabilities, and the use of various approaches to aid entities in preparation for Cyber Attack (such as the development of the CIP standards, creation and sharing of the Cyber-security Capability Maturity Model, and the biennial Grid Exercise). While work in this area remains important and will continue, it represents and ongoing need for focus, rather than an exception. Similarly, Workforce Capability and Human Error is important, but represents a continuing need for attention. NERC has enhanced its voluntary event analysis process, and both NERC and the industry are learning a great deal through this collaborative process. However, this is an area where focus is constantly changing, and what is needed is an ongoing operational capability, rather than a specific effort. Work in this area remains important and will continue as part of the ERO’s regular activities. Additional areas for work was have been identified as well. AC Substation Equipment Failure was noted in the 2013 State of Reliability report as an area of concern; however, sufficient information has not been gathered to support its inclusion in this document. As more is learned and actionable plans are developed, this area will be considered for inclusion as a top priority for the ERO. Other areas that have been identified but require additional analysis include outage coordination and the broad topic of infrastructure maintenance. The Reliability Risk Management Process (RRMP) The process used to develop this list is an interim approach as NERC transitions to a broader planning effort titled the Reliability Risk Management Process (RRMP). NERC staff worked with the RISC to develop this process to ensure the consideration of reliability risk and the development of associated reliability risk management projects are reflected in ERO business planning activities. Under the RRMP, the RISC will collect information to identify and prioritize broad areas of reliability risk. These areas then undergo a deeper analysis to identify specific reliability risks, how they can be measured, and what are the most critical risks within those broad areas that should be considered for further risk management activity. Following this analysis, strategies for managing these reliability risks are developed. Such strategies may include the use of Guidelines, Information Requests, Training, NERC Alerts, Technical Conferences, Research, Standards, and other tools. Strategies will be weighed for overall effectiveness and efficiency, and a plan will be developed that addresses each identified reliability risk with a set of approaches commensurate in scope to the level of risk being managed. Ultimately, these projects will be reflected in key ERO activities and the overall ERO planning process. The transition to the RRMP will be implemented and continuously improved over the next several years.


Risk Profile #1: Changing Resource Mix

Associated Reliability Risk Areas: Long Term Planning and System Analysis, Resource and Transmission Adequacy, Integration of New Technologies and Operations

As the generation and load on the power system changes (e.g. from integrated variable resources, increased

dependence on natural gas, increased demand-side management, new technologies deployed), the system is being brought into states that are significantly different than those considered when the system was designed and planned, exposing new vulnerabilities not previously considered. Fundamental operating characteristics and

behaviors are no longer a certainty. Absent focused action to respond, this risk will increase.

Detailed Problem Description The energy currently produced by large rotating machines is being replaced with energy produced by variable resources, demand response programs, and other new types of resources, which exhibit different characteristics with respect to some of the less obvious fundamental components of reliable operation (e.g., inertia, frequency response, maneuverability). At the same time, continuing improvements in energy efficiency and other changes in load composition impact characteristics and behavior of load, reactive power needs, and how the system operates and behaves during disturbances (e.g. fault-induced delayed voltage recovery). Finally, the ongoing shift in fuel from coal to natural gas brings its own sets of challenges, such as critical dependence on the just-in-time fuel supply chain of the natural gas infrastructure. All of these changes move the system toward different behaviors, operating characteristics, and levels of reliability risk.

Current Risk Management Activities • Ongoing problem evaluation. Research and analysis by NERC’s technical committees to address specific issues

related to this risk, such as the work being done by the Integrating Variable Generation Task Force and their reports and recommendations.

• Raising awareness. Annually publishing Long-Term Reliability and Seasonal Assessments, and NERC special assessments (such as Maintaining Bulk Power System Reliability While Integrating Variable Energy Resources – CAISO Approach (2013); Accommodating an Increased Dependence on Natural Gas for Electric Power (2013), A Primer of the Natural Gas and Electric Power Interdependency in the United States (2011), Accommodating High Levels of Variable Generation (2009)).

Recommendations Current activities provide information, but do not actively drive change. To directly respond to this risk, NERC should focus on these additional activities: • Execute previously proposed plans. Implement the recommendations that have been made in the assessments and

reports described above, such as: o Develop a standardized model of variable generation for stability and power-flow studies. o Develop guidelines for performing load composition modeling analysis; operations and emergency coordination

with gas suppliers and transporters; planning considerations for variable energy resources, performance and monitoring requirements for variable energy resources.

o Incorporate fuel risk and capacity impacts into long-term reliably assessments and planning activities. o Consider standards modifications to ensure appropriate applicability and alignment with reliability goals.

• Define essential reliability services by the end of 2014. Identify the fundamental components of reliable operation, and determine how to best ensure the need for those components is well understood and met (currently underway at the Planning Committee).

Measures of Success

• Stable and reliable levels for essential reliability services. • Accurate forecasts of system performance that account for characteristics of the changes to the resource mix.


Risk Profile #2: Resource Planning

Resource and Transmission Adequacy

Plant retirements (largely due to implemented environmental regulations; increased uncertainty in future resources due to other potential environmental regulations; and lower natural gas prices, which significantly affect power plant economics) are leading to cases where resources may be inadequate to ensure firm demand is served at all times. As

the system continues to change, some regional assessments identify concerns with insufficient reserve margins as early as 2014 and 2015 in the ERCOT and Midcontinent ISOs.

Detailed Problem Description

Environmental regulations, low natural gas prices, load forecasting uncertainty, and economic factors all contribute to an increased rate of plant retirements and a lack of construction. While demand response and energy efficiency may offset some of these losses, performance of those technologies can be uncertain, and each brings unique challenges. Long-term outages of multiple units to employ environmental retrofits also may have impacts. This all contributes to a lack of certainty regarding resource adequacy in North America over the next several years. Forecasts show potential deficiencies in reserve margins as early as 2014 and 2015 in the ERCOT and Midcontinent ISOs.


related to this risk, such as the work being done by the Reliability Assessments Subcommittee. • Raising awareness. Publishing Long-Term Reliability Assessments and NERC special assessments: Potential Impacts

of Future Environmental Regulations (2011); Resource Adequacy Impacts of Potential U.S. Environmental Regulations (2010).

Recommendations

While entities are aware of this issue and taking action, the amount of time required to implement solutions may be too long to provide relief in the near term, making a reactive approach inadequate. In order to be more proactive and provide assurance that issues are being addressed, NERC should undertake the following additional activities. Dependent on the results of these activities, NERC may need to consider whether its current body of Reliability Standards is sufficient to ensure this risk is appropriately managed. • Request information. Ask entities experiencing problems with resource planning to provide explanations of the

activities taken to manage this issue, as well as present regular progress updates. • Raise awareness. Continue emphasis on sharing information through assessments. Meet with regulators to discuss

the issue and explain the potential consequences. Issue press releases. Host technical conferences. • Promote Best Practices and Guidelines. Collaborate with entities that have experienced challenges in maintaining

sufficient reserve margins to develop best practices and guidelines to help other entities that may experience these challenges in the future manage the issue proactively.

• Measures of Success

• Resource adequacy in all North American regions should reverse declining trends and approach target reserve

margin levels by the end of the 2014-2017 period. Reserve margins forecasts should not fall below targets within the future three-year horizon.


Risk Profile #3: Protection System Reliability

Associated Reliability Risk Areas: Protection Systems

A fault accompanied by a failure of any Protection System component could in some cases result in instability, violations of applicable thermal or voltage ratings, unplanned or uncontrolled loss of demand or curtailment of firm

transfers, or cascading outages. Such cases should be identified and addressed.

Detailed Problem Description Protection Systems serve a vital role in defense against system disturbance events. However, there are cases where design of a protection system design may be insufficient - where a fault accompanied by a failure of any single Protection System component could result in outage significant event on the BES. One example is the June 24, 2004 Westwing outage event, which resulted in the loss of approximately 5,000 MW of generation and the potential for collapse of the Western Interconnection. NERC identified five events between 2004 and 2010 where a single point of failure on a protection system caused, in whole or in part, an event on the Bulk-Power System.


related to this risk, such as the work being done by System Protection and Control Subcommittee. • Promote Best Practices and Guidelines. System Protection and Control Subcommittee (SPCS) publication of a

document explaining the need for and design of redundancy in protection systems. • Section 1600 Data Request. NERC’s ongoing data request and analysis to determine the risks to the Bulk Power

System (“BPS”) posed by potential single point of failure events.

Recommendations

Current activities provide information, but do not actively drive toward change. Because of the number of events in which this risk has been implicated, NERC must take a more active role in addressing the problem by focusing on these additional activities:

• Continued data collection and analysis. NERC’s should continue its ongoing data request and associated analysis to

determine the risks to the Bulk Power System (“BPS”) posed by potential single point of failure events. • Mandatory Standards. Upon the completion of the data request described above and dependent on the associated

findings from that analysis, develop a standard that requires entities identify and address on an ongoing basis those cases in which a fault accompanied by a failure of any single Protection System component could result in instability, violations of applicable thermal or voltage ratings, unplanned or uncontrolled loss of demand or curtailment of firm transfers, or cascading outages.

Measures of Success • Zero instances in which a single point of failure on a protection system causes or contributes to an event on the Bulk

Power System.


Risk Profile #4: Uncoordinated Protection Systems

Associated Reliability Risk Areas: Protection Systems

A lack of protection system coordination has the potential to increase the size and magnitude of events due to unnecessary trips. Uncoordinated protection systems were identified as contributing to the September 8, 2011 and

August 14, 2003 events. Ensuring protection system coordination occurs should be a priority for the ERO.

Detailed Problem Description Protection systems that trip unnecessarily can contribute significantly to the size of an event. When protection systems are not coordinated properly, the order of execution can result in either incorrect elements being removed from service or more elements being removed than necessary. This can also occur with special protection systems, remedial action schemes, and under-frequency and under-voltage load shedding schemes. Such coordination errors occurred in the September 8, 2011 event (see Recommendation 19) and the August 14, 2003 event (see recommendation 21).

Current Risk Management Activities • Promote Best Practices and Guidelines. SCPS publication of a document explaining the need for power plant and

transmission system protection coordination, as well as associated training materials and webinars. • Mandatory Standards. Development of requirements for sharing information and protection system coordination

studies for interconnecting elements between functional model entities when certain system conditions change (Standards Project 2007-06 System Protection Coordination).

Recommendations NERC already has requirements (and associated enforcement capability) to address this area of concern, and additional improvements are being developed. However, an increased focus on prevention in addition to accountability, education and coaching techniques will help produce positive results, especially given the complex nature of the subject. To this end, NERC should undertake the following additional activity. • Mandatory Standards. Complete the standards project described above. • Develop Strategies for Coordination of Protection Systems and Other Devices. Develop a best practices document

on coordinating the design and operation of transmission system protection, generator protection and control, special protection systems, and UFLS and UVLS programs; include modeling considerations necessary for assessing coordination through planning and operating assessments of system performance. The issue of coordinating protection systems and controls that respond to different quantities such as voltage, frequency, apparent impedance, and excitation, is not traditional relay-to-relay coordination. Coordination must be addressed in assessments of system performance to compare the response of protection and controls responding to different quantities, and to account for time-based and location-based variations in these quantities.

• Promote Best Practices and Guidelines. Continue to promote best practices and guidelines to aid in protection system design and coordination, such as developed by the SCPS as described above. Collaborate with industry, as well as other entities, to develop additional training programs and educational opportunities for protection engineers to share knowledge and learn about best practices and guideline associated with protection system coordination. Consider working with other bodies (e.g., Energy Providers Coalition for Education) to provide continuing education credits and improve certifications related to protection system education programs.

Measures of Success • Downward trend in the frequency of unnecessary protection system trips caused by lack of coordination.


Risk Profile #5: Extreme Physical Events

Associated Reliability Risk Areas: Coordinated Attack on Multiple Facilities, Geomagnetic Disturbance, Extreme Weather/Acts of Nature, Localized Physical Attack, Electromagnetic Pulse

While the probability of physical events (such as physical attack, geomagnetic disturbance, or severe weather) that lead to extensive damage is low, the potential consequences are high enough that risk avoidance is insufficient as a sole risk management strategy. Risk mitigation is also underway, but additional focus is needed to address this risk

and minimize both the magnitude and duration of the consequences of an extreme physical event.

Detailed Problem Description Coordinated sabotage attacks, severe weather events, and geomagnetic disturbances are physical events that, at the extreme, can cause extensive equipment damage. Because of the long time involved in manufacturing and replacing some BES assets, an extreme physical event that causes extensive damage to equipment would result in degraded reliability for an extended period of time. While these events of this magnitude have a low probability of occurrence, the potential consequences of such an event are high enough that additional focus is needed to properly address this risk and minimize the consequences of an extreme physical event to acceptable levels.

Current Risk Management Activities

• Ongoing problem evaluation. Research and analysis by NERC’s technical committees to address specific issues related to this risk, such as the work being done by the Geomagnetic Disturbance Task Force, Severe Impact Resiliency Task Force and the Critical Infrastructure Protection Committee.

• Simulation and training. The biennial Grid Exercise, which identifies strengths and weaknesses by providing entities the opportunity to respond to simulated malicious attacks against the electricity subsector.

• Raising awareness. Publishing NERC special assessments and reports: High-Impact, Low-Frequency Event Risk to the North American Bulk Power System (2009), Geo-Magnetic Disturbances (GMD):Monitoring, Mitigation, and Next Steps (2011), Effects of Geomagnetic Disturbances on the Bulk Power System (2012)

• Mandatory standards. Requirements related to GMD (Standards Project 2013-03 GMD Mitigation). • Develop coordination programs. Establishment of NERC’s Spare Equipment Database, which facilitates sharing of

equipment in times of need. This is complementary to EEI’s Spare Transformer Equipment Program.

Recommendations While risk avoidance strategies can help prevent manifestation of this risk, a number of events are outside of human control, and avoidance strategies are ineffective. Mitigation efforts to reduce the magnitude of the consequence will address both malicious physical attack and those events which we have little or no ability to prevent.

• Mandatory Standards. Complete the standards projects described above. • Promote and support coordination programs. Emphasize the need for industry to participate in coordination

support programs, such as the Spare Equipment Database and the Spare Transformer Equipment Program. • Encourage resiliency. Promote the sharing of resiliency best practices within NERC, as well as through collaborative

activities with the North American Transmission Forum, the North American Generation Forum, and the North American Energy Standards Board. By leveraging best practices, the magnitude and duration of any significant event would be reduced. Additionally, support entities in pursuing and recovering the costs of implementing resilience strategies, such as the Recovery Transformer Program consortium's efforts to design and test a universal mobile spare transformer that could be deployed to respond to emergency needs quickly.

Measures of Success • Increased participation in the Spare Equipment Database and Spare Transformer Equipment Program. • Strategic deployment of recovery transformers across North America. • Reduced durations of customer outages caused by extreme physical BPS events. • Positive trending in other measures of system resilience and restoration performance.


Risk Profile #6: Availability of Real-Time Tools and Monitoring Associated Reliability Risk Areas: Monitoring and Situational Awareness

Not having the right tools and monitoring available to manage reliability in real time is a latent problem waiting for

the right combination of events. Such events occurred August 14, 2003, and September 8, 2011, resulting in significant blackouts. Reducing the probability of entities not having key capabilities is essential.


Less than adequate situational awareness has the potential for significant negative reliability consequences, and is often a precursor event or contributing cause to events. Experience has shown that not having the right tools and data available can play a critical role in reduced situational awareness, contributing to events such as those seen September 8, 2011 (see Recommendation 12) and August 14, 2003 (see Recommendation 22). NERC has analyzed data and identified that outages of tools and monitoring systems are fairly common occurrences, with approximately an 89% chance of a tool or monitoring system outage occurring within a given month. Each time one of these outages occurs, it creates a potential lack of situational awareness, resulting in a latent risk that could combine with other risks to produce a large event. In addition to outages, simply not having the correct tools or data provided to operators is also a key concern.


related to this risk, such as the work being done by the Real-time Tools Best Practices Task Force. • Raising awareness. Issuing Alerts, publishing Lessons Learned, presenting data and case studies to appropriate

technical committees, and NERC’s Monitoring and Situational Awareness Technical Conference, which provided a forum for vendors and users to share information and exchange knowledge about increasing EMS availability.

Recommendations

Current activities provide information, but do not actively drive toward change. Additional emphasis on education and coaching techniques will help produce positive results, especially given the complex nature of the subject. Because of the number of events in which this risk has been a factor, NERC must take an active role in addressing the problem. To more directly respond to this risk, NERC should focus on the following additional activities: • Raise awareness. Continue emphasis on analyzing and addressing unplanned full and partial EMS outages, including

activities such as issuing Alerts, publishing Lessons Learned, presenting data and case studies to appropriate technical committees, and hosting additional vendor/stakeholder conferences to discuss issues and strategies for minimizing unplanned full and partial EMS outages.

• Develop Best Practices and Guidelines. Collaborate with industry and vendors to develop best practices for system design and maintenance that minimize the probability of downtime, and a guideline to describe approaches for continued reliable operation following the loss of critical tools, such as reliable Real Time Contingency Analysis (RTCA) and Automatic Generation Control (AGC).

• Mandatory Standards. Develop a reliability standard to mandate minimum real-time monitoring and analysis capabilities (Standards Project 2009-02 Real Time Reliability Monitoring and Analysis Capabilities).

Measures of Success • No event where a root, initiating, or contributing cause is identified as a Reliability Coordinator, Transmission

Operator, or Balancing Authority not having the real-time tools and monitoring they need to maintain reliability. • Downward trend in frequency and duration of unplanned full and partial EMS outages.


Risk Profile #7: Protection System Misoperations Associated Reliability Risk Areas: Protection Systems

NERC’s 2012 and 2013 State of Reliability Reports identified protection system misoperations as a significant threat

to BPS reliability. Additional activities are needed to ensure this risk is managed adequately.

Detailed Problem Description Protection System Misoperations represent a double threat. Unnecessary trips can result in making a bad event worse, and even start cascading failures as each successive trip can cause another protection system to trip. However, failures to trip and slow trips can result in damaged equipment, which may result in degraded reliability for an extended period of time. Key Finding 4 from NERC’s 2012 State of Reliability Report concluded protection system misoperations are a significant contributor to disturbance events and automatic transmission outage severity.


related to this risk, such as the work done by the Protection System Misoperations Task Force. • Promote Best Practices and Guidelines. The Protection System Misoperations Task Force development of a set of

suggestions for addressing commonly seen problems and improving protection system performance through the development of guidelines. Ongoing development of training modules to further educate the industry in this area.

• Raise awareness. Publication of misoperations statistics in the State of Reliability Report, highlighting this risk. Quarterly updates and outreach to the Regional Protection Committees.

• Information Requests. Data collection and analysis regarding protection system misoperations, as well as additional activities to improve processes for collecting data and ensuring data quality and collaborating with other organizations for more focused analysis.

• Mandatory Standards. Development of requirements for analysis and corrective action for all protection system misoperations (Standards Project 2010-05.1 Phase 1 of Protection Systems: Misoperations), as well as a standard requiring appropriate disturbance monitoring equipment (Standards Project 2007-11 Disturbance Monitoring).

Recommendations

Increased focus on prevention through education, awareness, and coaching techniques are also expected to produce positive results, especially given the complex nature of the subject. To this end, NERC should undertake the following additional activities. • Mandatory Standards. Complete the standards projects described above. • Promote Best Practices and Guidelines. Develop best practices and guidelines to aid in the proper application of

relay elements, minimizing setting errors, maintaining microprocessor-based relay firmware, and the application of power line carrier communication aided protection. Collaborate with industry, as well as other entities, to develop training programs and educational opportunities for protection engineers. Consider working with other bodies to provide continuing education credits and improve certifications related to protection system education programs.

• Raise Awareness. Develop a better understanding of regional differences in protection system misoperation rates to support actions to reduce variability, where appropriate. Actively engage the industry through different forums (conferences, regional committee meetings, etc.) to promote awareness and foster mitigation measure development.


• Variability of regional and registered entity misoperation performance is reduced. • Overall median misoperation performance rate improves.


Risk Profile #8: Cold Weather Preparedness Associated Reliability Risk Areas: Extreme Weather/Acts of Nature, Generator Availability

Lack of generator preparedness for cold weather extremes may result in forced outages, de-ratings, and failures to

start. Insufficient availability of intra-regional generation and limits on import transfer capability may result in insufficient generation to serve forecasted load, resulting in load shedding.


Lack of generator preparedness for cold weather extremes may result in forced outages, de-ratings, and failures to start. During wide-area extreme weather events, unexpectedly large amounts of generation may be unavailable within a region or sub-region. Failure to communicate changes in operating status of generation during next-day and real time operations time periods may result in inaccurate Balancing Authority generation/load forecasts. Insufficient availability of intra-regional generation and limits on import transfer capability may result in inadequate generation to serve forecasted load, resulting in load shedding.

Current Risk Management Activities • Promote Best Practices and Guidelines. NERC Operating Committee development of a guideline for generator unit

winter weather readiness. Ongoing training offerings to further educate the industry in this area. • Raise awareness. Annual notifications, reminding entities to prepare for cold weather.

Recommendations

The industry experiences in January 2014 were less severe that those from the 1994 and 2011 cold weather events. Despite this, more work remains to be done. NERC should undertake the following additional activities. Dependent on the results of these activities, NERC may need to consider whether its current body of Reliability Standards is sufficient to ensure this risk is appropriately managed. • Promote Best Practices and Guidelines. Collaborate with industry, as well as other entities, to develop a voluntary

review process through which entities can verify their preparedness. Consider working with other bodies to provide continuing education credits and improve certifications related to cold weather preparation.


• Decreasing values in the following areas:

o Frequency of unexpected loss of generation during cold weather events o Percentage of Generation de-rates due to cold weather events o Frequency of generator failures during cold weather events o Frequency and magnitude of load shedding during cold weather events


Risk Profile #9: Right-of-Way Clearances Associated Reliability Risk Areas: Transmission Right of Way, Equipment Maintenance and Management

Transmission Owners and applicable Generation Owners may have established incorrect ratings based on design

documents, rather than on the actual facilities built. Managing to stay within SOL and IROL limits that are based on incorrect ratings may be inadequate to prevent equipment damage and/or cascading, instability, or separation.


Reports from various entities have indicated that in a number of cases, actual conductor-to-ground clearances seen in the field have been inconsistent with those assumed during the design of the facility. Examples of inaccurate historical information that leads to these inconsistencies includes, but is not limited to, misplaced structures or supports, inadequate tower height, and ground profile inaccuracies. While an entity may address this concern by changing the facility ratings, modifying the transmission line configuration, or changing the topography, such cases must be identified before they can be addressed. Failure to address these misalignments could lead to incorrect ratings that are be inadequate to prevent equipment damage and/or cascading, instability, or separation.

Current Risk Management Activities • Raise awareness. Publication of two alert Recommendations on October 7, 2010, and November 30, 2010. • Information Requests. Data collection and analysis regarding field conditions and alignment with design

assumptions, and when misalignment is identified, how that will be corrected.

Recommendations While this risk is in the process of being evaluated and managed, further activity may be needed. • Information Requests. Monitoring and analysis of the data collection described above should continue. Dependent

on the results of these activities, NERC may need to consider whether additional information requests are warranted, as well as whether its current body of Reliability Standards and associated compliance enforcement activities are sufficient to ensure this risk is appropriately managed.


• 95% of entities either have verified facility design, installation, and field conditions are within design tolerances when

the facilities are loaded at their rating or have taken remediation steps such that facility design, installation, and field conditions are within design tolerances when the facilities are loaded at their rating.


Risk Profile #10: 345-kV Breaker Failures Associated Reliability Risk Areas: Equipment Maintenance and Management

NERC has identified a trend of 345 kV SF6 puffer type breakers failing. Circuit breaker failures, in conjunction with another fault, may lead to more BES facilities removed from service than required to clear the original fault. This

poses a risk to the reliability of the BES.

Detailed Problem Description NERC has reviewed nine 345 kV breaker failures affecting both generation and transmission facilities. Six of these failures have occurred within the past year. From these reviews, NERC has identified a trend of 345 kV sulfur hexafluoride (SF6) puffer type breakers failing. A SF6 puffer type breaker compresses a bellows when opening, directing SF6 gas across the parting contacts to extinguish the arc. The SF6 gas is directed across the contacts via a nozzle. The reports indicate a trend with respect to a separation of the nozzle from its point of attachment. In most cases, the nozzle has been found lying on the tank floor. The manufacturer, Hitachi HVB, Inc (formerly HVB AE Power Systems, Inc.) issued a Maintenance Advisory on the affected model of breaker in 2010. The manufacturer has indicated that approximately 1,000 of these breakers were delivered to customers. Based on Transmission Availability Data System data, it is estimated that this type of breaker could comprise 10% to 16% of the 345 kV breakers in service.

Current Risk Management Activities • Raise awareness. NERC published an Industry Advisory alert on August 27, 2013. This alert was accompanied by the

Manufacturer’s Maintenance Advisory. • Information Requests. NERC requested the North American Transmission Forum, the North American Generator

Forum, and other trade associations work with their members to collect and report aggregate information related to this concern (such as the number of these breakers believed to be in operation and whether maintenance has been conducted to address this risk in accordance with the manufacturer’s maintenance advisory).

Recommendations While this risk is in the process of being evaluated and managed, further activity may be needed. • Information Requests. Monitoring and analysis of the data described above should continue. Depending on the

results of these activities, NERC may need to consider whether additional information requests are warranted, as well as whether its current body of Reliability Standards and associated compliance enforcement activities are sufficient to ensure this risk is appropriately managed.


• Reduction in the frequency of 345 KV SF6 puffer type breaker failures.


NERC PC Gap Analysis – Adaptation and Planning For Change Background Over the next 10 years, the electric industry will face a number of significant emerging reliability issues and face a transformational change:

• A significant change to resource mix with reliance on natural gas and renewable/variable generation

• Addressing resource adequacy challenges and regulatory uncertainty

• Integrating new technologies that change fundamental system behaviors

• A need for enhanced and robust modeling

These changes affect the way the power system operates and new operational tools and procedures will be needed to address new challenges. These issues all require careful consideration, preparation, and planning to avoid an adverse impact to system reliability. NERC’s annual long-term reliability assessment provides the basis for understanding these risks, and more importantly how these challenges are interdependent and require interconnection and/or ERO-wide coordination to be effectively addressed.1 The recently BOT-approved NERC Long-Term Reliability Assessment notes the pace of change, and the interaction of these factors with one another, introduces a new level of uncertainty that could affect the assumptions and models that underlie long-range planning and eventually real-time operation. The RISC felt further analysis of this area is needed to identify and prioritize specific initiatives that could include some or all of the elements described above.

Approach

When evaluating risks in the long-term timeframe, three risk areas are evaluated for each of the long-term emerging reliability issues:

1The challenges and risks noted in this gap analysis are and should be aligned with NERC’s long-term reliability assessment.

• Resource and Transmission Adequacy, • Integration of New Technologies and Operations, • Long-Term System Planning and Modeling

The emerging issues from NERC’s annual long-term reliability assessment provide the basis for understanding these risks, and more importantly how these challenges are interdependent and require interconnection and/or ERO-wide coordination to be effectively addressed.

Risk Areas Challenges over the next ten years generally fall into three risk areas:

• Resource and Transmission Adequacy risks represent the projected inability of the bulk power system to serve customer demand during all hours over a specified horizon (10-years is NERC’s Long-Term Reliability Assessment horizon). In general, resource and transmission adequacy risks are threatened continuously by public and society pressure, regulatory uncertainty (e.g., market, environmental), and the uncertainty of a myriad of resources and transmission expected to be in service across that specified horizon.

Resource adequacy is currently a problem in some regions and depending on the outcome and timing of key industry drivers such as fossil/nuclear retirement, gas availability and pipeline capacity, discounting of gas unit availability, wind subsidies, demand response penetration, etc., resource adequacy may become a much more wide spread problem. The industry needs more certainty in these drivers to adequately plan the resources in a timely manner. It is also essential for the markets to examine their rules to ensure that they are set up to adequately consider these emerging uncertainties send accurate signals to the electricity markets for future reliability needs.

• Integration of New Technologies and Operations risks represent potential future operational issues that may be introduced into the BPS as a result of change, such as, new resources with unique operating characteristics or changing needs of the interconnection. The ability to withstand BPS disturbances in real-time operations is threatened

• Long-Term System Planning and Modeling risks represent challenges to the approach and methods (the “how”) used for long-term planning. Incorrect assumptions and methods can lead to incorrect decision-making; therefore, model and analysis inputs need to be accurate and reflect a range of future transmission and resource risks. Effective long-term planning also has the capability to send the right price signals to the market and signal reliability needs such as ancillary services or flexible resources.

NERC RISC Gap Analysis – Adaptation and Planning For Change 2

Gap Assessment Method Risks are ranked and evaluated using this framework:

• Issue Importance/Potential Impact (1 to 4) [Low to High] • Likelihood (1 to 4) [Low to High] • Timeframe (Near, Mid, Long-term) [1 year, 3 year, 5 years and beyond] • Regionality (1 to 8) [1 to 8 based on Regions—must be significant risk in a Region] • Gaps (1 to 4) [Low to High] • Existing Risk Management Initiatives (1 to 4) [Low to High]

Gap Analysis on Primary Risk Areas Resource and Transmission Adequacy The overarching goal of resource planning is to ensure that sufficient resources, delivery capacity, and reliability characteristics exist to meet future demand requirements in a reliable and economic manner. All resource planners maintain some percentage reserve margin of capacity above their demand requirements to maintain reliability following unexpected system conditions and to meet state regulatory and regional requirements. Reserve margins are determined by calculating the capacity of supply resources, discounted to reflect the potential unavailability of the resource at high risk times. Two critical items reflect the manifestation of this risk: 1) uncertainty in regulatory environment (e.g., environmental, fracking, water, waste, variable energy resource, market, etc) and 2) shortcomings in existing infrastructure and fuel supply, as well as market and other rules serving as impediments. Putting economics aside, the first one can be addressed if sufficient lead time is given by the regulators. The second one requires changes to FERC policy, market rules, and the electricity markets reflecting the most accurate price signals for resources and transmission.

Threat: Increasing uncertainty in existing and future generation (availability, feasibility) due to regulatory uncertainty creates planning challenge to meet adequacy targets.


Resource and Transmission Adequacy Gap Assessment

Issue Importance/

Potential Impact (1 to 4)

[Low to High]

Likelihood (1 to 4)

[Low to High]

Timeframe (Near, Mid,

Long) [1 year, 3 year, 5 years and

beyond]

Regionality (1 to 8) [1 to 8

based on Regions—must be

significant risk in a Region]

Gaps (1 to 4) [Low to

High]

Existing Risk Management

Initiatives (1 to 4)

[Low to High]

Environmental Regulation

4 3 Mid-term 5 3 3

Integration of Variable

Generation 3 2 Long-term 5 3 3

Increased Dependence on

Natural Gas 4 3 Near-term 4 2 3

Demand-Side Management

2 2 Mid-term 6 2 2

Demand Forecast Uncertainty

2 1 Long-term 6 1 3

Nuclear Generation

3 1 Long-term 6 1 2

Transmission Siting/Permitting

and Aging Infrastructure

2 1 Long-term 8 2 3


High-Level Gaps: Medium Regional-level planning studies that address and consider the various risks

Post-action review of specific regional resource adequacy concerns

Defining capacity: what requirements should capacity resources have (e.g., fuel)

Common approach for determining capacity contributions of variable generation

Ability to make independent assessment and engineering judgment for unknowns

Closer coordination with responsible authorities (i.e., state PUC) on adequacy challenges

Some challenges are beyond the industry’s control or require significant cross-industry coordination (e.g., regulatory uncertainty, natural gas industry)

Consideration of distributed resources and demand-side resources in resource adequacy analysis and planning processes

Better harmonization of assessment recommendations and actionable risk projects

High-Level Recommendations Develop “lessons learned/best practices” and/or post-action summary for distribution to

the industry

Develop Special Reliability Assessments to address the high risk issues

Advocate and educate industry, public, regulators, and policy makers

Consideration of RAS and IVGTF recommendations in LTRA and special assessments


Sub-Risk Assessment Environmental Regulation Reference Documentation and Special Assessments:

Potential Impacts of Future Environmental Regulations Extracted from the 2011 Long-Term Reliability Assessment

2012 Long-Term Reliability Assessment

Risks and Gaps Early retirement of multiple units in the short-run can stress the bulk power system if plans are not in place to add resources. This can affect both short- and long-term planning strategies and reduce Planning Reserve Margins. With fewer resources, flexibility is reduced and the risk of a capacity shortage may increase, unless additional resources are available. Where Planning Reserve Margins fall below targets or requirements, resources in a specific area may not be sufficient to meet future demands. Of main concern is that the long-lead time to build new infrastructure (e.g., fuel-certain resources, transmission, or pipeline) can impact the ability to maintain resource adequacy.

Timing - Compliance deadlines will challenge the electric industry’s planning horizons, existing planning processes and typical construction schedules. Transmission lines, power plants, and environmental control retrofits are often planned and constructed over a long period of time. Successful implementation of the proposed EPA rules will be highly dependent on the amount of time the industry will be given to comply with future environmental regulations and the tools that are in place support the industry’s transition.

Regionality – The fuel-mix differs greatly across the country. Each area will face different dynamics due to the types of generators as well as the types of regulatory environments within a given area (i.e., deregulated markets, regulated utility service areas, state regulations). State decisions could greatly influence the cumulative impacts.

Outage Coordination – Given the window for compliance, many affected units may need to take long-term maintenance outages concurrently. The need to take multiple units out-of-service on extended scheduled outages can exacerbate resource adequacy concerns and reduce needed flexibility, even during off-peak periods. Outage coordination must be a priority to avoid resource adequacy concerns.

Uncertainty – A major concern among planning entities and regulatory bodies, such as NERC, is the lack of certainty both on the generating supply side and from EPA. Planning Authorities disconnected from the owning/operating functions of generation do not have the visibility needed to accurately model these potentially significant system changes. For example, rules within the ISO/RTO market structures allow generators to request retirement within 90 days of the requested retirement date. Even with facilities which are


http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/EPA%20Section.pdf

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/EPA%20Section.pdf

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/2012_LTRA_FINAL.pdf

not being retired, Planning Authorities and grid operators may not be aware of significant changes in plant operating parameters and new common contingencies. The lack of information and data sharing, and therefore sufficient planning, causes concern.

Uncertainty remains within the regulatory policy making process as to what the final requirements will be on generators. While many of the rules have been proposed, with some already finalized, the industry is forced to continue to make assumptions on what the final rules will require. While the proposed rules give a good representation, final rules can be quite different (e.g., as seen in Texas/ERCOT). Increasing the certainty with respect to the timing and requirements of the regulations would promote timely and sound engineering planning to support the implementation of these rules in an orderly and predictable manner.

Pending carbon, water, and other air and waste regulations and rules also cause concern for resources adequacy.


Integration of Variable Generation Reference Documentation and Special Assessments:

Reliability Impacts of Climate Change Initiatives: Technology Assessment and Scenario Development

Accommodating High Levels of Variable Generation: Summary Report IVGTF 1-3: Methods to Model and Calculate Capacity Contributions of Variable Generation for

Resource Adequacy Planning IVGTF 1-6: Probabilistic Methods to Calculate Capacity Controbutions of Variable Generation Multiple Long-Term and Seasonal Reliability Assessments

Risks and Gaps The addition of significant amounts of variable generation to the bulk system changes the way that transmission and resource planners develop their future systems to maintain reliability. Planners must consider the additional uncertainty in available peak capacity.

In high variable generation penetration scenarios, a larger portion of the total supply resource portfolio will be comprised of energy-limited resources when compared to today’s power system. This fact somewhat complicates, but does not fundamentally change existing resource adequacy planning processes in that the process must still be driven by a reliability-based set of metrics.

Consistent and accurate methods are needed to calculate capacity values attributable to variable generation.


http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/RICCI_2010.pdf


http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/Special%20Report%20-%20Accommodating%20High%20Levels%20of%20Variable%20Generation.pdf

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/IVGTF1-2.pdf

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/IVGTF1-2.pdf

Increased Dependence on Natural Gas Generation Reference Documentation and Special Assessments:

2013 Special Reliability Assessment: Accommodating an Increased Dependence on Natural Gas for Electric Power

2011 Special Assessment Report: A Primer of the Natural Gas and Electric Power Interdependency in the United States

Multiple Long-Term and Winter Reliability Assessments Risks and Gaps

Insufficient resources or transmission to meet demand given generation outages due to lack of natural gas

Long-lead time to build new infrastructure (e.g., fuel-certain resources, transmission, or pipeline) can impact the ability to maintain resource adequacy


http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/NERC_PhaseII_FINAL.pdf


http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/Gas_Electric_Interdependencies_Phase_I.pdf


Demand-Side Management Reference Documentation and Special Assessments:


Demand Response Availability Data System (DADS): Phase I & II Final Report Data Collection for Demand-Side Management Multiple Long-Term and Winter Reliability Assessments

Risks and Gaps Demand response programs provide the industry with the ability to reduce peak demand and to potentially defer the need for some future generation capacity. However, these programs are not an unlimited resource and may provide limited demand reductions during prespecified time periods. Unlike traditional generating resources with many decades of historic data for analysis, the long-term projections of Demand Response involve greater forecasting uncertainty.

Unavailability and non-performance of expected DR during peak periods can contribute to a capacity shortage. If DR programs fail to deliver committed load shedding, additional generation resources and transmission facilities may need to be planned and constructed to deal with an unplanned contingency response. Additionally, expected transmission and generation resources may not be online due to scheduled or unscheduled maintenance, further complicating the issue if DR programs are planned as fixed reductions in load.

Resource and transmission planners rely on demand response commitments in the long-term (3 to 10 years) to meet reserve and resource adequacy requirements and/or targets. Because of the short-term commitments, uncertainty in these resources exist as planners cannot sufficiently assess the viability of those resources.





http://www.nerc.com/pa/RAPA/dads/DADSPhaseII/DADS%20Phase%20I%20and%20II%20Report.pdf

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/Demand-response.pdf

Demand Forecast Uncertainty Reference Documentation and Special Assessments:

2010 Special Reliability Scenario Assessment: Potential Reliability Impacts of Swift Demand Growth After a Long-Term Recession

2011 Long-Term Reliability Assessment: Load Forecast Uncertainty Multiple Long-Term Reliability Assessments

Risks and Gaps Accurate load forecasts are essential to project and assess resource adequacy. The electric industry is currently facing several challenges in forecasting demand for electricity. The accuracy of demand forecasts have been decreasing since the beginning of the last recession, which officially began in late 2007 and ended in mid-2009. There is sufficient empirical evidence to suggest correlations such as the economic outlook, new technologies, and consumer awareness. With structural and cyclical effects of the recession continuing, demand forecasters are faced with the challenge of redefining methods to ensure accurate projections for both short- and long-term planning. As new variables are introduced to load forecasting models, further analysis will be necessary to gain a better understanding of their effects to short-run planning horizons, and ensure methods are consistent in long-term forecasting. With increasing distributed generation, some area may be challenged with manging larger ramps needed to meet net loads. Finally, any changes in climate or long-term weather forecasts may have residual effects on the load forecast and the distribution of potential outcomes.

Insufficient resources or transmission to meet demand unanticipated demand levels Long-lead time to build new infrastructure (e.g., fuel-certain resources, transmission, or

pipeline) can impact the ability to maintain resource adequacy Nuclear Generation Reference Documentation and Special Assessments:

Multiple Long-Term Reliability Assessments, 2013 LTRA in development


http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/NERC_Swift_Scenario_Aug_2010.pdf

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/NERC_Swift_Scenario_Aug_2010.pdf

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/2011%20LTRA_Final.pdf

https://www.surveymonkey.com/MySurvey_EditPage.aspx?sm=dJSDlZvbKISfzAyd4z2%2bB66LAS5mGp7lWj6KIN%2bE92oqYds2OGyqsPncSh4ubz8B&TB_iframe=true&height=450&width=650













Risks and Gaps Concerns about the safety of nuclear generation has reemerged as an issue for both policy-makers and the general public. Several plants in the U.S. have also been under scrutiny, specifically, the San Onofre Nuclear Generating Station (SONGS), which is scheduled for permenant closure in 2013. The reliability impacts in southern California are still being addressed, but the underlying issues still remains as more plants are being retired.

Multiple retirements and/or long-term outages of nuclear facilities could result in tight and in some cases insufficient generation to meet Planning Reserve Margins. Insufficient resources or transmission to meet demand



https://www.surveymonkey.com/MySurvey_EditPage.aspx?sm=dJSDlZvbKISfzAyd4z2%2bB2q0DV%2fbEpnYXmaxU%2fxHiTf4tyuE37RWh0%2bBUDEzv9yv&TB_iframe=true&height=450&width=650






Transmission Siting/Permitting and Aging Infrastructure Reference Documentation and Special Assessments:

Multiple Long-Term Reliability Assessments, 2013 LTRA in development

Risks and Gaps Siting difficulties have always been an issue for the power industry. However, with increasing plans for new transmission, ensuring transmission can sufficiently be built within a specified time period has increased the importance of this issue. Transmission challenges associated accommodating a changing resource mix and siting of new bulk power transmission lines brings with it unique challenges due to the high visibility, their span through multiple states and provinces, and, potentially, the amount of coordination required among multiple regulating agencies and authorities. Lack of consistent and agreed-upon cost allocation approaches (specific to transmission), coupled with public opposition due to land use and property valuation concerns, have at times resulted in long delays in transmission construction. When construction is delayed, special operating procedures may be needed. Province and State Renewable Portfolio Standards (RPS) will increase renewable resources located where wind power densities and solar development are favorable. U.S. federal RPS is also under consideration in Congress. Grid expansion is needed to support the dispersed nature of renewable resources. Finally, additional generation sources, especially large plants such as nuclear facilities, may require grid expansion to assure deliverability. The limited timeframe provided to meet RPS mandates requires that the current siting and approval processes be expedited to ensure meeting mandated energy requirements. The aging transmission system infrastructure has many challenges, such as the availability of spare parts, the obsolesce of older equipment, the ability to maintain equipment due to outage scheduling restrictions, and the aging of the work force and lost knowledge due to personnel retirements. Considering the diversity of equipment technologies and installation dates, potential for increased failure rates should be evaluated. However, implementation of any replacement strategy and in-depth training programs require additional capital investment, engineering and design resources, and construction labor resources, all of which are in relatively short supply.

Should needed transmission not be completed in time, generating capacity may not be fully deliverable and derates in resources and/or transfer capability could occur. Insufficient resources or transmission to meet demand





















Integration of New Technologies and Operations There are several areas where new technologies are picking up momentum. The increased use of Phasor Measurement Units for a variety of industry functions offers opportunities to change how the system is modeled, controlled and monitored. Advancements in energy storage options could increase the penetration of variable resources as they become more efficient and cost effective. Smart meter/ grid may alter traditional load patterns which can affect resource adequacy as well as real-time operations.

From an operations perspective, all system disturbances that result in the unplanned or uncontrolled interruption of customer demand, regardless of cause are considered as operating reliability impacts. When these interruptions are contained within a localized area, they are considered unplanned interruptions or disturbances. When they spread over a wide area of the grid, they are referred to as cascading outages—the uncontrolled successive loss of system elements triggered by an incident at any location. Cascading results in widespread electric service interruption that cannot be restrained from sequentially spreading beyond an exposed area.

The gap that exists is the uncertainty on the timing and penetration level at which new technologies may impact planning and operations. Once reasonable timing estimates are available, the industry can begin the process of quantifying impacts and developing a plan for dealing with the impacts.

Threat: The integration of new technologies on the BPS changes fundamental system behaviors and introduces new operational risks due to unknown/unplanned resource performance and/or availability. Theses threats can decrease the system’s resilience and capability to withstand BPS disturbances.


Integration of New Technologies and Operations Gap Assessment

Issue Importance/


[Low to High]

Likelihood (1 to 4)

[Low to High]

Timeframe (Near, Mid, Long-term) [1 year, 3

year, 5 years and beyond]





High]



[Low to High]

Integration of Variable

Generation 4 4 Long-term 5 4 3

Increased Dependence on

Natural Gas 3 3 Mid-term 5 3 3

Demand Response

2 2 Long-term 5 3 1

Smart Grid 2 2 Long-term 8 2 1


High-Level Gaps: Medium Regional-level planning studies that address and consider the various risks

Incorporating natural gas pipeline and/or supply risks into reliability analysis (e.g., TPL contingency analysis and power flow and stability)

Reliability Standards gap for asynchronous and variable generation

Distribution or demand-side challenges difficult to manage given NERC authority

Demand response operators (i.e., third-party aggregators not covered under NERC registration)


Some challenges are beyond the industry’s control or require significant cross-industry coordination (e.g., regulatory uncertainty, natural gas industry, technological progress)


Unknown impact of smart devices and systems (e.g., automatic control, system optimization)

High-Level Recommendations Enhance operator training and operational planning to ensure flexibility is planned and

available

Develop Special Reliability Assessments to address the high risk issues

Advocate and educate industry, public, regulators, and policy makers

Consideration of RAS and IVGTF recommendations in LTRA and special assessments


Sub-Risk Assessment Integration of Variable Generation Reference Documentation and Special Assessments:


DRAFT Joint NERC-CAISO Special Reliability Assessment: Maintaining Bulk Power System Reliability While Integrating Variable Energy Resources to Meet Renewable Portfolio Standards

Accommodating High Levels of Variable Generation: Summary Report Interconnection Requirements for Variable Generation Operating Practices, Procedures, and Tools Potential Bulk System Reliability Impacts of Distributed Resources Flexibility Requirements and Potential Metrics for Variable Generation Variable Generation Power Forecasting for Operations Multiple Long-Term and Seasonal Reliability Assessments

Risks and Gaps The expected significant increase in variable generation additions to the BPS will increase the amount of uncertainty that a system operator must factor into operating decisions. The system operator must have access to advanced variable generation forecasting techniques and sufficient flexible resources to mitigate the added variability and uncertainty. Operating criteria, forecasting, commitment, scheduling, dispatch and balancing practices, procedures, and tools must be enhanced to assist operators in maintaining BPS reliability. Improved operating practices, procedures and tools are critical in order to integrate variable generation into the power system, as well as improve the control performance and reliability characteristics of the power system. System resources supporting reliability, such as flexible generation and responsive load, are finite.




http://www.nerc.com/comm/PC/Integration%20of%20Variable%20Generation%20Task%20Force%20I1/NERC-CAISO_VG_Assessment_DRAFT_clean.pdf




http://www.nerc.com/files/2012_IVGTF_Task_1-3.pdf

http://www.nerc.com/files/IVGTF2-4.pdf

http://www.nerc.com/files/IVGTF_TF-1-8_Reliability-Impact-Distributed-Resources_Final-Draft_2011%20(2).pdf

http://www.nerc.com/files/IVGTF_Task_1_4_Final.pdf

http://www.nerc.com/files/Varialbe%20Generationn%20Power%20Forecasting%20for%20Operations.pdf

Increased Dependence on Natural Gas Generation Reference Documentation and Special Assessments:

2013 Special Reliability Assessment: Accommodating an Increased Dependence on Natural Gas for Electric Power

2011 Special Assessment Report: A Primer of the Natural Gas and Electric Power Interdependency in the United States

Multiple Long-Term and Winter Reliability Assessments

Risks and Gaps Along with other drivers, potential environmental regulations can change the overall fuel-mix, ultimately changing the inherent operating characteristics of a given resource portfolio. For example, with less coal-fired capacity, more gas-fired generation may need to provide base-load services. As a result, the interdependency of gas and electric supply, transport, and delivery must be further assessed to ensure reliability is not degraded.

Loss of multiple gas-fired generation needed to meet customer demand Load management or forced outages result in the loss of key gas transportation components

(e.g., electric compressor stations, electric controls at non-electric compressor stations), leading to further fuel interruptions and generation loss.

High demand for natural gas for electric power cannot be served by fully committed pipeline capacity; using non-firm transportation to supply gas-fired generation results interruption of fuel and subsequent unavailability of the generation.

A key component of the gas transportation system fails, resulting in the concurrent loss of multiple generation sources.

Emergency operations not sufficient to maintain reliability Generation that has its fuel source interrupted or curtailed may not have the ability to dual-fire

a back-up source of fuel. Oil back-up or natural gas storage may not be available to burn. The generator may also have technical issues in the switchover process.

Inter and intra-industry communication and coordination can impact the electric system operator’s ability to make risk-informed decisions on generator availability. The timely and comprehensive sharing of information and data will be of increasing importance.

In preparation for summer and winter extreme conditions, electric system operators need enhanced observability of pipeline conditions, capacity availability, supply concerns, and potential issues affecting fuel for gas-fired generation.






Demand Response Reference Documentation and Special Assessments:


Demand Response Availability Data System (DADS): Phase I & II Final Report Data Collection for Demand-Side Management Multiple Long-Term and Summer Reliability Assessments

Risks and Gaps Demand response programs generally offer operators more flexibility. However, unresponsive demand can lead to real-time challenges.

Third-party non-NERC Registered Entities are controlling hundreds of MW of capacity; potential inconcistency and gap compared to other resource operators of similar size.

Accidental activation of a demand response program




http://www.nerc.com/pa/RAPA/dads/DADSPhaseII/DADS%20Phase%20I%20and%20II%20Report.pdf

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/Demand-response.pdf

Smart Grid Reference Documentation and Special Assessments:

Reliability Considerations from the Integration of Smart Grid Multiple Long-Term and Summer Reliability Assessments

Risks and Gaps Governments, regulators, and industry organizations have proposed the “smart grid” to enhance consumer options, support climate change initiatives, and enhance the reliability of the North American bulk power system. The evolving integration of smart grid will require significant changes in bulk power system planning, design, and operations. This report defines smart grid, incorporating reliability of the bulk power system, and provides a preliminary assessment of successful smart grid integration.

Government initiatives and regulations promoting smart grid development and integration must consider bulk power system reliability

Integration of smart grid requires development of new tools and analysis techniques to support planning and operations

Smart grid technologies will change the character of the distribution system, and they must be incorporated into bulk power system planning and operations

Cyber security and control systems require enhancement to ensure reliability Research and development (R&D) has a vital role in successful smart grid integration


http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/SGTF_Report_Final_posted_v1.1.pdf

Long-Term System Planning and Modeling

This item serves as a roll-up of most of the planning issues identified, but targets the gaps in the planning and modeling approaches. Typical assumptions for long-term planning are 1) resource availability (nuclear, fossil, gas, intermittent, etc), 2) load forecast (demand response, economic uncertainty, climate impacts etc.), and 3) topology/equipment modeling (modeling, smart grid, new technologies, etc.). How these individual risks are addressed will ultimately impact the industry’s ability to perform adequate long-term planning. The modeling of individual equipment, topology, stations, etc. was identified as a standalone risk by RISC and is address separately.

Threat: Long-term planning and system analysis need to identify and reflect representative risks to the BPS. Given a large change in a given resource mix, system behavior (i.e., generation characteristics, frequency response, inertia requirements) will likely change. Robust and risk-oriented planning and modeling approaches will be needed to address transmission and operating reliability. Incorrect assumptions and methods can lead to incorrect decision-making for system reinforcement, resources, transmission, flexibility, and operational needs.

Long-Term System Planning and Modeling Gap Assessment

Issue Importance/


[Low to High]

Likelihood (1 to 4)

[Low to High]

Timeframe (Near, Mid, Long-term) [1 year, 3

year, 5 years and beyond]





High]



[Low to High]

Change in Resource Mix and

Operational Characteristics

4 4 Long-Term 8 3 3

Change in System Behavior and

Composition of System Load

4 3 Long-Term 8 3 2

Region/ Interconnection-

wide Planning and Modeling

3 3 Long-Term 8 3 2


High-Level Gaps: Medium-High

Industry trends show a significant reduction of large rotating thermal machines over the next 20 years. Issues regarding stability, frequency control and several other fundamental reliability services will be challenged as intertia and voltage support is reduced across the system Regional-level planning studies that address and consider the various risks

Identification and determination of frequency responsive resources

Incorporating natural gas pipeline and/or supply risks into reliability analysis (e.g., TPL contingency analysis and power flow and stability)

Reliability Standards gap for asynchronous and variable generation; standard models needed

Distribution or demand-side challenges difficult to manage (data and information on distributed/behind-the-meter generation often unknown to planners/operators)



Unknown impact of smart devices and systems (e.g., automatic control, system optimization), electronically coupled loads, and load composition

Unknown FIDVR concerns may be embedded risk that has not been fully analyzed

High-Level Recommendations Long-term studies need to determine the amount of flexibility needed to maintain reliability

Guidelines on frequency responsive resources being developed as part of the Frequency Response Initiative to address comprehensive integration of those various resources

Consideration of RAS, SAMS and IVGTF recommendations in LTRA and special assessments


Sub-Risk Assessment Change in Resource Mix and Operational Characteristics Reference Documentation and Special Assessments:


Accommodating High Levels of Variable Generation: Summary Report DRAFT Joint NERC-CAISO Special Reliability Assessment: Maintaining Bulk Power System

Reliability While Integrating Variable Energy Resources to Meet Renewable Portfolio Standards Frequency Response Initiative Report Multiple Long-Term and Seasonal Reliability Assessments

Risks and Gaps

At low variable generation penetration levels, traditional approaches towards sequential expansion of the transmission network and managing wind variability in Balancing areas may be satisfactory. However, at higher penetration levels, a regional and multi-objective perspective for transmission planning identifying concentrated variable generation zones.

Increased consideration for voltage stability, frequency response, diminishing reactive power and inertia impacts in system planning studies. Additional transmission will be needed to interconnect variable energy resources planned in remote regions, smooth the variable generation output across a broad geographical region and resource portfolio, and deliver ramping capability and ancillary services from inside and outside a Balancing Area to equalize supply and demand.

Sufficient modeling capabilities, including a “Standard Models,” defined as non-proprietary and publicly available models for the simulation of steady-state (power flow), short-circuit (fault calculations) and dynamic (time-domain simulations) behavior of such variable generation, must be made readily available for use by power system planners.

As replacement generation is constructed, new transmission may be needed to interconnect new generation. Enhancements may be needed to the transmission system in order to support firm and reliable transmission service to support new generation. Transmission system enhancements and reconfiguration may be necessary in some areas, which create additional timing issues as new transmission facilities take relatively longer to construct than generation. Second tier effects include impacts to system stability, short-circuit, and deliverability.

The retirement of larger and/or strategically situated generating units will cause changes to the power flows and stability dynamics of the bulk power system. These changing characteristics will require enhancements to the interconnected transmission systems to provide reactive and








http://www.nerc.com/docs/pc/FRI_Report_10-30-12_Master_w-appendices.pdf

voltage support, address thermal constraints, and provide for system stability. Based on information gathered from stakeholders and the Regional Entities, these issues may cause some reliability concerns unless the transmission system is reconfigured. In some cases, these reliability issues can result in violations of NERC Reliability Standards and, therefore, pose a threat to reliability if they are not addressed.

Probabilistic planning techniques and approaches are needed to ensure that system designs maintain bulk power system reliability.


Change in System Behavior and Composition of System Load Reference Documentation and Special Assessments:

A Technical Reference Paper Fault Induced Delayed Voltage Recovery Frequency Response Initiative Report Multiple Long-Term and Seasonal Reliability Assessments

Risks and Gaps

Higher penetrations high-efficiency residential air conditioners, compact fluorescent and LED lighting, plasma, LCD and LED televisions, and other electronically coupled loads are significantly changing the characteristics and behavior of the system load, particularly during system disturbances.Initial testing indicates that such changes may exacerbate emerging problems such as fault-induced delayed voltage recovery (FIDVR).

An immediate gap is the inability of current load modeling methods to predict behavior of the system with the integration of new electronically-coupled loads.

The changing nature of the load requires immediate improvements and additional sophistication in load modeling in order properly analyze potential system performance issues.


http://www.nerc.com/docs/pc/tis/FIDVR_Tech_Ref%20V1-2_PC_Approved.pdf

http://www.nerc.com/docs/pc/FRI_Report_10-30-12_Master_w-appendices.pdf

Region/Interconnection-wide Planning and Modeling Reference Documentation and Special Assessments:

Modeling Initiative

Risks and Gaps Studying interconnection-wide phenomena is becoming more important, to address frequency response, inertial response, small-signal stability, extreme contingency impacts, and geomagnetic disturbances. To ensure proper system performance, validated models are required that reasonably represent actual equipment performance in simulations to confidently support planned system enhancement. All devices and equipment attached to the electric grid must be modeled to accurately capture how that equipment performs under static and system disturbance conditions. Models provided for equipment must be open-source and shareable across the interconnection to support reliability of the interconnection. Such models cannot be considered proprietary.

Highlighted in Operational Models and Modeling Inputs Gap Assessment System modeling issues have been identified in several significant system events during the

past two decades (the latest being the Southwest Blackout) Issues cover full gamut of the system (i.e., transmission, generation, loads, protection) and,

more importantly, the interaction between all components NERC has advanced the development of appropriate modeling standards The industry as a whole has begun addressing various pieces and parts of the modeling issues Probabilistic planning techniques and approaches are needed to ensure that system designs

maintain bulk power system reliability. Various segments of the industry are currently addressing various modeling issues. The most

significant current risk is the lack of centralized coordination and oversight of the many on-going efforts. Without a centralized entity overseeing all the individual efforts, there is a risk of duplicity in effort, lack of reaching industry consensus and wasted time/effort/resources in an already resource-constrained industry environment.


Gap Assessment Conclusions High-Level assessment of all risk area’s under Adaptation and Planning for change indicate the:

• Need for operator flexibility to address Generation and transmission uncertainty Changing system response with the addition of new technologies Fuel uncertainty Serving unexpected loads

• Need for sufficient planning reserves to address Timing of retirements, scheduled outages, regulatory requirements Regulatory uncertainty Fuel uncertainty

• Need for accurate assessments and planning studies to address Risks and uncertainties of emerging reliability issues Actual operating characteristics and system dynamics

• Regionality of reliability issues Current NERC actions include:

• Technical Committee Role Long-Term and Seasonal Assessments Special Reliability Assessments on emerging issues Technical support and guidance documents for use in standards development and

compliance operations (e.g., IVGTF, SAMS) System Analysis and Reliability Initiatives (e.g., IFRO, modeling)

• NERC Staff Role

Collaboration with interconnection-wide/regional planning and study groups on emerging issues (e.g., EIPC on natural gas vulnerabilities)

Coordination with state regulators on resource adequacy challenges Education and key messaging to industry, regulators, and public Coordination with NERC governmental relations and communication


NERC PC Gap Analysis – Operational Modeling Issue Modeling and Model Inputs

Background System modeling issues have been identified in several significant system events during the past two decades (the latest being the Southwest Blackout). Issues cover full gamut of the system (i.e., transmission, generation, loads, protection) and, more importantly, the interaction between all components. NERC has advanced the development of appropriate modeling standards and the industry as a whole has begun addressing various pieces and parts of the modeling issues. While various segments of the industry are currently addressing various modeling issues, the most significant current risk is the lack of centralized coordination and oversight of the many on-going efforts. Without a centralized entity overseeing all the individual efforts, there is a risk of duplicity in effort, lack of reaching industry consensus and wasted time, effort, and resources in an already resource-constrained industry environment.

Gap Assessment Method Risks are ranked and evaluated using this framework:

• Issue Importance/Potential Impact (1 to 4) [Low to High] • Likelihood (1 to 4) [Low to High] • Timeframe (Near, Mid, Long-term) [1 year, 3 year, 5 years and beyond] • Gaps (1 to 4) [Low to High] • Existing Risk Management Initiatives (1 to 4) [Low to High]

Gap Analysis on Primary Risk Area Improvement of Existing Operational Modeling The improvement of interconnection-wide simulation models in the future will be necessary to support and reliability integrate a changing resource mix projected over the long-term, address interconnection-wide phenomena such as, event forensics, transient stability, frequency response and geomagnetically induced currents. When a credible disturbance event is simulated in computer models of the power system and the result is unacceptable performance, system planners and/or operators must develop operating strategies, adjustments to existing system components or planned equipment additions (e.g., line re-conductoring, addition of shunt reactive compensation devices, etc.) to address the potential problem. Validation of planning models is needed on a regular basis to ensure the model is working correctly and delivering accurate results. There will always be evolving changes in the characteristics of the power system, particularly with respect to load characteristics. Unforeseen interactions can also occur when new control strategies are used through the addition of novel devices and technologies. The models must therefore be validated periodically to ensure that trends in the power system which can affect reliability are captured in system studies. Threat: Inaccurate or non-representative data that is used for operational and/or system modeling may lead to incorrect actions and decisions

Improvement of Existing Operational Modeling Gap Assessment Issue

Importance/ Potential

Impact (1 to 4)

[Low to High]

Likelihood (1 to 4)

[Low to High]

Timeframe (Near, Mid,

Long) [1 year, 3 year, 5 years and

beyond]


High]



[Low to High]

Generator modeling

3 3 Near-term 1 3

Case error data reduction

4 2 Near-term 2 2

NERC RISC Gap Analysis – Operational Models and Model Inputs 2

Consistency of Generator Model

Parameter definitions

2 2 Mid-term 1 2

Frequency response modeling

4 4 Near-term 4 3

Model validation 4 4 Mid-term 3 3

Standardized component

models 3 2 Mid-term 3 2

Proprietary models and data

3 3 Mid-term 3 2

Change in nature and composition of system load

2 2 Long-term 2 2

Bus-branch vs. Node-breaker

2 2 Mid-term 2 2

Inter-area oscillations

2 2 Long-term 2 2

Protection system modeling

2 3 Long-term 2 2

Turbine control modeling research

2 2 Long-term 2 2


High-Level Gaps: Medium

Lack of centralized coordination and oversight of the many on-going efforts.

Risk of duplicity in effort

Lack of reaching industry consensus

Potential for wasted time/effort/resources

Issues with the timeframe of Mid or Long-term will take multiple years to address and the industry may not have the time or sustained resources to commit to the problem resolution which could cause the issue to languish.

Equipment owners and manufacturer’s (generators for dynamic studies including sub-synchronous resonance studies and transformers for geomagnetic disturbances) willingness to provide the necessary data or technical support

Timely cooperation amongst all vested parties

High-Level Recommendations Support of NERC’s Modeling Initiative

Identification of lead roles

The Model Working Group, reporting to the Planning Committee is developing recommendations for enhanced model validation.

NERC is leading the effort through Regional Entity staff to engage the model development groups of each Interconnection (ERAG,1 ERCOT, and WECC), and build plans for their improvement, based on priority interconnection-wide study requirements.

NERC staff’s goal is to not to direct the development of each interconnection’s model, but to provide guidance and feedback to the modelers, as all stakeholders have a vested interest in developing the most accurate interconnection model possible.

Reference Documentation

Modeling Initiative FIDVR Report FRI Report

1 ERAG is the Eastern Interconnection Reliability Assessment Group and contains representatives from the following NERC Regions: FRCC, MRO, NPCC, RFC, SERC, and SPP.


Sub-Risk Assessment

Generator Modeling (H, Near, Small)

North American Transmission Forum (NATF) and North American Generator Forum (NAGF) are leading this effort. Initial activities center on developing a standardized generator data request format and data content, developing a definition of generating unit reactive capability and, defining necessary practices to develop and maintain up to date models as plant modifications are made.

SAMS/MWG is finalizing a plan for developing and maintaining a library of standardized component models that can be substituted for proprietary models so that accurate interconnection wide models can be built. (see standardized models below)WECC and ERCOT have processes and procedures in place which have proved successful, and

Case Error Data Reduction (H, Near, Med)

ERAG/MWG is leading this effort for the Eastern Interconnection. WECC and ERCOT have processes and procedures in place which have proved successful.

Consistency of Generator Model Parameter Definitions (M, Mid, Small)

Differences in understanding of models needing better and more consistent definition: o Steady state modeling, e.g., min/max MW and Mvar capabilities, station service loading,

voltage schedules, etc., o Frequency response of generators to system disturbances, o Modeling of internal plant limitations such as protective equipment or auxiliary system

requirements that would affect reactive output.

The NERC MWG, NATF and NAGF all have either on-going or start-up efforts addressing these concerns.


Frequency Response Modeling (H, Near, Large)

With the implementation of the new BAL-003 standard, the Interconnection Frequency Response Obligations and the allocation to the BAs will be established. This will provide significant impetus to maintain and improve both frequency response and its modeling.

The NERC Resources Subcommittee has also launched an outreach effort to generators to improve performance their primary frequency response through management of their droop characteristic settings and frequency response deadbands.

Various industry modeling entities are working in the area of validating the dynamics models generators against actual frequency response performance during actual system events. (See model validation above).

Model Validation (H, Mid, Med)

MWG is currently working in this area and has an industry wide field test of suggested model validation techniques ongoing.

WECC, ERCOT, and other companies already have process and procedures in place which have proved successful. WECC already regularly performs model validation to actual system events.

Several non utility organizations such as University of North Carolina at Charlotte (UNCC) are doing additional research in comparing models to DFR/DSR/PMU measurements of actual real-time events.

The NATF is collaborating with the NAGF to establish an industry-wide guideline for 1) ensuring that generating unit reactive limits are modeled appropriately in the various types of studies performed and 2) validating that the model appropriately reflects a generating unit’s response to actual system disturbances.

Standardized Component Models (M, Mid, Large)

The Planning Committee (PC) has directed the NERC Modeling Working Group (MWG) to develop, validate, and maintain a library of standardized component models and parameters for powerflow and dynamics cases for use in developing interconnection wide simulations.

An initial library of standardized models will be created by MWG using the current regionally approved dynamic model libraries. WECC already has a library of standardized component models already in place. Additional models from the Institute of Electrical and Electronics Engineers (IEEE) and other appropriate organizations will be added as appropriate. Models for new technological innovations will be developed, validated, and added to the library of standardized models.


This effort will require a collaborative effort with manufacturers, software vendors, NATF, NAGF, and stakeholders. The effort will take several years to complete and will require significant SME resources.

Proprietary Models and Data (H, Mid, Large)

This effort is imbedded in the standardized model effort (see above). NERC’s MWG whitepaper on Standardized Component models approved by the PC in

September 2013), proposes a methodology for transitioning from proprietary models to standardized models. MWG, manufacturers, software vendors, and stakeholders through the standardized model proposal would, over time, transition user-defined models into industry wide standardized component model thus eliminating the problems.

Change in Nature and Composition of System Load (M, Long-term, Med)

Higher penetrations high-efficiency residential air conditioners, compact fluorescent and LED lighting, plasma, LCD and LED televisions, and other electronically coupled loads are significantly changing the characteristics and behavior of the system load, particularly during system disturbances. Initial testing indicates that such changes may exacerbate emerging problems such as fault-induced delayed voltage recovery (FIDVR).

The changing nature of the load requires immediate improvements and additional sophistication in load modeling in order properly analyze potential system performance issues

WECC has made significant progress in this area including the development of an available industry wide composite load model for dynamics. Various sub regions within the eastern interconnection have begun analysis of other load composition models. ERCOT is also begun load composition modeling analysis.

Bus-branch vs Node-breaker (M, mid, Med)

The MWG presented a whitepaper recommending the industry to adopt the node/breaker model for planning and operational studies to the Planning Committee. The NERC PC has approved the concept, and has asked SAMS to develop an implementation plan. This will require a rather lengthy transition period. SAMS and MWG will, work with vendors, to develop an implementation plan and timeline.

Inter-Area Oscillations (M, Long-term, Med)

Current interconnection wide models have proven inadequate to accurately reproduce inter-area oscillations especially those documented with the new PMU devices.


MWG has several on-going efforts including a recommendation for the industry to improve modeling.

WECC is developing an improved West-wide System Model that is expected to provide much better correlation between simulations and real-time response. ERCOT will conduct a new small signal stability study in the last quarter of 2013 focusing on detection of oscillatory modes and corresponding solutions. Other sub regions in Eastern Interconnect have conducted similar studies.

The risk from such oscillation appears to be minimal at this time. Protection System Modeling (M, Long-term, Med)

Modeling groups in all interconnections are examining ways to model the behavior of key protection systems in powerflow and dynamics analysis.

SPCS and SAMS have developed a white paper defining requirements for studying Special Protection Scheme (SPS) or Remedial Action Schemes (RAS) and their potential impacts and interactions on reliability. The SPCS with assistance from SAMS continues to address emerging protection issues. This has resulted in the continuous modification of existing PRC standards. It is expected this will be an ongoing effort.

Turbine Control Modeling Research (M, Long-term, Med)

NERC is preparing to lead research on which aspects of turbine and boiler controls systems need to be modeled to accurately represent physical machine responses to system electrical events.

Several non utility organization like UNCC and EPRI are also performing research on turbine response.

NERC in conjunction with SAMS/MWG and the NAGF will begin this effort in 2014.


Gap Assessment Conclusions At present there is significant evidence that nearly all issues are being reviewed and addressed by various industry groups. Also as a result of SAMS/MWG efforts, MOD 10-15 standards are being consolidated and rewritten to address several of the modeling issues.

1. There is a lack of centralized coordination and oversight of the many on-going efforts. Without a centralized entity overseeing all the individual efforts, there is a risk of duplicity in effort, lack of reaching industry consensus and wasted time/effort/resources in an already taxed industry environment.

2. Issues with the timeframe of Mid or Long-term will take multiple years to address and the industry may not have the time or sustained resources to commit to the problem resolution which could cause the issue to languish.

3. Accurate assessments and operational planning studies to address

o Risks and uncertainties of emerging reliability issues o Actual operating characteristics and system dynamics

4. Regionality of reliability issues needs consideration

o Regions and neighbors need to share more data more frequently and quickly.

Equipment owners and manufacturer’s (Generators for dynamic studies including Sub Synchronous Resonance studies and transformers for Geomagnetic Disturbances) willingness to provide the necessary data or technical support could cause the issue to languish. Timely cooperation amongst all vested parties is essential to minimize these risks.

Studying interconnection-wide phenomena is becoming more important, to address frequency response, inertial response, small-signal stability, extreme contingency impacts, and geomagnetic disturbances. To ensure proper system performance, validated models are required that reasonably represent actual equipment performance in simulations to confidently support planned system enhancement. All devices and equipment attached to the electric grid must be modeled to accurately capture how that equipment performs under static and system disturbance conditions.


Current NERC actions include:

• Technical Committee Role SAMS/MWG Modeling Initiative Technical support and guidance documents for use in standards development and

compliance operations (e.g., IVGTF, SAMS, MWG)

• NERC Staff Role Collaboration with interconnection-wide/regional planning and study groups on

emerging issues (e.g., EIPC, ERAG, MMWG, DOE Labs) Coordination with standards drafting teams Education and key messaging to industry, regulators, and public Coordination with NERC governmental relations and communications


NERC | Standards Committee Strategic Work Plan | Approved by Standards Committee October 17, 2013 8 of 9

Attachment A: “Independent Experts’ Appendix F – BPS Risks

not adequately Mitigated (Gaps)”

Priority GAP IERP Recommendation/RISC Triage3 High Outage Coordination Included in proposed Authority Standard. See

Appendix I - draft Authority Standard. Recommended RISC triage: Recommend that the Operating Committee (OC) review the need for a new Standard or revised Standards on outage coordination (including the Expert’s proposed authority Standard) versus the implementation of a tool other than, or in addition to, a Standard. The OC is to present its findings on this issue to RISC for discussion and prioritization, which is to be completed by the end of the second quarter 2014.

High Governor Frequency Response Develop a standard/requirement for governor frequency response for GO/GOPs for inclusion in appropriate BAL standard(s). Recommended RISC triage: Same as Outage Coordination, above.

High EMS RTCA models Develop a standard that defines the requirements for EMS RTCA models or the performance expectations of the models (Project 2009-02 - Real-Time Monitoring and Analysis Capabilities). Recommended RISC triage: Have the Standards Committee chair coordinate a discussion between one of the Independent Experts with the Project 2009-02 - Real-Time Monitoring and Analysis Capabilities SAR drafting team, the associated Standards Developer and SC’s Project Management and Oversight Subcommittee liaison to determine whether the scope of the current SAR is sufficient to address Expert’s concern or whether SAR needs to be expanded. If SAR needs to be expanded, it is recommended that the SAR drafting team post a revised SAR for comment prior to end of 2013.

High Lack of requirement for use of three-part communications

Resolve COM-002 and COM-003 by requiring three-part communication for operational directives and for registered entity defined operational instructions that involve taking specific actions or steps that would cause a

3 These gaps were identified by the Independent Experts in Appendix F of their report. RISC’s recommended triage of these gaps is stated

after each identified gap. The Chair of SC will coordinate the execution of the recommended triage.

NERC | Standards Committee Strategic Work Plan | Approved by Standards Committee October 17, 2013 9 of 9

change in status or output of the BPS or a generator. This does not include three-part communication for myriad of conversations where information is being exchanged or options are being discussed. Recommended RISC triage: Address via RISC and OC responses to the August 15, 2013 Board of Trustee questions on COM-002-3 and COM-003-1, as well as Standards Project 2007-02 (COM-003-1).

Medium Verification of accuracy of planning models

Develop a guideline for verifying the accuracy of the various planning models developed under the existing MOD 010-MOD 015 standards. Recommended RISC triage: No additional action recommended, given the Expert’s concerns are with the scope of Standards Project 2010-3 (Consolidation of MOD 10 through 15) and the Experts are satisfied with direction of project.

Medium Short circuit/fault duty models Develop a standard/requirements for short circuit/fault duty models that would fit with the existing MOD 010-MOD 015 standards. Recommended RISC triage: Same as verification of accuracy of planning models.

Medium Infrastructure maintenance Develop a dashboard indicator to assure adequacy of current equipment maintenance programs

Substation/switchyard equipment

Transmission line maintenance Recommended RISC triage: Experts are not recommending a Standard. Thus, recommend that Planning Committee (PC) develop tools (other than a Standard) that address Expert’s concern. It is recommended that PC develop these tools and present them to RISC for informational purposes by the end of the second quarter of 2014.

DRAFT Essential Reliability Services Task Force Scope Background Essential Reliability Services (ERS) are the elemental ‘reliability building blocks’ from resources (generation and demand) necessary to maintain Bulk Power System (BPS) reliability. ERS are operational attributes from conventional generation, such as providing reactive power to maintain system voltages and physical inertia to maintain system frequency, necessary to reliably operate the BPS. In contrast, retirement of conventional generation in near future across many areas in North America, coupled with increasing variable generation installation can adversely impact the availability of ERS unless due considerations are given in planning and operations. The amount of variable renewable generation such as distributed, and utility scale solar, and wind generation is expected to grow considerably as governmental energy policies and regulations are developed and implemented by states and provinces throughout North America. The proposed levels of commitment to renewable variable generation is one component of an ongoing shift in resource mix. It is imperative that power system planners and operators understand the potential and cumulative reliability impacts associated with large scale integration of variable generation, an overall capacity reduction in larger base-load generation, increased participation from demand resources and distributed generation, and a more evident increase in reliance on natural gas-fired generation. Variable generation, in particular, has different operating characteristics and responds differently to changes in frequency and voltage on the system. As larger amounts of variable generation are added to the system, they have strong potential to displace the traditional large, rotating machines and the operating characteristics those machines and the ancillary benefits to system reliability that these units provided. Beyond capacity and energy characteristics, essential reliability services (ERS), such as inertia, frequency response, and voltage control, must be maintained across a given system to ensure reliable operation. These along with other characteristics or functions make up a suite of Essential Reliability Services or ERS. To meet the needs of the future Bulk Power System, maintaining sufficient ERS will include a mix of market approaches, technology enhancements, and reliability rules or other regulatory rule changes. While the solution sets will likely be different in various regions, it may be necessary for regulators to make appropriate adjustments to market rules and reliability standards that will ensure reliable operation of the BPS. Purpose The ERSTF has a multi-faceted purpose that includes developing a technical foundation of ERS; educating and informing industry, regulators, and the public about ERS; developing an approach for tracking and trending ERS; formulating recommendations to ensure the complete suite of ERS are provided and

available; and providing roadmap necessary for operating and maintaining a reliable grid. More specifically, the ERSTF will reconcile a collection of analytical approaches for understanding potential reliability impacts as a result of increasing variable resources and how those impacts can affect system configuration, composition, operation and the need for increased ERS. Activities

1. Develop a technical reference document (primer) on ERS. The primer can be used as a reference manual for regulators and policy makers to inform, educate, and build awareness on the reliability ramifications of the elements essential for the reliability of the BPS.

2. Develop an approach and framework for the long-term assessment of essential reliability services to supplement existing resource adequacy assessments. The approach should include a series of metrics that can be continually measured for further evaluation.

a. Assess impacts on ERS due to increase in variable generation along with retirements of base generation. Articulate how each region is impacted by this scenario.

3. Develop specific recommendations for practices and proposed requirements, including potential reliability standards, that cover the transmission and generation planning, operations planning, and real-time operating procedures.

4. Provide a roadmap in form of a guidance document incorporating ERS for operations personnel along with appropriate tools to use and implement in order to maintain BPS reliability. Get industry and stakeholder involvement in identifying, planning and implementing operational needs.

Based on the work plan generated in this first phase of activity, the OC and PC will determine follow-on activities to support technical committee recommendations, implementation of enhanced reliability assessment approaches, and/or technical guidance to standard drafting teams. Membership NERC requests industry’s subject experts to continue their efforts and add additional members as needed, with final selection agreed to by the officers of the Planning Committee and Operating Committee. Members must be willing to commit their time to participate in the task force discussions and contribute to writing the final report. The task force is comprised of the following:

• Co-chaired (OC/PC)

• One representative from each Regional Entity

• At least one representatives from the NERC Planning Committee

• At least one representatives from the NERC Operating Committee

Essential Reliability Services Task Force Scope 2

• One member-at-large representing Canada

• Additional members can be added:

o At the request of the Planning Committee sector representatives, or

o As needed by the NERC coordinator

• Chair of Reliability Assessment Subcommittee (or designated liaison)

• Chair of System Analysis and Modeling Subcommittee (or designated liaison)

• NERC staff coordinator(s)

• Governmental members include, but not limited to:

o Federal Energy Regulatory Commission

o United States Department of Energy

o National Energy Board, Canada

Participation of additional industry subject matter experts may be requested to support task force activities. The task force co-chairs will be appointed by the chairs of the NERC Planning Committee and Operating Committee. Representation on this task force follows established Planning Committee and Operating Committee guidelines for participation. Members are appointed by their Region or electric industry sector for two-year terms, without limit to the number of terms. Any Region or electric industry sector may name an alternate representative(s) who may attend task force meetings. Order of Business In general, the desired, normal tone of the task force business is to strive for constructive technically sound solutions which also achieve consensus. On the relatively few occasions where that desired outcome cannot be achieved, the task force will defer to a determination by the Planning and Operating Committees to settle the issue. If any strong minority opinions develop, those opinions may be documented as desired by the minority and forwarded to the PC and OC Chair for future meeting consideration. Reporting The task force is responsible to the Planning and Operating Committees for the completion of work associated with the scope items outlined above. Final work products of the task force will be approved as necessary by the Planning and Operating Committees and, if necessary, by the NERC Board of Trustees. The task force chairs will periodically apprise the Planning Committee, Operating Committee, and Board of Trustees, as required, on the task force's status, activities, assignments, and recommendations.


Meetings Weekly to biweekly conference calls can be expected. Additionally, two to three open in-person meetings per year may be needed.

Approved by the NERC Planning Committee: ____________, 2014

Approved by the NERC Operating Committee: ____________, 2014


Essential Reliability Services Task Force (ERSTF) Work Plan 2014-2015 Work Plan

Item

Activity

Abstract

Lead

Deliverables

Milestones

1 Technical Reference -

Whitepaper Develop a technical white paper on ERS. The white paper will be used as a reference manual for regulators and policy makers and to inform, educate, and build awareness on the ERS elements essential for the reliability of the BPS. Recommendations for next steps included within whitepaper. Identify, define and formulate a standardized set of Essential Reliability Services (ERS). The definition will be technology-neutral, utilize performance based terms that can be used to meet the operational needs of the BPS.

• Define each ERS

• Describe why each ERS is important for bulk power system reliability

• Describe how each ERS fits into the planning and operational needs of the bulk power system.

• Describe current status of ERS and projected strains on ERS due to variable resources.

NERC Staff Review and endorsement by ERSTF Approved by PC/OC

• 10-15 page document

Final Reviewed by OC/PC March 2014 Meeting

Page 1 of 4


Item

Activity

Abstract

Lead

Deliverables

Milestones

2 Special Reliability

Assessment – Metrics and Approaches for Evaluating Essential Reliability Services

Develop an approach and framework for the long-term assessment of essential reliability services to supplement existing resource adequacy assessments. The new approach may include the development of metrics for further evaluation in future long-term reliability assessments. This assessment should include an evaluation and/or reconciliation of emerging ERS impacts in terms of current conditions and potential future trends. Identify metrics, procedures, and methodologies to determine the need for, provide, and maintain ERS for an electric system.

• How is the analysis performed?

• What data is needed?

• What are the parameters that can be used to gauge acceptable levels of ERS?

• How are the parameters related to the overall composition of the resource mix, today and into the future?

ERSTF • Metrics, Measures and Methodology

Final Reviewed by OC/PC December 2014 Meeting

Page 2 of 4


Item

Activity

Abstract

Lead

Deliverables

Milestones

2A Scenario Reliability

Assessment – Impact on ERS due to increase in variable generation, and retirement of base generation.

Frequency Response. What strains these services? How are various regions strained by different scenarios?

ERSTF Support with RAS/SAMS

• Scenario Assessment Scope Reviewed by OC/PC December 2014 Meeting Final Report Reviewed by OC/PC June 2015

3 Special Reliability Assessment – Proposals for Performance Expectations of Essential Reliability Services

Develop specific recommendations for practices and requirements, including reliability standards, that cover the planning, operations planning, and real-time operating procedures.

ERSTF • Whitepaper – recommendations

• SARs if needed • Guideline

recommendations and draft

Scope Reviewed by OC/PC June 2015 Meeting Final Report Reviewed by OC/PC December 2015

4 Specific Reliability Assessment – Provide a guidance document incorporating ERS in operational tools.

Provide a roadmap for operations personnel along with appropriate tools needed to operate and maintain grid reliability.

ERSTF Support from OC

• Guidance Document Scope Reviewed by OC/PC June 2015 Meeting

Page 3 of 4


Item

Activity

Abstract

Lead

Deliverables

Milestones

Final Report Reviewed by OC/PC December 2015

*See assessment scope document for more details on each activity/phase.

Page 4 of 4

DRAFT Task 1.6 Probabilistic Methods

10 Feb 2014

3353 Peachtree Road NE Suite 600, North Tower

Atlanta, GA 30326 404-446-2560 | www.nerc.com

NERC’s Mission

NERC’s Mission

The North American Electric Reliability Corporation (NERC) is an international regulatory authority for reliability of the bulk power system in North America. NERC develops and enforces Reliability Standards; assesses adequacy annually via a 10-year forecast and winter and summer forecasts; monitors the bulk power system; and educates, trains, and certifies industry personnel. NERC is a self-regulatory organization, subject to oversight by the U.S. Federal Energy Regulatory Commission (FERC) and governmental authorities in Canada.1

NERC assesses and reports on the reliability and adequacy of the North American bulk power system divided into the eight Regional Areas as shown on the map below (See Table A).2 The users, owners, and operators of the bulk power system within these areas account for virtually all the electricity supplied in the U.S., Canada, and a portion of Baja California Norte, México.

Note: The highlighted area between SPP and SERC denotes overlapping regional area boundaries: For example, some load serving entities participate in one region and their associated transmission owner/operators in another.

1 As of June 18, 2007, the U.S. Federal Energy Regulatory Commission (FERC) granted NERC the legal authority to enforce Reliability Standards with all U.S. users, owners, and operators of the bulk power system, and made compliance with those standards mandatory and enforceable. Reliability Standards are also mandatory and enforceable in Ontario and New Brunswick, and NERC is seeking to achieve comparable results in the other Canadian provinces. NERC will seek recognition in Mexico once necessary legislation is adopted.

2 Note ERCOT and SPP are tasked with performing reliability self-assessments as they are regional planning and operating organizations. SPP-RE (SPP – Regional Entity) and TRE (Texas Regional Entity) are functional entities to whom NERC delegates certain compliance monitoring and enforcement authorities.

Table A: NERC Regional Entities

ERCOT Electric Reliability Council of Texas

RFC ReliabilityFirst Corporation

FRCC Florida Reliability Coordinating Council

SERC SERC Reliability Corporation

MRO Midwest Reliability Organization

SPP Southwest Power Pool, Incorporated

NPCC Northeast Power Coordinating Council, Inc.

WECC Western Electricity Coordinating Council

IVGTF Task 6-1: Probabilistic Methods

Table of Contents

NERC’s Mission ................................................................................................................................ 0

Table of Contents ......................................................................................................................... 1

Nomenclature ......................................................................................................................... 1

Chapter 1: Introduction ......................................................................................................... 1

Decision Problems and Associated Uncertainties ...................................................................... 2

Probabilistic and Deterministic Methods ................................................................................... 1

Variable Generation .................................................................................................................... 2

Example: Planning with VG ..................................................................................................... 5

Example: Operations with VG ................................................................................................. 5

Chapter 2: Methods and Data Requirements ................................................................. 1

Data Requirements ..................................................................................................................... 2

Incorporating Uncertainty .......................................................................................................... 2

Model, Model Outputs and Decision Criteria ............................................................................. 3

Basic Computational Approaches ............................................................................................... 6

Convolution ............................................................................................................................. 6

Markov Models, Including Frequency & Duration .................................................................. 6

Enumeration ........................................................................................................................... 7

Monte Carlo ............................................................................................................................ 7

Chapter 3: Application Areas .......................................................................................................... 1

Generation Expansion Models .................................................................................................... 1

Resource adequacy ................................................................................................................. 2 IVGTF Task 1-6 Probabilistic Methods

1

Flexibility ................................................................................................................................. 3

Network planning ....................................................................................................................... 6

Transmission Networks ........................................................................................................... 7

Distribution Networks ........................................................................................................... 13

Operations Planning ................................................................................................................. 14

Chapter 4: Conclusions and Recommendations ........................................................... 1

Conclusions ................................................................................................................................. 1

Recommendations ...................................................................................................................... 2

IVGTF Task 1-6 Probabilistic Methods 2

Nomenclature BA – Balancing Area CAISO - California ISO CREZ - Competitive Renewable Energy Zones ELCC - Effective Load Carrying Capability EPRI - Electric Power Research Institute EUE - Expected Unserved Energy EWITS - Eastern Wind Integration and Transmission Study GIS - Geographical Information Systems HILP - High Impact Low Probability IEEE - Institute of Electrical and Electronics Engineers IRRE - Insufficient Ramping Resource Expectation IVGTF - Integration of Variable Generation Task Force LOLE - Loss of Load Expectation LOLH - Loss of Load Hours LOLP - Loss of Load Probability MISO – Midcontinent (formerly Midwest) ISO NREL - National Renewable Energy Laboratory NXT - Network Expansion Tool RAS - Remedial Action Schemes SCDT - Study Case Development Tool SCED - Security Constrained Economic Dispatch SCUS - Security Constrained Unit Commitment TransCARE - Transmission Contingency and Reliability Evaluation PV - Photovoltaic VG - Variable Generation WECC - Western Electricity Coordinating Council WREZ - Western Renewable Energy Zones

IVGTF Task 1-6 Probabilistic Methods

1

Chapter 1: Introduction

In April 2009 the NERC Integration of Variable Generation Task Force (IVGTF) released its landmark special report entitled: “Accommodating High Levels of Variable Generation.”3 One of the primary findings of that report is that as the penetration of variable generation (VG) reaches relatively high levels, the characteristics and operation of the bulk power system will be significantly altered. The primary drivers of this change are the increases in the overall system variability and amount of uncertainty encountered in decision problems.

The IVGTF Report resulted in a number of conclusions and recommended actions to develop the planning and operational practices as well as the methods and resources needed to integrate variable generation resources into the bulk power system. The IVGTF Report highlighted the need for risk assessmment and probabilistic methods to assist in the integration of VG. It did so primarily in the context of planning, but numerous references throughout the report also suggested that operations could benefit from improved methods that can broadly be referred to as probabilistic methods.

Arising from the recommendations of the NERC IVGTF Report multiple tasks were established. Task 1.6, focused on probabilistic methods, is the subject of this report and is reflective of the increased attention that these methods are getting. Variability and uncertainty are not new to the power system, and part of this increased attention on probabilistic methods reflects advancements in computing speed and algorithms – methods that were previously intractable due to data size and computer processing time are increasingly within our reach. But growing levels of wind and solar energy are clearly serving as catalysts for these efforts. For example, the 2012 NERC Summer Reliability Assessment points out that more probabilistically based methods will be needed as the penetration of wind energy (and solar energy) increases on the bulk power system.4

To various degrees, other IVGTF tasks are related to probabilistic methods and are referenced as appropriate in this report. Task 1.2 “Methods to Model and Calculate Capacity Contributions of Variable Generation for Resource Adequacy Planning”5 and Task 1.4 “Flexibility Requirements and Metrics for Variable Generation: Implications for System Planning Studies”6 are particularly relevant and in many ways are subsets of the probabilistic methods reported on here.

3 NERC, “Special Report -- Accommodating High Levels of Variable Generation”, http://www.nerc.com/files/IVGTF_Report_041609.pdf, April 2009

4 NERC; “Summer Reliability Assessment” North American Electric Reliability Corporation, 2012. http://www.nerc.com/files/2012SRA.pdf 5 NERC. Integration of Variable Generation Task 1.2, "Methods to Model and Calculate Capacity Contributions of Variable Generation for

Resource Adequacy Planning" North American Electric Reliability Corporation, 2011, http://www.nerc.com/files/ivgtf1-2.pdf 6 NERC . Integration of Variable Generation Task 1.4, "Flexibility Requirements and Metrics for Variable Generation: Implications for System

Planning Studies" North American Electric Reliability Corporation, 2010, http://www.nerc.com/files/IVGTF_Task_1_4_Final.pdf


1

http://www.nerc.com/files/IVGTF_Report_041609.pdf

http://www.nerc.com/files/2012SRA.pdf

The objective of this report is to summarize the potential influence on power system operating and planning decision problems of increased uncertainty caused by high VG penetration levels, and to describe the role that probabilistic methods can play in improving the basis on which the various decisions are made.

Decision Problems and Associated Uncertainties

There are six classes of decision problems in power systems engineering which are influenced by uncertainties associated with increased levels of VG penetration. These are:

1. Reserves: How much and what type of regulation and contingency reserves to have in the next 10 minutes, next hours, next day?

2. Dispatch: How to dispatch generation in next 10 minutes? 3. Commitment: How to schedule unit commitment in next hours, next day? 4. Maintenance: When to allow generation and transmission to be scheduled out of

service for maintenance in next month, season, year? 5. Generation planning: How much and what type of capacity should be developed, and

where, over next 1-5 years, next 10 years? 6. Transmission planning: How much and where should additional transmission capacity be

developed over next 1-5 years, next 10 years?

Associated uncertainties may be grouped into three classes: (a) operating conditions (e.g., MW generation and load); (b) element unavailability due to failure or maintenance (e.g., generation resource, transmission circuit, protective device) ; (c) performance of an element (e.g., speed of breaker to open or speed of a unit to ramp). Decision problems influenced by VG encounter increased uncertainty in the first two classes (a & b) and increased significance of the uncertainty in the third class (c). The most obvious of these is that (a) operating conditions become more uncertain due to the inherent uncertainty in the wind and solar forecast for each VG installation. The uncertainty of (b) exists at three levels: an entire VG may experience an outage due to failure at its point of interconnection, or it may experience a sharp decrease in MW production due to failure of a collector circuit or due to a fast change in wind or solar resource. The uncertainty of (c) related to speed of a unit to ramp is made more significant by the presence of high VG penetration levels because of the increased variability and consequential need for greater reserves and faster ramping capability.

This conceptualization of uncertainty as it relates to the time frame of the various decision problems is summarized in Table 1 below.


Table 1: Most significant uncertainties related to VG

TIME FRAME

UNCERTAINTY

Variable generation (windfarm level)

Conventional generation (unit level)

*Demand (bus level) Transmission (cct level)

Real-time market (10 mins)

Forced un-availability

10 min forecast


10 min ramp capability

10 min forecast

10 min ramp capability


Intra-day scheduling (1-6 hrs)


Hrs-ahead forecast


10 min & 1 hour ramp capability

Hrs-ahead forecast



Day-ahead market (1 day)


1 day forecast



1 day forecast



Seasonal planning (3 months)

Forced & scheduled un-availability

3 month forecast



3 month forecast



Mid-term planning (1-5 years)


5 year forecast

Location & type of new VG



Location & type of new gen

5 year forecast



Location & capacity of new xmission

Long-term planning (>10 years)


10 year forecast

Location & type of new VG



Location & type of new gen

10 year forecast



Location & capacity of new xmission

* “Demand” is considered to include bilateral transmission schedules.


1

Probabilistic and Deterministic Methods

Probabilistic methods are referred to under various names. There is no clear consensus on the distinction between probabilistic methods and risk assessment methods, and we make no distinction here. Therefore the scope ranges from methods that calculate loss of load probabilities and other reliability indices, to expected consequences7, variance, and value at risk. Deterministic methods are special limited cases as they cannot be used to calculate risk.8

Methods based on loss of load probability (LOLP) are well known and often applied to resource adequacy assessments. For example, two otherwise identical power systems might be designed with the same deterministic planning reserve margin (percentage by which installed generation exceeds peak load) but they may have a different probabilistic loss of load expectation (LOLE).9 In the traditional “N-1” deterministic power systems analysis approach, there is no differentiation between a “10km transmission line supplying a highly meshed part of the network” and a “200 km line supplying a less meshed load center” with significantly different probabilities of occurrence and consequences.10 These N-k deterministic methods are based on a level of redundancy and do not have any explicit economic considerations around the consequences of unserved energy (EUE).

Probabilistic methods allow many situations to be treated rationally and systematically that can only be treated in an ad hoc way using deterministic rules. Probabilistic methods are more informative – giving estimates of how much, how long, how often, and quantifying the expected consequences of investments (planning) or operator actions (expected cost of operation). Interestingly, probabilistic methods have been investigated in conjunction with deterministic approaches in an attempt to get the best of both worlds.11

NERC is adopting more probabilistic methods in general. For example NERC is running a Pilot Probabilistic Assessment to produce enhanced resource adequacy metrics for NERC’s Long-Term Reliability Assessment. NERC traditionally gauged resource adequacy using a deterministic Planning Reserve Margin metric. The pilot is investigating two probabilistic

7 Li, W.;: Risk Assessment Of Power Systems: Models, Methods, and Applications, John Wiley & Sons, 13 May 2005 325pp http://books.google.ie/books?id=wvmHYncAZhYC&lpg=PP1&pg=PP1#v=onepage&q&f=false

8 Li, W.; Choudhury, P.; , "Probabilistic Transmission Planning," Power and Energy Magazine, IEEE , vol.5, no.5, pp.46-53, Sept.-Oct. 2007, doi: 10.1109/MPE.2007.904765 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4295037&isnumber=4295014

9 Milligan, M.; Porter, K. (2008). Determining the Capacity Value of Wind: An Updated Survey of Methods and Implementation; Preprint. 30 pp.; NREL Report No. CP-500-43433. Available at http://www.nrel.gov/docs/fy08osti/43433.pdf

10 CIGRE 2010 “Review of the Current Status of Tools and Techniques for Risk-Based and Probabilistic Planning in Power Systems" CIGRE October 2010

11 Billinton, R.; Huiling Bao; Karki, R.; , "A Joint Deterministic - Probabilistic Approach To Bulk System Reliability Assessment," Probabilistic Methods Applied to Power Systems, 2008. PMAPS '08. Proceedings of the 10th International Conference on , vol., no., pp.1-8, 25-29 May 2008 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4912682&isnumber=4912596


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http://books.google.ie/books?id=wvmHYncAZhYC&lpg=PP1&pg=PP1%23v=onepage&q&f=false

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4295037&isnumber=4295014

http://www.nrel.gov/docs/fy08osti/43433.pdf


metrics, Loss of Load Hours (LOLH) and Expected Unserved Energy (EUE).12 Unserved energy can be monetized, although this is a difficult problem.13,14

Probabilistic methods allow for the quantification of tail events, making it possible to then assess their risk. The mean of a distribution measures only the central tendency of the variable in question, and does not provide an accurate assessment of their risk. The risk averse nature of society in general, and power systems operation in particular, imply that use of mean values should often be complemented with information on tail events.

The use of probabilistic methods in power system planning and operations has been a growing trend for the past 30 years but is significantly accelerating due to the increase in VG penetration as well as many other drivers including:

• The development of competitive markets and the consequential separation of transmission and generation planning have led to increased uncertainty, which has spurred the development and use of probabilistic methods that can account for it. The time difference between the construction of generation (e.g. 3 years) and transmission (e.g. 10 years) increases this uncertainty.

• The availability of inexpensive computing resources that have allowed computationally intensive probabilistic methods to be more readily explored and eventually implemented in practice.

• The societal push for less environmentally impactful solutions (e.g., public opposition to transmission infrastructure) has led to a growing need to extend the limits of the system and quantify the risk/reward trade-offs.

• There is increasing interest in capturing High Impact Low Probability (HILP) events.

Variable Generation

The addition of VG into the power system does not fundamentally change the problems that must be solved, both in operations and in planning. Power system operators will still need to keep the system balanced and operating in a reliable and efficient manner. Sufficient generation and transmission must be planned for and operated. However, VG, by its variable and uncertain nature, increases complexity since fixed quantities cannot be readily used as they are currently used in conventional deterministic methods for planning and operations. In this

12 NERC 2012B; “Pilot Probabilistic Assessment” North American Electric Reliability Corporation, 2012. : http://www.nerc.com/files/2012_ProbA.pdf

13 Herman, R.; Gaunt, T.; , "Probabilistic interpretation of customer interruption cost (CIC) applied to South African systems," Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on , vol., no., pp.564-568, 14-17 June 2010

doi: 10.1109/PMAPS.2010.5528947 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5528947&isnumber=5526245 14 Leahy, Eimear & Tol, Richard S.J., 2011. "An estimate of the value of lost load for Ireland," Energy Policy, Elsevier, vol. 39(3), pages 1514-1520,

March.


http://www.nerc.com/files/2012_ProbA.pdf


sense, probabilistic methods may be the only proper way to inform decisions for systems with significant penetrations of variable generation.

As VG penetration is growing, demand side management is also growing and has the potential to significantly help the integration of VG by increasing system flexibility).15,16,17 Accounting for demand side management further increases the need for the use of probabilistic methods for power system studies.18,19

While all load and generation contributes to uncertainty, VG adds in particularly distinct ways that drive the trend in the development of probabilistic methods, in particular:

• VG is a distributed resource with uncertainty around its future locations and in the resource quality of each possible location, can come in multi GW blocks and/or KW blocks, and is a low capacity factor resource. This makes planning transmission and distribution networks far more challenging.

• VG can have particularly short construction times relative to transmission, requiring transmission planners to consider a range of possible generation expansion scenarios, and therefore coordinated risk-based transmission and generation planning becomes more important.

• On the operational time scales, the uncertainty in forecasting (due to the nature of weather) is adding to the short term temporal uncertainty.20

• From a resource adequacy perspective, VG is largely an energy resource. Its capacity contribution can be relatively small (and declining gradually with higher penetrations).

15 GE Energy, 2010: Western Wind and Solar Integration Study. Golden, CO: National Renewable Energy Laboratory, New York, May, 536pp. http://www.nrel.gov/wind/systemsintegration/wwsis.html

16 R. Sioshansi, "Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation," IEEE Transactions on Power Systems, Vol 25, No 2, pp 741-748, May, 2010.

17 Kirby, B, M.J. O’Malley, O. Ma, P. Cappers, D. Corbus, S. Kiliccote, O. Onar, M. Starke, and D. Steinberg, “Load participation in Ancillary Services”, Workshop Report. Department of Energy, USA, 2011. http://www1.eere.energy.gov/analysis/pdfs/load_participation_in_ancillary_services_workshop_report.pdf

18 Kashyap, A.; Callaway, D.; , "Estimating the probability of load curtailment in power systems with responsive distributed storage," Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on , vol., no., pp.18-23, 14-17 June 2010 doi: 10.1109/PMAPS.2010.5528896 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5528896&isnumber=5526245

19 Kazerooni, A.K.; Mutale, J.; , "Network investment planning for high penetration of wind energy under demand response program," Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on , vol., no., pp.238-243, 14-17 June 2010, doi: 10.1109/PMAPS.2010.5528517 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5528517&isnumber=5526245

20 Holttinen, Hannele; Kiviluoma, Juha; Estanqueiro, Ana; Gómez-Lázaro, Emilio; Rawn, Barry; Dobschinski, Jan; Meibom, Peter; Lannoye, Eamonn; Aigner, Tobias; Wan, Yih Huei; Milligan, Michael. 2011. Variability of load and net load in case of large scale distributed wind power. Proceedings of 10th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, 25 - 26 October, 2011, Aarhus, Denmark. Energynautics, ss. 177-182


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http://www.nrel.gov/wind/systemsintegration/wwsis.html

http://www1.eere.energy.gov/analysis/pdfs/load_participation_in_ancillary_services_workshop_report.pdf



However even this energy contribution is subject to significant variation over seasonal and annual time scales.21

• VG has uncertain impacts on the rest of the system that need to be accounted for. For example there is an increased need for access to flexibility in the remaining generation fleet22 but quantification and encouragement of this flexibility is difficult.23,24 As VG displaces other energy resources, it is important to ensure that necessary flexible capacity is available to the system operator.25 To ensure this flexible capacity is available requires (a) physical response capability, which can be either generation, demand response, or storage and (b) possessing the proper incentives to invest in, and operate this flexible capability when needed. This could be accomplished with markets or with regulatory oversight, of a combination of both.

While considering application of probabilistic paradigms and methods, the tendency is to focus on long term resource and transmission planning needs. However, it is critical to note that system operation will be increasingly subject to the same uncertainties but to a more limited extent. Currently an operator needs to only focus on the uncertainties in demand variations, together with generation and transmission outages, when performing its operations planning (scheduling and committing resources within hours to days of real-time) or system operation (dispatch of available dispatchable resources within minutes to hours of real-time) responsibilities. These tasks are currently performed using well established deterministic paradigms and methods. However, large penetration of VGs significantly increases the magnitude of the uncertainty that the operator needs to deal with when performing its various tasks. The primary uncertainty involves VGs’ uncertain output level even in the near future. Although this uncertainty is significantly smaller than the one dealt with in longer-term planning, the timeline to deal with unforeseen events is also shorter with significantly fewer options to deal with adverse effects. Hence, from our viewpoint, the introduction of probabilistic paradigms and methodologies into system operation will also be critical.

Below, two descriptive examples illustrate the impact that VG will have on planning and operations.

21 Wiser, R. Bolinger, M.; "2011 Wind Technologies Market Report" US Department of Energy, Energy Efficiency & Renewable Energy, DOE/GO-102012-3472 August 2012 http://www1.eere.energy.gov/wind/pdfs/2011_wind_technologies_market_report.pdf

22 NERC (2010b). Integration of Variable Generation Task 1.4, "Flexibility Requirements and Metrics for Variable Generation: Implications for System Planning Studies" North American Electric Reliability Corporation, 2010

23 Lannoye, E., Flynn, D., O’Malley, M., “Evaluation of Power System Flexibility” IEEE Transactions on Power Systems, Vol. 27, pp. 922 – 931, 2012.

24 Gottstein, M.; Skillings, S.A.; , "Beyond capacity markets — Delivering capability resources to Europe's decarbonised power system," European Energy Market (EEM), 2012 9th International Conference on the , vol., no., pp.1-8, 10-12 May 2012

25 Hogan, M., Gottstein, M . What Lies “Beyond Capacity Markets”? Delivering Least-Cost Reliability Under the New Resource Paradigm. August 2012. http://www.raponline.org/document/download/id/6041


http://www1.eere.energy.gov/wind/pdfs/2011_wind_technologies_market_report.pdf

http://www.raponline.org/document/download/id/6041

Example: Planning with VG Typical mid- to long-term transmission planning studies (as well as generation interconnection studies) are based on one or more study scenarios (base cases) which are snapshots of critical system operating conditions in the future. The system snapshots typically correspond to the peak system load condition, off-peak (very low) system load condition and shoulder peak (medium) system load conditions. In every one of these study scenarios, all generators' output and load values are assumed to be at specific MW levels. In most current systems, such system snapshots are reasonably accurate and the transmission upgrades that would be derived from these studies would be reasonable as well. Given the highly uncertain VG output levels especially at bus levels, the operational picture for these study snapshots will be less predictable and deterministic as VG penetrations grow.

To cope with such strong uncertainty, some transmission planners would assume the worst-worst condition in building their study scenarios (e.g., assuming that wind generators are generating zero power at the time of summer peak load condition and maximum power at the time of minimum system load). Others assume an arbitrary generation value for VG resources for their study scenario; say, 20% of nameplate for wind at summer peak system load condition and 100% at off-peak and shoulder-peak load conditions. As one can imagine, the outcome of such "arbitrary" scenario selections can lead to over or under estimation of the need for transmission upgrades. Probabilistic transmission planning studies, whereby VG output (and potentially that of conventional generation and load) levels would vary within a range based on a probability distribution, should help overcome the problems associated with deterministic transmission planning in the presence of large VG penetration. In a probabilistic planning paradigm, one would not rely on the traditional deterministic planning criteria of no loss of load under Category B (N-1) contingency conditions or some planned loss of load under Category C (common mode N-2) contingency conditions to develop/test/accept a "least cost best fit" transmission upgrade. Instead, one would rely on a threshold for the loss of load probability (LOLP) of one event in ten years or other similar measures to develop a transmission upgrade or weigh the benefits of a set of proposed upgrades.

Example: Operations with VG Typical short-term operations planning studies examine the operation of the power system under one or more operational snapshots – from a few hours up to one week in advance. The deterministic study criteria here are similar to those of planning described above: no loss of load under Category B (N-1) and some planned loss of load under Category C (common mode N-2) contingency conditions. Of course, unlike long-term planning, the outcome of operations planning is not to determine the transmission or generation upgrades needed to maintain system reliability but to ensure system operation will remain secure given the resources that are available and could be made available (committed) within time frames available prior to actual real-time system operation. With low VG penetrations, at the timeframe when short-term operations planning studies are performed, the status of the system is fairly accurately known and as such operations planning results are deemed accurate and certain. However, given the uncertainty and variability introduced by VGs, high VG penetrations can introduce a


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somewhat higher level of uncertainty in operations planning study results. Under these circumstances, given the critical nature of the operations planning studies, the tendency of the operations planners could be to assume the worst possible operational characteristics for VGs resulting in "super-secure," yet very inefficient, system operation. Here again, probabilistic operations planning studies can be used to strike a better balance between the need for system operational security and efficiency. Probabilistic operations planning studies would look at a range and probability for VG outputs as well as system contingency conditions and verify the power system operational security based on an acceptable threshold for LOLP or other probabilistic system performance measures.

Numerous sources of uncertainty and variability impact the power system simultaneously.26 VG increases both variability and uncertainty but it is the combined variability and uncertainty that must be dealt with to reliably and economically operate the power system. Researchers are developing probabilistic techniques beginning to evaluate the uncertainties of the balancing capacity, ramping capability, and ramp duration requirements in power systems operations and operations planning.27,28 The approach proposed by Makarov et al includes three steps: forecast data acquisition, statistical analysis of retrospective information, and prediction of grid balancing requirements for a specified time horizon and a given confidence level. It includes a probabilistic algorithm based on histogram analysis, capable of incorporating multiple sources of uncertainty—both continuous (wind and load forecast errors) and discrete (forced generator outages and startup failures). A new method called the “flying-brick” technique was developed to evaluate the look-ahead required generation performance envelope for the worst-case scenario within a user-specified confidence level. A framework for integrating the proposed methods with an energy management system (EMS) was also developed. To improve the system control performance, maintain system reliability, and minimize expenses related to system balancing functions, it is necessary to incorporate expected wind and load uncertainties into scheduling, load following, and, to some extent, into regulation processes. Some wind forecast service providers in both North America and Europe offer uncertainty information for their forecasts, including probabilities of extreme ramping events. The proposed method addresses the uncertainty problem comprehensively by including all types of uncertainties (such as load, variable generation, etc.) and all aspects of uncertainty including the ramping requirements. The main objective is to provide rapid (every 5 min) look-ahead (up to 5 to 8 hours ahead) assessment of the resulting uncertainty ranges for the balancing effort in terms of the required capacity, ramping capability, and ramp duration.

26 Makarov, Y.V.; Shuai Lu; Samaan, N.; Zhenyu Huang; Subbarao, K.; Etingov, P.V.; Jian Ma; Hafen, R.P.; Ruisheng Diao; Ning Lu, “Integration of uncertainty information into power system operations,” IEEE Power and Energy Society General Meeting, July 2011

27 Makarov, Y.V.; Etingov, P.V.; Jian Ma; Zhenyu Huang; Subbarao, K., “Incorporating Uncertainty of Wind Power Generation Forecast Into Power System Operation, Dispatch, and Unit Commitment Procedures,” IEEE Transactions on Sustainable Energy, Volume: 2 , Issue: 4, 2011, pp. 433 – 442.

28 Makarov, Y. V.; Etingov, P. V.; Huang, Z.; Ma, J.; Chakrabarti, B. B.; Subbarao, K.; Loutan, C.; Guttromson, R. T., “Integration of wind generation and load forecast uncertainties into power grid operations,” Proc of the 2010 PES Transmission and Distribution Conference and Exposition, 2010


Case studies were run with the prototype tool to test the uncertainty assessment approach and to demonstrate the capabilities of the tool. CAISO’s actual data were used in the simulation and the tool development. The actual data used include total load, total wind generation, load forecast (day-ahead, hour-ahead, and real-time forecast), and wind generation forecast. Genetic algorithms are used to optimize unit commitment and economic dispatch. Next steps include implementing the methods in an actual EMS.

As the need for probabilistic criteria and methods for risk assessment is a growing area in power system analysis, the next section (Chapter 2) offers an assessment of these methods and the data requirements. Chapter 3 covers VG related applications areas where probabilistic methods are used. Chapter 4 concludes and offers recommendations.


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Chapter 2: Methods and Data Requirements

A review of the literature shows that there is research activity in probabilistic methods in power systems analysis, design and control across the planning and operations domains. The planning area has had the most activity, due to the greater levels of uncertainty. A recent CIGRE report29 did a comprehensive review on probabilistic planning in power systems. It concluded that methods and tools are available, but mainly for adequacy issues, with limited capabilities to address other uncertainties such as location, timing and availability for proposed new generation. The difficulty in obtaining the necessary quality and quantity of data is limiting their applicability especially for high-impact low-probability (HILP) events. Despite the availability of powerful computational resources, the area is still computationally constrained and the interpretation and translation of results for practical applications is still challenging. The CIGRE report had a number of case studies and interestingly only two out of seven had a significant VG dimension (both wind cases from Denmark), which underlines that VG is but one of many drivers of the development of probabilistic methods in power systems.

A forthcoming CIGRE Technical Brochure on Coping with Limits for Very High Penetrations of Renewable Energy from Joint Working Group C1/C2/C6.18 of Study Committee C6 is also noteworthy.30 The Joint Working Group used a quantitative and qualitative survey of 50 CIGRE members from across the world. Some of the survey questions related specifically to “new criteria (probabilistic planning)”. The joint working group received 30 completed surveys from 19 countries across Europe, North America, Oceania and Asia. There was a low rate of response on the probabilistic planning issue which may be linked to the fact that few consolidated models and planning procedures exist. However it was noted that probabilistic planning methods are under development together with the definition of new planning criteria. It was concluded that the use of probabilistic methods to identify network reinforcements is becoming increasingly common but is not yet an accepted standard. CIGRE also had a working group to specifically investigate planning with the uncertainty of wind generation.31 Another recent CIGRE activity, explicitly assessing risk management, did not have an emphasis on VG.32

Key aspects of probabilistic methods include identification of the decision problem of interest and associated uncertainties influencing that problem, data inputs, incorporation of uncertainty within the models, model outputs, interpretation and risk formulation and model execution. These aspects are described below.


30 CIGRE 2012; Technical Brochure on Coping with Limits for Very High Penetrations of Renewable Energy, Joint Working Group C1/C2/C6.18 of Study Committee C6, August 2012, International Conference on Large High Voltage Electric Systems

31 CIGRE Technical Brochure 293, Electric Power System Planning with Uncertainty of Wind Generation, April 2006, CIGRE WG C1.3 (www.e-cigre.org).

32 CIGRE 2011; “Assessing and improving power system security, reliability and performance in light of changing energy sources”, Special report for session 8 Risk Analysis - CIGRE Report - Asset Management 2011


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Data Requirements

Variable generation is a difficult probabilistic input to introduce into the power system. Unlike many other uncertain inputs, variable generation output does not conform to a normal distribution. This negates many of the standard statistical assumptions that are made on the basis of a normal distribution. Even if a normal distribution could be assumed, special attention must be paid to high-impact low-probability (HILP) events. HILP events, such as all variable VGs operating simultaneously near rated capacity or a sudden drop in VG generation, have the potential to disrupt the power system. Because they are low probability, these events may not be captured or may be strongly discounted in traditional analyses.

Variable generation data must also be selected carefully due to its time dependence and cross-correlation with other natural events. For example, wind generation may be correlated with load, hydro production and solar output.33 Thus a single year of data may not be extrapolated to other years and matched data must be used for meteorological data, load and other generation resources.34 Failure to adhere to this can result in time periods when the load is from a hot day when peak loads are high, at the same time wind power is from a different year when it is cool and rainy. Using data from the same year, whether actual or a high-fidelity simulation, is important.

Incorporating Uncertainty

There are two dominant ways to integrate uncertainty into deterministic problems: probability density functions and scenarios. The type of uncertainty representation should reflect the goal of the analysis, the level of underlying uncertainty and knowledge of the underlying uncertainty.

Scenario analysis is the most common representation of uncertainty in probabilistic methods applied to power systems. It is an intuitive way to reduce uncertainty and allows problems that may be otherwise untenable to be solved. Scenario analysis also allows bounding results by examining, for example, both the best and worst case. Reducing the range of uncertainty to scenarios, however, implies correlations between variables that may be fictional. For example, a high coal price is not necessarily correlated with high natural gas price even though a “high fossil cost” case may be constructed. It also has the potential to bias results because the highest probability cases may not be those modeled or conversely HILP scenarios may be excluded. The most simplistic approach is the deterministic approach, which is scenario analysis with an assumed probability of one.

33 This correlation is unlikely to occur on an hourly basis, but may be present over longer time periods. See the discussion in Keane, A.; Milligan, M.; Dent, C.J.; Hasche, B.; D'Annunzio, C.; Dragoon, K.; Holttinen, H.; Samaan, N.; Soder, L.; O'Malley, M.; , "Capacity Value of Wind Power," Power Systems, IEEE Transactions on , vol.26, no.2, pp.564-572, May 2011, doi: 10.1109/TPWRS.2010.2062543 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5565546&isnumber=5753358

34 Milligan, M.; Ela, E.; Lew, D.; Corbus, D.; Yih-huei Wan; Hodge, B., "Assessment of Simulated Wind Data Requirements for Wind Integration Studies," Sustainable Energy, IEEE Transactions on , vol.3, no.4, pp.620,626, Oct. 2012



Probability density functions (PDFs)—usually referred to more succinctly as probability distributions—are the most granular form of uncertainty representation. The use of PDFs generally implies the use of multiple input models (such as from ensemble-based weather forecasting) or the creation of a large set of plausible outcomes (such as with Monte Carlo techniques). PDFs allow the greatest search space because artificial correlations between variables are not required. Because PDFs do not include artificial correlations, unintuitive combinations of decision variables can be explored that may produce new best- or worst-case outcomes. Although the large search space produced by PDFs provides the most neutral analysis of a problem, it may also increase the size of the problem to the point where it cannot be solved in a reasonable amount of time.

The use of scenarios and PDFs are not mutually exclusive. For example, it may be appropriate to have some variables with very low variability represented using scenarios and other variables with long-tailed distributions as PDFs.

Model, Model Outputs and Decision Criteria

Here we distinguish three features of a decision problem: the model, the model output, and the decision criteria. The model may be considered to be deterministic or probabilistic.

If a model is deterministic, then all parameters used in the model are single-valued, typically characterizing a single condition for which it is assumed the decision can be appropriately based. The condition is often “worst-case,” providing that the resulting decision will result in satisfactory operation for all other conditions. The output for deterministic models must necessarily be one or more “performance measures,” i.e., parameters characterizing the physical performance of the system. For example, a power flow model for a 20% wind energy penetration level (under peak load conditions) may indicate that the flow on a particular circuit is 1000 MW, whereas a 21% wind energy penetration may result in a 1020 MW flow on the same circuit. The decision criteria for a deterministic model is based on acceptance or rejection of certain levels of physical performance. In our example, the decision criteria is that all circuit flows must remain at or below 1000 MW, and so the 20% wind penetration level is acceptable, whereas the 25% level is not.

If a model is probabilistic, then some or all parameters used in the model are represented over a range characterized by a probability function, so that many different operating conditions are captured. The output for probabilistic models is a range of values of one or more performance measures, also characterized by a probability function, or else characterized by attributes of a probability function (measures of center and spread are enough for Gaussian distributions; measures of shape are also needed for non-Gaussian distributions). For example, a power flow model for a 20% wind energy penetration level may result in the flow on a particular circuit (assumed Gaussian for this example) having a mean of 700 MW and standard deviation of 150 MW, whereas a 21% wind energy penetration level may result in the same circuit flow having a mean of 720 MW and standard deviation of 170 MW. The decision criteria for the probabilistic model is based on acceptance or rejection of certain levels of risk. In our example, the decision


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criteria might be that all circuit flows must remain at or below 1000 MW for 95% of the time. Because it is known that Gaussian distributed random variables remain within the mean plus or minus twice the standard deviation with probability 0.95, the 20% wind penetration level (700+2×150=1000) is acceptable, but the 21% wind penetration level (720+2×170=1060) is not.

Probabilistic models can be constructed to produce distinct output types. Three broad categories of results are discussed below: deterministic, probabilistic and trade-off. Probabilistic outputs may consist of several types of outputs, as discussed below.

Deterministic results are answers without embedded probabilistic information. This type of result is easily translated into a single action, for example, a dispatch instruction or new transmission line to develop, without an indication of the risk of that action. Deterministic results are generally the most common and easiest to produce. It is possible for probabilistic methods to produce a single answer without producing information regarding the risk of being wrong. Scenario-tree analysis, for example, can produce a single-output result by calculating a statistically-expected outcome. Although there may be value in also producing multiple results with their associated estimated probabilities, in cases requiring quick action a simpler form of output may be desired.

In addition to single-actions, probabilistic models can also be used to produce deterministic rules. These rules indicate the correct actions for different scenarios. One example from the Western Wind and Solar Integration Study explores the development of simple rules to determine operating reserve (sometimes called ‘flexibility reserve’) that is needed to balance load and wind (p. 237).35 The rules are suggested from statistical analysis rather than probabilistic analysis, based on covering 3σ of the variability of an assumed normal distribution. One of the example rules for a particular BAA is to hold operating reserve equal to 3% of load plus 5% of the wind generation. This is a dynamic method that is based on the load and wind energy at each moment in time.

Probabilistic results with multiple outputs allow the decision maker (system operator and/or planner) to make an explicit decision about risk. These results may be a single-action output as above or a mapping of output action to risk. A single-action can be produced in a probabilistic setting by choosing an explicit risk level. A common example of this is in the resource adequacy domain where a common LOLE target is 1 day/10 years, and a generation portfolio that can achieve this level of reliability is deemed adequate. In other applications the risk level could indicate that the action is optimal with a calculated distribution of outcomes or that the action will produce the desired outcome with a certain risk. For example, a dispatch could be created which will satisfy demand with a 95%, 99%, or 99.9999% probability. This is an example of how a given risk level can be translated into some form of confidence interval, specifying that the dispatch would be effective in maintaining system balance (and interchange schedules) with a

35 GE Energy, 2010: Western Wind and Solar Integration Study. Golden, CO: National Renewable Energy Laboratory, New York, May, 536pp. http://www.nrel.gov/wind/systemsintegration/wwsis.html


http://www.nrel.gov/wind/systemsintegration/wwsis.html

given probability. An example of this can be found in http://www.nrel.gov/docs/fy12osti/52330.pdf36 which develops a series of ramping envelopes up to 12 hours in duration and at different probability levels, which we show in a later section of this report.

Alternatively, probabilistic results can be created which inform a decision but do not specify an action. For example using sampling and simulation, a relationship between wind output, flexibility reserve levels and non-served energy could be developed. For a given wind output level and reserve level, the probabilistic result would indicate the likelihood of non-served energy. This result does not indicate the correct decision but allows the decision maker to explicitly decide an acceptable level of risk. Of course, a decision rule could be developed that captures the decision maker’s risk tolerance.

Probabilistic models can also be used to demonstrate trade-offs in a multi-objective framework. Pareto curves or efficient frontiers inform the decision maker of the best solution(s) that can be constructed for a performance metric without sacrificing value from another performance metric. Pareto curves are developed in a space of two or more objectives that conflict, i.e., when one improves, the others worsen. Pareto curves may be made with either deterministic objectives or probabilistic objectives or a combination of both. For example, an efficient frontier could be constructed where cost is on one axis and probability of non-served energy is on the other. The efficient frontier then indicates the lowest possible cost for given reliability levels or, alternatively, what reliability level can be achieved for each cost point.

This efficient frontiers approach has been has been applied in industry. Hydro Quebec used it to study balancing reserves when hypothetically adding 3,000 MW of wind power to the system. For this Hydro Quebec case, the trade-off is between increased balancing reserves and decreased risk of non-served load.37 As shown in Figure 1, at a nominal amount of balancing reserves (BRnom), the system exists at 17% risk level (point one). When wind in a high generation scenario is added to the system (point two), the same level of balancing reserves produces a 25% risk of non-served energy. To return to the 17% risk rating, the balancing reserves must be increased by ∆BR to 650 MW (point three). Both with and without wind generation, the system operator is able to trade-off increased balancing reserves and decreased risk.

36 J. King, B. Kirby, M. Milligan, S Beuning, “Flexibility Reserve Reductions from an Energy Imbalance Market with High Levels of Wind Energy in the Western Interconnection”, NREL/TP-5500-52330, October 2011, http://www.nrel.gov/docs/fy12osti/52330.pdf

37 M. Milligan et al, “Operating reserves and wind power integration, an international comparison,” 9th Annual International Workshop on Large-Scale Integration of Wind Power into Power Systems, Quebec, Canada, October 2009.


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Figure 1 Balancing reserve and risk trade-off diagram from Hydro Quebec (adapted from Milligan et al 2009)

Basic Computational Approaches

There are several different computational approaches used within probabilistic methods. In this section, we briefly outline several of the most common computational approaches applied for power system analyses.

Convolution Convolution is a method appropriate when one obtains a probabilistic description of a random variable which is the sum or difference of other random variables for which probability descriptions are known. There are several computational approaches, including recursion, Fourier transform, method of cumulants, and segmentation.

Markov Models, Including Frequency & Duration A Markov process is a random process, i.e., a probabilistic representation of transitions between defined states in which a system or component may reside. A Markov process is said to be “memoryless” because the present state “summarizes” the entire history of the process, i.e., all of the information contained in the values taken by the random variables of the past are


contained in the random variable of the present.38 In general, a Markov process may have any number of states, and the component or system may reside in any one of them (but not in more than one of them simultaneously). The key to Markov representation is then to express the probability of transitioning from one state to each other possible state. If these so-called transition probabilities can be obtained, then the probability of finding the system in any particular state in the future may be obtained based on the knowledge of the state in which it presently resides. It is also possible to compute the “long-term” state frequency and duration. The state frequency is the expected number of stays in (or arrivals into, or departures from) a particular per unit of time; the state duration is the expected amount of time per stay the random process is in a particular state.

Enumeration In the pure enumeration approach, one simulates all possible events and classifies each one according to some particular outcome. The probability of a given outcome is the summed probabilities of the events for which the outcome occurred. The pure enumeration approach can be computationally expensive, so when it is applied (e.g., for power system contingency analysis), the number of contingencies are typically restricted in some way, to provide bounds on the desired outcome probabilities.

Monte Carlo In Monte Carlo simulation, a functional evaluation is performed under parameter uncertainty. The parameter uncertainty is modeled by representing each uncertain parameter with a numerical distribution. Then a value for each uncertain parameter is drawn from each distribution, and for each draw, the function is evaluated. This is repeated many times, so that the many functional evaluations themselves form a distribution from which statistics, e.g., mean, variance, etc., may be computed.

Model Execution Probabilistic methods can be effectively included in power system modeling. Once an operator/planner has decided on appropriate formulation and risk metric, the problem remains to be solved. Some of the modeling techniques utilize optimization methods but the problems themselves are not inherently optimization problems. One example is production simulation, which uses cost-minimization, an optimization method, to simulate the power system operation over a given time period. Monte Carlo simulation, for example, uses probabilistic information about a random variable (RV), or multiple RVs, to generate multiple scenarios. It is often possible to parallelize the analysis, particularly if Sequential Monte Carlo is used. Solving this class of problems may therefore be made computationally less demanding if multiple

38 Although we do not discuss them here, we note the existence of processes that do have “memory,” such as auto-regressive integrated moving average (ARIMA) processes.


7

processors can be devoted to them. Other approaches include convolution, which is often used in LOLP applications, for example.

Other problems are inherently orientated to optimization, for example providing an optimal transmission buildout, subject to the objective function and various constraints. Problems such as this represent the most difficult problems from an optimization perspective. They are multi-period, stochastic, integer, and often nonlinear. As mentioned above, even if a problem may be formulated in a commercial solver, a solution may not be found on an appropriate timescale given the complexity and size of the problem. This is especially true when actual utility, rather than academic-scale, problems are attempted. Thus approximations may be necessary. In cases that cannot benefit from some type of parallelization or other computational improvement, and are therefore computationally challenging, there are a variety of simplifications and off-line simulations that can be done beforehand to incorporate probability without requiring exhaustive modeling of every possible solution of the full problem. One option when there is a mismatch between solution time and real timescales is off-line preprocessing. Off-line preprocessing shifts the required computational time to prior to the decision point. This allows running multiple optimizations before the decision must be made and using the closest set of inputs and running models in increasing complexity to refine inputs. If off-line preprocessing is insufficient, the problem may also be simplified to reduce its size. Reducing the size of the problem can be achieved by switching from a full PDF representation of uncertainty to scenarios or reducing the number of scenarios considered. If the uncertainty is not the main source of computational time, constraints may be relaxed (e.g. allowing for construction of fractional transmission lines and then rounding to the nearest line size). The solution to the relaxed problem must be validated as it may not be realistic.

Finally, if the problem cannot be solved using traditional techniques, meta-heuristics may be used. Unlike traditional optimization techniques, meta-heuristic solution algorithms are neutral to the type of problem considered. This neutrality allows them to be applied to probabilistic and other traditionally difficult problems. Meta-heuristics include genetic algorithms, simulated annealing, Tabu search, ant colony, and many other biologically inspired search algorithms. The advantage of these algorithms is that they can produce solutions quickly and often provide solutions to problems where traditional solvers cannot. The inherent disadvantage, however, is that there is no guarantee to the quality of the solution produced – the solution may be good but cannot be proven to be the best optimal solution.


Chapter 3: Application Areas

VG adds to the complexity of power systems long- and short-term planning; however, planning the bulk power system for the future has always been subject to various risk factors that are suitable to the application of probabilistic methods. These factors include (a) fuel price and fuel delivery capacity uncertainty39, which may become more important with high reliance on natural gas40 and higher uncertainty levels that accompany increased penetrations of wind/solar energy, for example; (b) unforeseen economic stagnation (or growth) that reduces (or increases) the demand and, hence, the need for new facilities; (c) regulatory risk; (d) uncertainty regarding future operating rules, emission limits, etc.; (e) meteorological conditions impacting hydro generation and more recently wind and solar generation output uncertainty and variability; (f) retirement plans for existing resources; (g) transmission development in neighboring systems; (h) construction delays; and (i) risks of new technologies. The generation and transmission system should be robust across multiple possible scenarios. This leads to the need for more robust planning processes for both generation/resource adequacy and flexibility, along with network (transmission & distribution) system planning and design. Operationally, VG also brings increased levels of uncertainty that can benefit from the application of probabilistic methods.

In this chapter, applications in the supply planning area are dealt with under the heading of generation expansion models that includes resource adequacy and flexibility. This is followed by network planning, both transmission and distribution, and finally operations planning including forecasting, reserve estimation and stochastic unit commitment.

Generation Expansion Models

The portfolio of future generation must perform over a wide range of conditions because the future mix of generation and load conditions can’t be known with certainty. In terms of uncertainty around future costs, it should be noted that the levelized cost of energy from VG is more certain than the levelized cost of other forms of generation, so VG reduces cost uncertainty in this case. This point highlights the uncertainty at different time scales – VG is uncertain at short timescales (minutes to days) and less so at long time scales of months and years.41 Resource planning, whether performed by regulated utilities or conducted via market mechanisms, must provide a fleet of generation that is itself robust to alternative levels of development, including varying amounts of VG. What is needed is one or more generation build-out scenarios that provide sufficient capacity and sufficient flexibility so that the future power system can be operated reliably and economically.

39 Roques, Fabien A. & Newbery, David M. & Nuttall, William J., 2008. "Fuel mix diversification incentives in liberalized electricity markets: A Mean-Variance Portfolio theory approach," Energy Economics, Elsevier, vol. 30(4), pages 1831-1849, July

40 NERC, “2012 Long-Term Reliability Assessment”, http://www.seia.org/sites/default/files/resources/2012_LTRA_FINAL.pdf, November 2012 41 Holttinen, H.; Kiviluoma, J.; Estanqueiro, A.; Gómez-Lázaro, E.; Rawn, B.; Dobschinski, J., Meibom, P.; Lannoye, E.; Aigner, T.; Wan Y.H.;

Milligan, M.; "Variability of load and net load in case of large scale distributed wind power" http://repositorio.lneg.pt/bitstream/10400.9/1518/1/Task%2025%20Variability%20paper%20final.pdf



http://www.seia.org/sites/default/files/resources/2012_LTRA_FINAL.pdf

http://repositorio.lneg.pt/bitstream/10400.9/1518/1/Task%2025%20Variability%20paper%20final.pdf

Resource adequacy A key issue when planning for future generation is to determine how much generation is needed by a given future date to serve the expected future load while maintaining a desired reliability level. A related issue is whether an over-building or under-building of generation can be alleviated by additional market transactions with neighboring systems (exports and imports). Traditional approaches have targeted the estimated future peak load plus a planning reserve margin. More robust approaches incorporate LOLP or related methods, which provide for an estimate of the reliability of the power supply. VG does not fundamentally change the problem that must be solved; however it does influence the relationship between the planning reserve margin, expressed as a percentage of peak load, and resource adequacy. The NERC IVGTF 1.2 Task Force Report recommends a probabilistic approach, based on effective load carrying capability (ELCC), to estimate resource adequacy.42 This is consistent with the recommendations of a recent IEEE task force paper on the subject of capacity value/credit calculations.43

Multiple years of data are critical to achieve the objective of resource adequacy assessment. Limited data on extremes may mean that it is not possible to quantify any relevant statistical relationship even with multiple years of data. Therefore, there are efforts to better understand the tails of the relevant probability distributions for VG production, unit outages, and other factors. This is important because LOLP and related metrics provide a measure of some aspect of the tail of the probability distribution of insufficient resources to meet load. Milligan et al.44 analyzed 3 years of data and compared annual ELCC for wind. At some sites there was little variation in the capacity value; however one site varied from 27% to 42% of maximum capacity. An earlier analysis also included the evaluation of solar and other types of renewable resources.45 A study in Ireland showed that 4 years of data provides reasonable assurance that the wind capacity value, measured by ELCC, is a stable measure given enough data.46 Dent and Zachary found that limited historic experience of high demands coincident with poor wind resource leads to large uncertainties in the results of capacity value calculations.47 Work on the capacity credit of solar energy is moving forward but there is much work to be done.48 An industry case study on probabilistic resource adequacy assessment, including wind, of the Mid-

42 NERC (2011a). Integration of Variable Generation Task 1.2, "Methods to Model and Calculate Capacity Contributions of Variable Generation for Resource Adequacy Planning" North American Electric Reliability Corporation, 2011, http://www.nerc.com/files/ivgtf1-2.pdf

43 Keane, A.; Milligan, M.; Dent, C.J.; Hasche, B.; D'Annunzio, C.; Dragoon, K.; Holttinen, H.; Samaan, N.; Soder, L.; O'Malley, M.; , "Capacity Value of Wind Power," Power Systems, IEEE Transactions on , vol.26, no.2, pp.564-572, May 2011, doi: 10.1109/TPWRS.2010.2062543 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5565546&isnumber=5753358

44 Milligan, M.; Shiu, H.; Kirby, B.; Jackson, K. (2006). Multi-Year Analysis of Renewable Energy Impacts in California: Results from the Renewable Portfolio Standards Integration Cost Analysis; Preprint. 40 pp.; NREL Report No. CP-500-40058. Available http://www.nrel.gov/docs/fy06osti/40058.pdf

45 B. Kirby, M. Milligan, Y. Makarov, D. Hawkins, K. Jackson, H. Shiu, 2003, California RPS Integration Cost Analysis-Phase I: One Year Analysis of Existing Resources, California Energy Commission, December . Available at http://www.consultkirby.com/files/RPS_Int_Cost_PhaseI_Final.pdf

46 Hasche, B., Keane, A. and O’Malley, M.J. “Capacity value of wind power: calculation and data requirements: The Irish power system case”, IEEE Transactions on Power Systems, Vol. 26, pp. 420 - 430, 2011.

47 Dent, C.; Zachary, S.; "Capacity Value of Additional Generation: Probability Theory and Sampling Uncertainty" Probabilistic Methods Applied to Power Systems (PMAPS), 2012 IEEE 12th International Conference

48 Duignan, R. Chris J. Dent, Andrew Mills, Member Nader Samaan, IEEE and Michael Milligan, “Capacity Value of Solar Power” IEEE PES, San Diego, July 2012




http://www.consultkirby.com/files/RPS_Int_Cost_PhaseI_Final.pdf

Continent Area Power Pool (MAPP) for the 10-year planning horizon from 2009 – 2018 was recently reported.49

Flexibility There are two interrelated aspects to flexibility. Resources themselves (generators, responsive loads, and storage), for example, are flexible if they can change states quickly: start/stop, ramp, have low minimum loads, and follow AGC signals quickly and accurately. Future system plans (including generation, transmission, storage, and responsive load) are flexible if they can deliver the required energy reliably, economically, and environmentally under the full range of expected and possible future conditions. Increasing levels of variable generation may increase the need for both types of flexibility. A flexible plan will likely include flexible resources but it may also include a significant quantity of inflexible resources if this provides greater reliability, lower costs, and/or environmental benefits while still providing sufficient flexibility to meet future needs. Flexibility needs for future time periods (years) can be assessed, and some form of market mechanism or regulatory requirement (or combination) is required to ensure investment in this flexibility. This problem is in the planning domain, involving the design and building of a system that is sufficiently flexible. The objective is to build the best possible (or at least sufficiently flexible considering both economics and reliability) system that has the needed flexibility. Once the flexibility has been planned, designed, and built, it must be made available to the system operator via the commitment and dispatch process. This is the operational domain, and its objective is to make the best possible use of existing resources. These are two different problems; however, it is clear that if the system is not planned well, it may not be flexible enough to operate efficiently. Thus efficient planning, design, and building of the needed flexibility is a necessary, but not sufficient condition for achieving flexibility in operations. It is not a sufficient condition because there may be conditions that make it difficult or impossible for the existing flexibility to be accessed when needed. These constraints may include (a) lack of information (b) lack of institutional/market structures, or other factors.

In systems with large amounts of VG, increased flexibility needs include: (a) more and faster ramping; (b) lower minimum generation; (c) faster start-up times; (d) smaller minimum up/down times; (e) faster and more accurate following of AGC signals; (f) appropriate market design; and (g) access to required transmission. Generation, storage, and demand response can all provide these flexibility attributes. IVGTF Task 1.4 addressed this issue in detail (NERC, 2010b), and Milligan et al. addresses some potential market challenges.50,51

49 Bagen, B.; Koegel, P.; Couillard, M.; Stradley, K.; Giggee, B.; Jensen, A.; Iverson, J.; Haringa, G.E.; , "Probabilistic resource adequacy assessment of large interconnected systems," Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on , vol., no., pp.252-258, 14-17 June 2010, doi: 10.1109/PMAPS.2010.5528519 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5528519&isnumber=5526245

50 NERC (2010b). Integration of Variable Generation Task 1.4, "Flexibility Requirements and Metrics for Variable Generation: Implications for System Planning Studies" North American Electric Reliability Corporation, 2010, http://www.nerc.com/files/IVGTF_Task_1_4_Final.pdf

51 Milligan, M.; Holttinen, H.; Soder, L.; Clark, C.; Pineda, I.; “Markets to Facilitate Wind and Solar Energy Integration into the Bulk Power Supply: an IEA Task 25 Collaboration.” Presented at the 11th Annual International Workshop on Large-Scale Integration of Wind Power into Power


3


In the long-term, the amount, type, and location of VG that will be installed is often unknown. As is the case for resource adequacy, there is likely a need for flexibility adequacy that essentially establishes the level of ramping or other flexibility that will be sufficient given this uncertainty level.52 In the operational domain, the concern is that the precise level, timing and duration of the ramping need and availability of the flexible resources are all uncertain. This is because VG performance is based on weather conditions that can’t be precisely known. Load ramping tends to be somewhat predictable, whereas VG ramps are less predictable.53 Their statistical characterization can be formulated, and combined with the load characterization for an estimate of the net load ramping characteristics, assuming some knowledge about the installed capacity, type, and location of the variable generation.54 As an example, Figure 2, illustrates the distribution of the magnitude of increasing and decreasing net load (load - wind) ramps as a function of the duration of the ramp. Ramp envelopes, describing the percentile range of the ramps, highlight the extreme values in the distribution. The graph is based on one year of data that consists of 10-minute load and wind data. Each ramp envelope shows ramp durations of alternative magnitudes and duration at a given probability level. Graphs and analysis such as this only help establish the need for ramping capability. Additional analysis, modeling, and/or tools are needed to assess the generation fleet’s capability to provide this ramping.

Systems as Well as on Transmission Networks for Offshore Wind Power Plants Conference, Lisbon, Portugal ,November 13–15, 2012. Preprint available at http://www.nrel.gov/docs/fy12osti/56212.pdf

52 NERC , Integration of Variable Generation Task 1.4, "Flexibility Requirements and Metrics for Variable Generation: Implications for System Planning Studies" North American Electric Reliability Corporation, 2010, http://www.nerc.com/files/IVGTF_Task_1_4_Final.pdf

53 Mills, Andrew. (2010). Understanding Variability and Uncertainty of Photovoltaics for Integration with the Electric Power System. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. LBNL Paper LBNL-2855E. Retrieved from: http://escholarship.org/uc/item/58z9s527

54 Bouffard, F.; Ortega-Vazquez, M.; , "The value of operational flexibility in power systems with significant wind power generation," Power and Energy Society General Meeting, 2011 IEEE , vol., no., pp.1-5, 24-29 July 2011 doi: 10.1109/PES.2011.6039031 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6039031&isnumber=6038815



http://escholarship.org/uc/item/58z9s527


Figure 2 Alternative ramp envelopes for different confidence intervals55

Operational tools can be applied to this problem in a planning context, but existing tools do not generally assess the ramping capability in a probabilistic way, or otherwise account for the risk of not being able to access ramping capability when it is needed. An exception is the model of Lannoye et al., which calculates a probabilistic metric called the Insufficient Ramping Resource Expectation (IRRE), which is an analogue to LOLE.56 The basic difference is that LOLE is based on installed capacity, whereas IRRE is based on the ability of that capacity to ramp (essentially the first derivative of capacity w.r.t. time), accounting for forced outages. This metric thus captures part of the probabilistic nature of the problem of providing ramp capability when it is needed. Other approaches and methods to quantify flexibility are starting to appear in the literature.57,58

55 J. King, B. Kirby, M. Milligan, S. Beuning, 2011, Flexibility Reserve Reductions from an Energy Imbalance Market with High Levels of Wind Energy in the Western Interconnection, NREL/TP-5500-52330, November, http://www.nrel.gov/docs/fy12osti/52330.pdf

56 Lannoye, E., Flynn, D., O’Malley, M., “Evaluation of Power System Flexibility” IEEE Transactions on Power Systems, Vol. 27, pp. 922 – 931, 2012

57 Ma, J., Silva, V., Belhomme, R., Kirschen, D. Evaluating and Planning Flexibility in Sustainable Power. IEEE Transactions on Sustainable Energy, in press, 2012

58 Menemenlis, N.; Huneault, M.; Robitaille, A.; , "Thoughts on power system flexibility quantification for the short-term horizon," Power and Energy Society General Meeting, 2011 IEEE , vol., no., pp.1-8, 24-29 July 2011 doi: 10.1109/PES.2011.6039617 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6039617&isnumber=6038815

-60000

-40000

-20000

0

20000

40000

60000

0 2 4 6 8 10 12

Ram

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)

Ramp Duration (Hours)

Probability of Net Ramp Magnitude and Duration

100% Prob.99.9% Prob.99% Prob.95% Prob.90% Prob.


5

http://www.consultkirby.com/files/NREL-TP-5500-52330_Flexibility_Reserve_Reductions_from_an_EIM.pdf





A fuller treatment of flexibility would involve the accounting for the stochastic nature of the ramp itself. The probabilistic aspect of the calculation involves a stochastic treatment of the generation fleet’s ability to provide the needed ramping capability when it is actually needed. Also needed is a method to estimate the distribution of available ramping capacity conditional on available assessment of possible ramp. A “double convolution” that treats both the ramping need and supply of ramping as stochastic variables would provide a more robust assessment of the flexibility needs, and how to supply them. More accurate probabilistic assessments of both the need for flexibility and the supply of flexibility would benefit greatly from more information about the shape of the relevant probability distributions, particularly the tail characteristics, and correlations between them. This can only be achieved with larger accurate data sets that describe VG performance, corresponding weather data, load data, and generating unit data that can characterize the nature of ramping behavior of the unit. Therefore there is significant research still required on these methods, but some applications are starting to appear.

Recently, some ISOs in North America have begun looking at the need for including ramping products in their markets to ensure sufficient ramping capability is available to respond to variability and uncertainty. For example, the California ISO (CAISO) has proposed a “Flexible Ramping” product which would be co-optimized with energy and ancillary services in their day ahead and real time processes.59 This would ensure sufficient ramping is available in each interval to manage a range of ramps defined by a confidence interval of up and down net load ramps based on analysis of historical data. A similar approach is being proposed in the Midcontinent ISO (MISO).60 Ramping capability is currently considered in day ahead security constrained unit commitment (SCUC) processes, ensuring enough ramping capability is made available to meet statistically determined ramping requirements for every time interval (Gribik, 2012).61 This will be extended to MISO’s day ahead security constrained economic dispatch (SCED) and real time SCED. Generators providing ramping in both CAISO and MISO would get compensated based on the marginal price of the ramping service, similar to existing energy and ancillary service markets. Including ramping requirements in this way ensures ramps can be met in an economically efficient way, while improving reliability.

Network planning

Increased levels of VG normally require substantial investment in transmission and distribution networks. The best large scale VG resources (in particular wind) tend to be far from major load centers and transmission solutions that are robust with respect to the possible locations of the VG deployment will need to be identified. Solar, in particular photovoltaic (PV) systems, are well suited as distributed generation (DG) and will drive the need for improved and/or new

59 Xu, L. and Tretheway, D. Flexible Ramping Products, California ISO, Available online: http://www.caiso.com/Documents/DraftFinalProposal-FlexibleRampingProduct.pdf

60 Navid, N., Rosenwald G. and Chatterjee, D. MISO Markets, Midewst Independent System Operator, Available online: https://www.midwestiso.org/Library/Repository/Communication%20Material/Key%20Presentations%20and%20Whitepapers/Ramp%20Capability%20for%20Load%20Following%20in%20MISO%20Markets%20White%20Paper.pdf

61 Gribik, P., Chatterjee, D., Nivad, N. “New Products and Models to Manage Uncertainty”, presented at IEEE Power and Energy Society General Meeting, July 2012


http://www.caiso.com/Documents/DraftFinalProposal-FlexibleRampingProduct.pdf

http://www.caiso.com/Documents/DraftFinalProposal-FlexibleRampingProduct.pdf

https://www.midwestiso.org/Library/Repository/Communication%20Material/Key%20Presentations%20and%20Whitepapers/Ramp%20Capability%20for%20Load%20Following%20in%20MISO%20Markets%20White%20Paper.pdf

https://www.midwestiso.org/Library/Repository/Communication%20Material/Key%20Presentations%20and%20Whitepapers/Ramp%20Capability%20for%20Load%20Following%20in%20MISO%20Markets%20White%20Paper.pdf

distribution networks. The VG technology itself is continuing to evolve and network solutions will need to account for potential future developments.

Research results are starting to appear that indicate that with increased VG, and the need for flexibility, expansion models will have to account for the sequential nature of power system operations, such as the starts of conventional generators, and this will require far more computing power.62,63 Combined with the additional computational power that is required for probabilistic methods it can lead to computationally intensive problems with long run times. Even so, there appears to be significant value in moving toward long-term time series analysis or other sequential methods.

Transmission Networks Probabilistic methods for steady-state transmission planning have recently been proposed.64,65,66 The French system operator has over the past few decades developed its own probabilistic methods to assess the static and dynamic security of real transmission systems under uncertainty.67 Smith et al. (2012) review transmission planning for wind energy and there are a few references to probabilistic methods.68

The variable nature of wind and solar resources with a relatively low capacity factor leads to an obvious reassessment of the classic N-1 deterministic planning criteria of transmission networks.69 Interestingly, Karki et al. point out that probabilistic techniques require data that may not be available and propose a model to simulate this data. Traditionally, deterministic N-

62 Shortt, A., Kiviluoma, J. and O’Malley, M., “Accommodating Variability in Generation Planning”, IEEE Transactions on Power Systems, in press, 2012

63 V. Krishnan, E. Ibanez, T. Das, Y. Gu, and J. McCalley, “Modeling Operational Effects of Variable Generation within National Long-term Infrastructure Planning Software,” to appear in IEEE Transactions on Power Systems.

64 Choi, J.; Tran, T.; El-Keib, A.A.; Thomas, R.; Oh, H.; Billinton, R.; , "A Method for Transmission System Expansion Planning Considering Probabilistic Reliability Criteria," Power Systems, IEEE Transactions on , vol.20, no.3, pp. 1606- 1615, Aug. 2005, doi: 10.1109/TPWRS.2005.852142 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1490615&isnumber=32048

65 Choi, J.; Mount, T.D.; Thomas, R.J.; Billinton, R.; , "Probabilistic reliability criterion for planning transmission system expansions," Generation, Transmission and Distribution, IEE Proceedings- , vol.153, no.6, pp.719-727, November 2006, doi: 10.1049/ip-gtd:20050205 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4015896&isnumber=4015882

66 Xu, Xiaokang; Edmonds, Michael J. S.; , "Probabilistic Reliability Methods and Tools for Transmission Planning and System Analysis," Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on , vol., no., pp.1-6, 11-15 June 2006, doi: 10.1109/PMAPS.2006.360254 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4202266&isnumber=4202205

67 Henry, S. J. Pompee, L. DeVatine, M. Bulot, K. Bell; “New Trends For The Assessment Of Power System Security Under Uncertainty” Assess, CIGRE 2004 http://areeweb.polito.it/eventi/irep2004/Session%20D3/D3_5.pdf

68 Smith, C.J., Osborn, D., Zavadil, R., Lasher, W., Gómez-Lázaro, E., Estanqueiro, A., Trötsche, Statnett T., Tande, J., Korpås, M., Van Hulle, F., Holttinen, H., Orths, A., Burke, D., O’Malley, M., Dobschinski, J., Rawn, B., Gibescu, M., Dale, L. “Transmission Planning for Wind Energy: Status and Prospects”, Wiley Interdisciplinary Reviews: Energy and Environment, in press, 2012

69 Karki, R.; Hu, P.; Billinton, R.; , "Adequacy criteria and methods for wind power transmission planning," Power & Energy Society General Meeting, 2009. PES '09. IEEE , vol., no., pp.1-7, 26-30 July 2009 doi: 10.1109/PES.2009.5275810 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5275810&isnumber=5260217


7


http://areeweb.polito.it/eventi/irep2004/Session%20D3/D3_5.pdf


1 has led to underutilization of the investment in transmission70 and VG with its relatively low capacity factors can make this worse. VG is often viewed as an energy resource, so from the point of view of transmission planning, building transmission capacity to accommodate the full rated amount of all VG simultaneously is likely to be too expensive. There is a tradeoff between under and over building transmission that can be assessed with probabilistic methods.

While wind and solar variability is significantly smoothed by aggregation over larger geographic areas, the remaining level of correlation can still have a significant impact on the optimal design of transmission systems.71 This correlation can also be used to reduce the amount of dimensionality of planning studies while maintaining the spatial correlations.72 Commercial considerations, particularly with regard to VG curtailment risk due to a lack of transmission, are spurring the development of probabilistic methods. For example, Burke and O’Malley developed a method to quantify curtailment that also investigated the uncertainty in these estimates and the influence of other interdependencies.73

The Electric Power Research Institute (EPRI) has been working on a methodology for probabilistically modeling the uncertainty related to the output of variable generation such as wind and solar power and coincidental loads since 2010. The objective of this modeling approach is to provide appropriate inputs to reliability analysis programs and other traditional software used in power system transmission planning. The output from the model is especially well-suited for developing inputs to probabilistic programs such as EPRI’s Transmission Contingency and Reliability Evaluation (TransCARE) in that it allows the uncertainty associated with the availability of variable generation to be properly captured in system wide reliability analysis.

The Eastern Wind Integration and Transmission Study (EWITS) used the effective load carrying capability (ELCC) metric to calculate the capacity contribution from wind energy, both with and without a hypothetical transmission build out.74,75 This work showed that wind capacity value is a function of transmission, among other factors. But more fundamentally, it is clear that the level of capacity required to achieve a specific target LOLE is driven in part by the transmission

70 O’Neill, R.P.; Hedman, K.W.; Krall, E.A.; Papavasiliou, A. Oren, S. "Economic analysis of the N-1 reliable unit commitment and transmission switching problem using duality concepts" Energy Syst DOI 10.1007/s12667-009-0005-6 December 2009 http://www.ieor.berkeley.edu/~oren/pubs/Economic_Analysis_N1_2009.pdf

71 Burke, D.J., O'Malley, M.J. (2011b). A Study of Optimal Nonfirm Wind Capacity Connection to Congested Transmission Systems. IEEE Transactions on Sustainable Energy, vol.2, no.2, pp.167-176, April 2011 doi: 10.1109/TSTE.2010.2094214 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5643190&isnumber=5735622

72 Burke, D.J. and O'Malley, M.J. (2011a). A Study of Principal Component Analysis Applied to Spatially Distributed Wind Power. IEEE Transactions on Power Systems, vol.26, no.4, pp.2084-2092, doi: 10.1109/TPWRS.2011.2120632 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5738711&isnumber=6048014

73 Burke, D.J. and O’Malley M.J. (2011c). Factors influencing wind energy curtailment”, IEEE Transactions on Sustainable Energy, vol. 2, pp. 185-193

74 EnerNex Corporation, 2010: Eastern Wind Integration and Transmission study (EWITS), prepared for the National Renewable Energy Laboratory, January 2010, http://www.nrel.gov/docs/fy11osti/47078.pdf

75 NERC (2011a). Integration of Variable Generation Task 1.2, "Methods to Model and Calculate Capacity Contributions of Variable Generation for Resource Adequacy Planning" North American Electric Reliability Corporation, 2011, http://www.nerc.com/files/ivgtf1-2.pdf


http://www.ieor.berkeley.edu/%7Eoren/pubs/Economic_Analysis_N1_2009.pdf



system. To investigate this further, Ibanez and Milligan developed a model of the Western Interconnection.76 The question they examined is this: what is the ELCC with “perfect transmission” (unlimited interconnection) in the West, compared to that of the system as currently planned and operated? They discovered that when individual balancing areas (BAs) develop their own generation requirements based on a target of 1d/10y, approximately 60 GW of additional generation is needed, above and beyond the case in which adequacy targets are developed with perfect transmission. This impact is caused by both the transmission expansion and full coordination among the various BAs that resulted from the transmission infrastructure. The key implication of this work is that transmission design and generation needs are inextricably linked, and the need for tools that account for this link will only increase with more VG.

The Eastern Wind Integration and Transmission Study (EWITS) also illustrates a good example of scenario-based transmission planning, along with an iterative approach to coordinate generation and transmission development to achieve the desired resource adequacy target. Alternative wind build-out scenarios were modeled to determine common transmission needs among the cases. The process recognized the interplay between the transmission configuration and resource adequacy, similar to that pointed out by Ibanez and Milligan (2012).

A real practical industry example of scenario based transmission planning can be found in The Midcontinent Independent Transmission System Operator (MISO). MISO has a very extensive Transmission Expansion Planning (MTEP) process that has adopted many probabilistic methods, is continually evolving, and in particular it has changed to account for the rapid increase in wind energy.77 Therefore it is an excellent example of state of the art probabilistic planning methods with increasing levels of variable generation. Significantly MISO is adopting probabilistic methods to account for all uncertainties but wind is not the most significant. Wind energy production ranks third as to the component contribution to the variability of Net Load behind Load and Non Scheduled Interchange. Load, Non Scheduled Interchange and Wind are the major sources of Net Load variability.

The MISO Value Based (Economic & Reliability) Planning process was developed to produce transmission expansion plans for very large power systems. The results of multiple scenarios or futures that have unique generation forecasts and unique transmission system conceptual design are tested for robustness against other futures transmission conceptual designs. Weighted probabilistic analysis is performed to select a single robust transmission system expansion that could best meet the future’s requirements. The MISO transmission planning process is characterized by an extensive stakeholder process which is used to develop scenario

76 Ibanez, E.; Milligan, M., “Impact of Transmission on Resource Adequacy in Systems with Wind and Solar Power,” IEEE Power and Energy Society General Meeting, 2012. San Diego, CA

77 https://www.midwestiso.org/Planning/TransmissionExpansionPlanning/Pages/TransmissionExpansionPlanning.aspx


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weights, rather than weighting them equally which was the practice until 2013. These weights are used to value different plans across all scenarios and hence to find the one that is most robust with respect to the different possible futures and multiple objectives including cost minimization, reliability, renewable energy mandates and resource adequacy. The conversion of stakeholder beliefs of what the future involves requires a survey and the conversion of the results using a Rasch model into numerical weights.78

The MISO system is very large with 65,000 buses and 9,000 generators and the computational challenge it poses for a Monte Carlo approach is very significant. MISO has to model neighboring systems as well as the MISO system to obtain accurate study results as a result of market interactions.

NREL models and MISO experience show that full wind energy production is not greater than 91% of the installed capacity. MISO wind generation is located in an area of 680x640 miles. The chance that wind would be sufficient for maximum generation everywhere is unlikely. Full wind output is modeled for the 70% load shoulder studies for reliability studies. Wind energy has a 14% capacity credit established by the Equivalent Load Carrying Capability method using the GE MARS probabilistic Loss of Load Probability Program. The power transfer capability of the robust transmission plan is used for Resource Adequacy calculations and the wind capacity factor analysis when using the LOLP program. This trade-off between transmission investment and curtailed wind is consistent with the fundamental goal of the MTEP to provide access to the lowest cost electric energy for the consumer by addressing local and regional reliability needs. 79 This is a similar approach to that adopted in the Texas Competitive Renewable Energy Zones (CREZ) described below.

In some parts of the U.S., locations for potential renewable (VG) development have been identified so that transmission planning could move forward even in advance of renewable development. The CREZ in Texas allowed ERCOT to plan for (and start building) transmission, sized for future high renewable build-out levels, in some cases prior to renewable build-out.80,81 Although zone renewable build-out is not guaranteed, knowing the likely zones of future renewable development reduces the risk of building transmission to the wrong locations or not building transmission until the VG is developed, thereby stranding some or all of the renewable energy until the line is built.

The Long Term Planning Tools Task Force of the Western Electricity Coordinating Council are developing a Study Case Development Tool (SCDT) and a Network Expansion Tool (NXT) to

78 http://www.rasch.org 79https://www.midwestiso.org/Planning/TransmissionExpansionPlanning/Pages/BenefitsofMTEP.aspx 80 B. Kirby, 2007, Evaluating Transmission Costs and Wind Benefits in Texas: Examining the ERCOT CREZ Transmission Study, The Wind Coalition

and Electric Transmission Texas, LLC, Texas PUC Docket NO. 33672, April, www.consultkirby.com 81 Smith, C.J., Osborn, D., Zavadil, R., Lasher, W., Gómez-Lázaro, E., Estanqueiro, A., Trötsche, Statnett T., Tande, J., Korpås, M., Van Hulle, F.,

Holttinen, H., Orths, A., Burke, D., O’Malley, M., Dobschinski, J., Rawn, B., Gibescu, M., Dale, L. “Transmission Planning for Wind Energy: Status and Prospects”, Wiley Interdisciplinary Reviews: Energy and Environment, in press, 2012


http://www.rasch.org/

http://www.consultkirby.com/

study long term scenarios and develop transmission plans.82 The Geographical Information System (GIS)-based tools optimizes generation and transmission build-out, and incorporates consideration of a wide range of inputs, including policy targets, environmental limitations, and government mandates. They use information from the Western Renewable Energy Zones (WREZ) project (modeled on ERCOT’s CREZ ), which independently developed “likely” locations where large capacities of VG would be developed. Using information on load hubs, renewable energy hubs, existing transmission, and existing/future generation attributes, the attributes of future generation mixes are calculated and generation and transmission are optimized. There is a requirement that the probabilities of horizon end-states must be determined and communicated clearly to users. The purpose of the WREZ is, in part, to narrow the universe of potential building locations for VG, which therefore reduces the uncertainty of building transmission to undeveloped VG locations.83

In the fall of 2013, the Eastern Interconnection States’ Planning Council (EISPC) initiated efforts to examine whether existing deterministic transmission planning processes and tools are adequate moving forward or should be augmented with probabilistic methods. To this end, the EISPC engaged EPRI to develop a probabilistic transmission planning white paper and to conduct a limited number of probabilistic transmission planning case studies with selected planning authorities. The EISPC white paper will serve as a primer on how to incorporate probabilistic assessment methods into existing transmission processes and surveying existing tools and methods. The EISPC case studies will provide for applying existing probabilistic planning tools to portions of the existing planning processes at Tennessee Valley Authority (TVA), Midcontinent Independent System Operator (MISO), and Southwest Power Pool (SPP).

It is also worth noting that remedial action schemes (RAS) are becoming more prevalent in transmission planning. Here again, probabilistic methods can help make their design more robust.84,85

Because transmission is so difficult to build in the U.S., transmission solutions must be robust through time in addition to being robust through space. Thus “single-shot” transmission planning may miss the robust solution and probabilistic methods that are robust for multiple future scenarios are critical. For example, as shown in Figure 3, the single-shot plan for 2035 produces a plan with both different corridors developed and different capacities than a dynamic programming model that optimized over 30 years with investment decisions every ten years. In this model, 50% of the lines constructed in the single-shot model are inconsistent with

82 WECC, 2012, https://www.wecc.biz/committees/BOD/TEPPC/TAS/LTPTTF/default.aspx 83 Nickell, B.; Long-term Planning Tool Demonstration, Presented at the CREPC and SPSC Joint Meeting, April 4, 2012 84 Wen, J.; Arons, P.; Liu, W.-H.E.; , "The role of Remedial Action Schemes in renewable generation integrations," Innovative Smart Grid

Technologies (ISGT), 2010 , vol., no., pp.1-6, 19-21 Jan. 2010, doi: 10.1109/ISGT.2010.5434770 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5434770&isnumber=5434721

85 Burke, D.J., O'Malley, M.J. (2011b). A Study of Optimal Nonfirm Wind Capacity Connection to Congested Transmission Systems. IEEE Transactions on Sustainable Energy, vol.2, no.2, pp.167-176, April 2011 doi: 10.1109/TSTE.2010.2094214 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5643190&isnumber=5735622


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the dynamic programming model with two 400 MW lines not constructed and one 400 MW line changed to a 750 MW line.

Single-Shot, 2035

Dynamic Programming, 2035

2035 Single-Shot Dynamic

Programming 400 MW 4 1 750 MW 1 2

1500 MW 3 3

Figure 3: Single-shot and Dynamic Programming Transmission Plans for Spain. Differences between the plans are circled in black on the single-shot map.86

In Great Britain, for a number of years the system planning standard has consisted of two components: a deterministic demand security part, and a cost-benefit analysis part balancing the cost of additional reinforcement against reduced cost of finite network capacity constraining the generation schedule. These cost-benefit analyses are carried out through calculation of expected (in the mathematical sense) constraint costs by non-sequential Monte Carlo simulation, with stochastic modeling of generation forced outages and available wind capacity, and the distribution of demand represented by a limited number of discrete levels. Future research and development prospects include improved wind power resource data for training the statistical wind resource model, and consideration of fluctuations of the constraint costs about the expected (mean) value output by the current modeling approach.87,88 The role of risk modeling in transmission planning with wind in Great Britain is described in Dent et. al.89

86 Donohoo, P. “Integrating Dynamics and Generator Location Uncertainty for Robust Electric Transmission Planning.” INFORMS Annual Meeting. November 13, 2011. Charlotte, North Carolina, USA

87 National Grid 2012 "Amendment Report GSR009: Review of Required Boundary Transfer Capability with Significant Volumes of Intermittent Generation ", available at http://www.nationalgrid.com/uk/Electricity/Codes/gbsqsscode/LiveAmendments/


http://www.nationalgrid.com/uk/Electricity/Codes/gbsqsscode/LiveAmendments/

Distribution Networks Connecting variable generation close to the load, such as with distributed solar PV or other distributed generation (DG), is a trend that brings with it many challenges that can benefit from probabilistic methods. Distribution networks were not designed for any significant level of local generation, and coupled with the stochastic nature of the distributed energy resources and the desire to optimize the network utilization, this leads to many challenging research questions. NERC IVGTF Task 1.8 documents the Reliability Impacts of Distributed Resources.90

Opathella et al. are exploring probabilistic load flow methods for distribution networks that take account of the stochastic nature of wind, while Zou et al., use a Monte Carlo based probabilistic load flow for similar purposes.91,92 Su proposes a probabilistic load flow method that incorporates the uncertainties associated with DG output, load demand, network configurations and the operation of voltage control devices.93 Accounting for the reactive power capability of DG units should lead to improved voltage levels in the distribution system, which would ultimately provide increased benefits in terms of improved system operation. However, incorporating the reactive power capability of DG together with system uncertainties can potentially increase the complexity of optimization tools for planning. Jayaweera et al., are developing probabilistic methods to quantify expected losses, voltage rise effects and wind integration capacity with distributed control of reactive power.94 Similarly, in Zou et al., a distribution system planning model is proposed which determines the optimal allocation for DG while minimizing the computational effort of the algorithm.95 The model accounts for the uncertainties inherent with DG and load demand by using probabilistic models while also incorporating the reactive power capability of the generation units.

88 National Grid 2012 "National Electricity Transmission System Security and Quality of Supply Standard Version 2.3", available at http://www.nationalgrid.com/uk/Electricity/Codes/gbsqsscode/DocLibrary/

89 Dent, C.J.; Bell, K.R.W.; Richards, A.W.; Zachary, S.; Eager, D.; Harrison, G.P.; Bialek, J.W.; , "The role of risk modelling in the Great Britain transmission planning and operational standards," Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on , vol., no., pp.325-330, 14-17 June 2010, doi: 10.1109/PMAPS.2010.5528890 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5528890&isnumber=5526245

90 NERC (2011b). Integration of Variable Generation Task 1.8, "Potential Bulk System Reliability Impacts of Distributed Resources" North American Electric Reliability Corporation, 2011, http://www.nerc.com/docs/pc/ivgtf/IVGTF_TF-1-8_Reliability-Impact-Distributed-Resources_Final-Draft_2011.pdf

91 Opathella, C.; Venkatesh, B.; Dukpa, A.; , "Probabilistic voltage solution method for distribution systems with wind electric generators," IPEC, 2010 Conference Proceedings , vol., no., pp.220-223, 27-29 Oct. 2010, doi: 10.1109/IPECON.2010.569710 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5697109&isnumber=5696950#

92 Zou, K.; Agalgaonkar, A.P.; Muttaqi, K.M.; Perera, S.; Browne, N.; , "Support of distribution system using distributed wind and PV systems," Power Engineering Conference, 2009. AUPEC 2009. Australasian Universities , vol., no., pp.1-6, 27-30 Sept. 2009

93 Su, C-L., "Stochastic Evaluation of Voltages in Distribution Networks With Distributed Generation Using Detailed Distribution Operation Models," Power Systems, IEEE Transactions on , vol.25, no.2, pp.786-795, May 2010 doi: 10.1109/TPWRS.2009.2034968 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5340599&isnumber=5452108

94 Jayaweera, D.; Islam, S.; Tinney, P.; , "Analytical approaches to assess embedded wind generation effects on distribution networks," Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on , vol., no., pp.419-424, 14-17 June 2010 doi: 10.1109/PMAPS.2010.5528959 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5528959&isnumber=5526245

95 Zou, K.; Agalgaonkar, A.P.; Muttaqi, K.M.; Perera, S.; , "Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties," Sustainable Energy, IEEE Transactions on , vol.3, no.1, pp.112-123, Jan. 2012 doi: 10.1109/TSTE.2011.2166281 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6102294&isnumber=6102278


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Improvements in the modeling of load demand will also be necessary if DG is to be successfully incorporated in future distribution system designs. The introduction of new loads, such as electric vehicles, together with demand side management schemes will alter the traditional load profiles, in particular at the low voltage residential level of the system. In Qian et al., and Richardson et al., probabilistic methods are proposed for modeling distribution system load with varying levels of electric vehicle penetration.96,97 Emerging flexible resources such as demand side management resources, electric vehicles, etc., and their potential impact on the integration of VG are detailed in the NERC IVGTF Task 1.5 report.98

Operations Planning

Operationally, the uncertainties primarily appear in the unit commitment and dispatch time frames. IVGTF Task 2.4 has done a comprehensive job on reviewing the state of the art in power system operations with VG.99 Here we concentrate on studies and applications where probabilistic methods are most relevant.

From the early days of variable generation integration into power systems, the forecasting of these resources has received significant attention.100 Of particular importance is forecasting on time scales of up to a few days ahead. A recent discussion by the CAISO can be found in the joint CAISO-NERC report.101 A comprehensive coverage of this technology can be found in the NERC IVGTF Task 2.1 report.102 As the technology has advanced, the concept of a probabilistic forecast has gained momentum – using not just one forecast, but a multitude of forecasts each with its own probability103,104 or a forecast represented as a probability distribution for each time value. For example, ERCOT has recently implemented a probabilistic wind forecasting system for ramps.105,106

96 Qian, K.; Zhou, C.; Allan, M.; Zhou, W.; , "Modeling of the Cost of EV Battery Wear Due to V2G Application in Power Systems," Energy Conversion, IEEE Transactions on , vol.26, no.4, pp.1041-1050, Dec. 2011 doi: 10.1109/TEC.2011.2159977 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5958591&isnumber=6083464

97 Richardson, P.; Taylor, J.; Flynn, D.; Keane, A., "Stochastic analysis of the impact of electric vehicles on distribution networks," Proceedings of CIRED 21st International Conference on Electricity Distribution, June 2011.

98 NERC (2010c). Integration of Variable Generation Task 1.5, "Potential Reliability Impacts of Emerging Flexible Resources" North American Electric Reliability Corporation, 2010, http://www.nerc.com/files/IVGTF_Task_1_5_Final.pdf

99 NERC (2011d). Integration of Variable Generation Task 2.4, "Operating Practices, Procedures, and Tools" North American Electric Reliability Corporation, 2011, http://www.nerc.com/files/ivgtf2-4.pdf

100 Kariniotakis , G; “European Research in Wind Power Forecasting. The objectives of the Anemos.plus and SafeWind projects”, ANEMOS Workshop, June 2011, http://www.anemos-plus.eu/

101 NERC 2013 Special Reliability Assessment: Maintaining Bulk Power System Reliability While Integrating Variable Energy Resources – CAISO Approach. Available at http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/NERC-CAISO_VG_Assessment_Final.pdf

102 NERC (2010d). Integration of Variable Generation Task 2.1, "Variable Generation Power Forecasting for Operations" North American Electric Reliability Corporation, 2010, http://www.nerc.com/files/Varialbe%20Generationn%20Power%20Forecasting%20for%20Operations.pdf

103 Pinson, P.; Madsen, H.; , "Probabilistic Forecasting of Wind Power at the Minute Time-Scale with Markov-Switching Autoregressive Models,"Probabilistic Methods Applied to Power Systems, 2008. PMAPS '08. Proceedings of the 10th International Conference on , vol., no., pp.1-8, 25-29 May 2008 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4912618&isnumber=4912596

104 This should not be confused with the closely related but distinct concept of ensemble forecasts where a number of different methods are used to produce multiple forecasts.

105 Doggett, T. "UPDATE ON WIND TECHNOLOGY" President & CEO, ERCOT National Association of Regulatory Utility Commissioners July 23, 2012 http://www.narucmeetings.org/Presentations/Wind%20tech%20Dogget%2023%20Jul%202012.pdf



http://www.nerc.com/files/ivgtf2-4.pdf

http://www.anemos-plus.eu/


http://www.narucmeetings.org/Presentations/Wind%20tech%20Dogget%2023%20Jul%202012.pdf

Probabilistic forecasts facilitate dynamically changing reserve targets107,108,109 and have spurred the development of stochastic scheduling algorithms where all the forecasts can be used to schedule the system in an optimal manner.110,111 The relationship between the modeled stochastic characteristics of an underlying wind resource, as represented by scenario trees, can have important implications on how units are scheduled.112 This further emphasizes the need for high quality data. Stochastic unit commitment with correct probabilistic forecast input data can actually negate the need for explicit reserve constraints.113,114 While probabilistic methods applied to unit commitment can be shown in a study environment to give better performance (i.e. cost and/or reliability) than deterministic methods115, there is significant translational work required before they can be deployed operationally in control rooms.116 It is also possible that they may not be deployed directly, but rather that the methods will be used to develop “smarter” deterministic criteria, or “rules of thumb” that capture most of the benefits and are more easily translatable into operable actions as discussed above.

An NREL study on the Eastern Interconnection with increased levels of wind penetration showed that the Wilmar stochastic unit commitment tool brought significant benefits.117

106 “ERCOT Using New Forecasting Tool to Prepare for Wind Variability” News release, March 2010 http://www.ercot.com/news/press_releases/show/326

107 Doherty, R. and O’Malley, M.J. (2005). A New approach to quantify reserve demand in systems with significant installed wind capacity, IEEE Transactions on Power Systems, Vol. 20, pp. 587 -595.

108 da Silva, A.M.L.L.; Sales, W.S.; da Fonseca Manso, L.A.; Billinton, R.; , "Long-Term Probabilistic Evaluation of Operating Reserve Requirements With Renewable Sources," Power Systems, IEEE Transactions on , vol.25, no.1, pp.106-116, Feb. 2010 doi: 10.1109/TPWRS.2009.2036706 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5374085&isnumber=5395745

109 Papavasiliou, A.; Oren, S.S.; O'Neill, R.P.; , "Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework," Power Systems, IEEE Transactions on , vol.26, no.4, pp.2197-2206, Nov. 2011

110 Meibom, P.; Barth, R.; Hasche, B.; Brand, H.; Weber, C.; O'Malley, M.; , "Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland," Power Systems, IEEE Transactions on , vol.26, no.3, pp.1367-1379, Aug. 2011 doi: 10.1109/TPWRS.2010.2070848 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5587912&isnumber=5958775

111 Costa, L.M.; Juban, J.; Bourry, F.; Kariniotakis, G.; , "A Spot-Risk-Based Approach for Addressing Problems of Decision-Making under Uncertainty,"Probabilistic Methods Applied to Power Systems, 2008. PMAPS '08. Proceedings of the 10th International Conference on , vol., no., pp.1-9, 25-29 May 2008 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4912609&isnumber=4912596

112 Lowery, C. and O’Malley, M.J. “Impact of wind forecast error statistics upon unit commitment", IEEE Transactions on Sustainable Energy, in press, 2012.

113 Stuart, A and G. Strbac, "Efficient Stochastic Scheduling for Simulation of Wind-Integrated Power Systems", IEEE Trans. Power Syst., 27(1), pp. 323 - 334, 2012.

114 J. Wang, A. Botterud, R. Bessa, H. Keko, L. Carvalho, D. Issicaba, J. Sumaili, V. Miranda, Wind power forecasting uncertainty and unit commitment, Applied Energy, Volume 88, Issue 11, November 2011, Pages 4014-4023, ISSN 0306-2619, 10.1016/j.apenergy.2011.04.011. http://www.sciencedirect.com/science/article/pii/S0306261911002339

115 Tuohy, A.; Meibom, P.; Denny, E.; O'Malley, M.; , "Benefits of Stochastic Scheduling for Power Systems with Significant Installed Wind Power,"Probabilistic Methods Applied to Power Systems, 2008. PMAPS '08. Proceedings of the 10th International Conference on , vol., no., pp.1-7, 25-29 May 2008 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4912607&isnumber=4912596

116 ANEMOS.plus, (2011a). "The State of the Art in Short-Term Prediction of Wind Power A Literature Overview", 2nd Edition, Deliverable 1.2 http://www.anemos-plus.eu/images/pubs/deliverables/aplus.deliverable_d1.2.stp_sota_v1.1.pdf

117 Meibom, P.; Barth, R.; Hasche, B.; Brand, H.; Weber, C.; O'Malley, M.; , "Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland," Power Systems, IEEE Transactions on , vol.26, no.3, pp.1367-1379, Aug. 2011 doi: 10.1109/TPWRS.2010.2070848 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5587912&isnumber=5958775


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http://www.anemos-plus.eu/images/pubs/deliverables/aplus.deliverable_d1.2.stp_sota_v1.1.pdf


However the computational issues were significant due to the size of the system and more frequent commitment rather than a statistical approach was more practical and beneficial.118

Some of the commitment issues will change if high penetrations of VG are accompanied by flexible generation that replaces retiring base load units, as anticipated in the NERC 2012 Long Term Reliability Assessment.119 In the extreme, one could imagine a large fleet of reciprocating engines and/or aero derivative gas turbines with fast start-up times and efficient ramping and cycling. In such a system, unit commitment and reserves scheduling will likely be quite different than it is today.

In addition to fundamental methodological work on reserves and stochastic unit commitment, there are significant practical and human elements involved. Interpretation of the outputs of probabilistic methods is very challenging and visualization techniques may be an important tool for addressing this.120 Whatever method is used to calculate a probabilistic reserve level, a specific set of operator actions are required and translating from a stochastic commitment algorithm to an actionable commitment plan may not be straightforward. Markets can help with this if they are properly designed and function well by increasing system operator access to the full flexible capability of all resources. Discretion with regard to a specific operating action is also a function of human risk aversion. Overcommitting generation may result in an economic penalty to some units and to the power system as a whole; however, under scheduling may result in insufficient generation and lost load. Faced with this choice and limited knowledge regarding the relative risks, a rational system operator will likely err on the side of overscheduling. The human element (e.g. the risk profile of the operators and how they are presented with the information) is probably different in an operations situation than in a planning context.

118 E. Ela; M. Milligan; P. Meibom; R. Barth; A. Tuohy; "Advanced Unit Commitment Strategies for the U.S. Eastern Interconnection" 9th Annual International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants, Québec, Canada October 2010

119 NERC, “2012 Long-Term Reliability Assessment”, http://www.seia.org/sites/default/files/resources/2012 LTRA_FINAL.pdf, November 2012 120 Sun, Y.; Overbye, T.J.; , "Visualizations for power system contingency analysis data," Power Systems, IEEE Transactions on , vol.19, no.4, pp.

1859- 1866, Nov. 2004, doi: 10.1109/TPWRS.2004.836193 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1350824&isnumber=29700


http://www.seia.org/sites/default/files/resources/2012


Chapter 4: Conclusions and Recommendations Conclusions

Most activity in probabilistic methods for integrating VG in power systems is still in the research domain. The probabilistic tools and techniques being developed by the research community have not yet been widely adopted by industry. This is consistent with the recent findings of a CIGRE study VG121 and probabilistic methods more generally.122

It should be obvious, however, that this is an area of very active and vibrant research. Many power system planning and operating problems are implicitly probabilistic, and while deterministic assumptions and approximations have served us reasonably well in the past, it is very likely that probabilistic methods will be required to ensure more optimal and effective solutions in the future. The growing penetrations of variable generation, with the variability and uncertainty that are an implicit characteristic of the wind and sun that fuels such generation, will further serve to make probabilistic methods useful and valuable.

To be sure, there are many challenges to be addressed for the widespread deployment of probabilistic methods. Research scale tools and models are being developed and deployed on small representative test systems, but despite cheaper computation platforms, they are not yet demonstrated to be practical for the full detail of real systems. There is an understandable reluctance on the part of industry to adopt probabilistic methods, that initially appear to be very complex and difficult to understand, and there may be a “skills gap” within industry and more broadly. The benefits of probabilistic methods may not be fully understood and appreciated. Many of the traditional deterministic methods are still fit for their purpose in many ways, but with more VG they may become increasingly inefficient. Probabilistic methods require significant amounts of data that may not exist or are difficult and expensive to acquire.

Despite the challenges, the potential benefits and the changing nature of the power system will move us down the path to probabilistic methods for some applications. All generators, transmission lines and distribution lines have probabilistic characteristics, but in the past, deterministic methods or simple rules-of-thumb have seemed to be adequate since we often viewed their characteristics primarily as a contingency concern (either it works or it doesn’t). With a future that will increasingly be dominated by variable generation, distributed generation, demand response and other elements of implicit variability and uncertainty, we will need to exploit the benefits of increasingly sophisticated probabilistic methods to ensure that our simplifying assumptions are still reasonable in the near term, and to directly help us plan and operate the power system in the longer term.

121 CIGRE 2012; Technical Brochure on Coping with Limits for Very High Penetrations of Renewable Energy, Joint Working Group C1/C2/C6.18 of Study Committee C6, August 2012, International Conference on Large High Voltage Electric Systems



1

Recommendations

Recommendations from the six classes of decision problems associated with VG:

• Reserves: the level of reserves needed for VG integration is broadly understood; however, specific methods have not been rigorously tested and compared. The application of stochastic methods appears promising, but needs validation and acceptance if they are to be successfully used.

• Dispatch: there is emerging work on probabilistic methods to dispatch conventional generation to minimize cost and maintain reliability in the presence of high levels of VG; however, there is a gap between academic work and practice as a result of more limited experience with high levels of VG.

• Commitment: there has been considerable interest in stochastic unit commitment. Methods and approaches vary, and most of this work resides in the academic/research domain. Collaborations to evolve and move these methods into actual practice, when appropriate, are needed.

• Maintenance: maintenance scheduling can be made more difficult in market regions where there is limited central authority to coordinate scheduled maintenance, and high levels of VG will likely have a significant influence. Methods to quantify the risk of both capacity shortfalls and flexibility shortfalls can be developed and tested in industry.

• Generation planning: there have been new methods developed to begin quantifying flexibility needs and risks of insufficient flexibility within generation expansion planning algorithms, so that results balance investment in VG with investments in technologies providing the flexibility that VG requires. There is room for improvement in these methods, and a need to evolve and move these approaches from the research community to industry practice.

• Transmission planning: there is evidence that stochastic approaches to transmission planning may yield more robust solutions; combining such methods with co-optimization of generation and planning decisions is particularly appealing, but computationally challenging. However, these methods are new/emerging, and additional research and deployment of such methods may be useful.

Other more general recommendations include:

• Grow appreciation for the ways in which the future power system will change. Variable generation is a catalyst for this change in viewpoint, but it is certainly not the only driver toward a different future system that challenges some of our traditional assumptions and practices.

• Work to develop and demonstrate efficient probabilistic techniques and solutions that are capable of addressing full-scale industry problems.

• Improve the understanding of probabilistic methods within the industry. The research community needs to work more closely with the industry to clearly demonstrate the benefits of probabilistic methods. The industry needs to clearly communicate shortcomings in deterministic methods and areas that probabilistic methods can be most fruitful. Cooperative efforts of the research and industry communities are needed to overcome the challenges in terms of both understanding and demonstration.


• More and better data are needed to allow the research and demonstrations to be meaningful and realistic – from real power systems, from VG resources and for future VG deployment scenarios. Collecting and maintaining large data sets can be expensive; however, stochastic methods generally require data that may not currently be readily available. We encourage industry discussion of the trade-offs and costs vs. benefits of collecting data that could help inform stochastic methods and the more rigorous assessments of various risks associated with power system planning and operations with high levels of VG.

• Because we envision a significant increase in the development and application of stochastic methods to help analyze the impacts of VG, we recommend that NERC perform a bi-annual assessment of development in this area. The IVGTF would be an appropriate home for this work.


3

1 Load loss due to the response of voltage sensitive load and load that is disconnected from the system by end-user equipment is not included.

ALR 1-4 BPS Transmission Related Events Resulting in Loss of Load

Metric Number ALR1-4 Submittal Date February 27, 2009, revised November 12, 2013 Sponsor Group (OC, PC or subgroup name)

PAS

Short Title Transmission related events resulting in loss of load

Metric Description • Number of transmission related events resulting in loss of load • Duration and loss of firm load involved in ALR1-4 events

Purpose To track BPS transmission related events which resulted in firm Load Loss1. This will allow planners and operators to validate their design and operating criteria assuring acceptable performance of the system

How will it be suited to indicate performance?

The relative number within any given BA, Reliability Organization, Planning Authority, or Interconnection will be assessed to establish a trend of Transmission related events.

Formula

• Number of events in a year. “Event” is an unplanned disturbance that produces an abnormal system condition due to equipment failures/system operational actions (either intentional or unintentional) that result in the loss of firm system demands for more than 15 minutes, utilizing the subset of data provided in accordance with EOP-004-2: Event Reporting EOP-004-2 as described below:

1. Loss of firm load for 15 minutes or more: a. 300 MW or more for entities with previous year’s demand of 3,000 MW or more. b. 200 MW or more for all other entities.

2. BES Emergency requiring manual firm load shedding of 100 MW or more. 3. BES Emergency resulting in automatic firm load shedding of 100 MW or more (via automatic

undervoltage or underfrequency load shedding schemes, or SPS/RAS). 4. Transmission loss event with an unexpected loss within an entities’ area, contrary to design,

of three or more BES Elements caused by a common disturbance (excluding successful automatic reclosing) resulting in a firm load loss of 50 MW or more.

• Duration in Hour • Loss of firm load (MW)

Time Horizon Historical and current year perspective Metric Start Time or Baseline 2002, or whenever data first became available Data Collection Interval and Roll Up

NERC Standard EOP-004 and OE-417 requires reporting of the data. Applicable Event Analysis reports will also be included.

Ease of Collection Data is available; may require some adjustments to accommodate all the different groups for measurement.

Aggregation BA, Reliability Organization, Planning Authority, or Interconnection Linkage to NERC Standard NERC Standard EOP-004 and OE-417

Linkage to Data Source NERC data base Need for Validation or Pilot

Need to validate completeness and consistency of reporting by entities

Data Submitting Entity

SMART Rating Total Score

Specific/ Simple

Measurable Attainable Relevant Tangible/ Timely

15 3 3 3

3 3

Reporting Style (look and feel) Bar Chart or line chart Publications and Documentation (e.g., section of LTRA)

Annual state of reliability report and NERC reliability indicator dashboard

NERC | Report Title | Report Date 1 of 20

DRAFT SRI Enhancement NERC Performance Analysis Subcommittee February 21, 2014



Table of Contents Table of Contents ......................................................................................................................................................................... 2 Introduction ................................................................................................................................................................................. 3

OLD Severity Risk Index (SRIOLD) Graphical Depiction & Proposed Severity Risk Index (SRIbps) ........................................... 3 Recommendation for Enhancement of OLD SRI (SRIOLD) ..................................................................................................... 6

Generation, Transmission and Distribution-related Load Loss Segregation ................................................................................ 7 Bulk Power System SRI (SRIbps) Concept and Calculation .................................................................................................... 7 SRI Comparison for Specific Significant Days ....................................................................................................................... 8 SRIOLD and SRIbps : Comparative Charts ................................................................................................................................ 9

Conclusion of Analyses of SRIOLD and SRIbps and Recommendation........................................................................................... 11 APPENDIX A................................................................................................................................................................................ 12

SRIOLD and SRIbps : Descriptive Statistics and Paired t-test .................................................................................................. 12 SRIOLD: Time Trend, Seasonal and Annual Changes ............................................................................................................ 15 SRIbps : Time Trend, Seasonal and Annual Changes ........................................................................................................... 16

References ................................................................................................................................................................................. 20


Introduction In August 2010, the Reliability Metrics Working Group (RMWG) released its Integrated Bulk Power System Risk Assessment Concepts paper1 introducing new concepts, such as the “universe of risk” of the bulk power system. In the concepts paper, a method to assess “event-driven” risks was introduced and the Severity Risk Index (SRI) was established to quantify the impact of various events of the bulk power system. As methods were analyzed, the SRI became a foundational attempt to quantify the performance of the bulk power system on a daily basis. It was designed to provide comparative context for evaluating current and historical performance of the system. The concept of quantifying “events” was not viable since there is no mechanism to aggregate operational data in a manner consistent with the concept of an event. However, it was determined that the daily measure of performance of the system was a good surrogate, still allowing for investigation, identification of ranges of performance and other measures important to gauge the current and historic bulk power system reliability. The concepts paper and SRI refinement calculation were endorsed by NERC’s Operating Committee (OC) and Planning Committee (PC) in September 2010. Subsequently, a companion whitepaper2 Integrated Risk Assessment Approach – Refinement to Severity Risk Index [Note: Move footnote 3. here.] was developed and approved by OC and PC in March 2011. The NERC Performance Analysis Subcommittee (PAS) (the successor to the RMWG) has continued this analysis following the release of the State of Reliability Report 20133. At their April 2013 meetings, the Operating Committee (OC) and Planning Committee (PC) approved the 2013 State of Reliability Report and provided recommendations to enhance the Severity Risk Index (SRI). This Report builds on previous work of the RMWG and the PAS and presents methods to address the recommendations and enhancements suggested by the OC and the PC.

OLD Severity Risk Index (SRIOLD) Graphical Depiction & Proposed Severity Risk Index (SRIbps) As defined in the Integrated Risk Assessment Approach – Refinement to Severity Risk Index whitepaper2, the SRIOLD is a daily blended metric where transmission loss, generation loss, and load loss events are aggregated into a single value that measures performance of the system. Each element (transmission, generation, and load loss) is weighted by the inventory for that element to rate each day’s performance and determine significant days for appropriate performance analysis. SRIOLD values range from zero (a theoretical condition in which virtually no elements out of service) to 1,000 (a theoretical condition in which every transmission line, all generation units and all load lost (for more than 12 hours) across the system in a single day). The SRIOLD was designed to be fungible and usable for the entirety of NERC as well as applied more granularly, such as at a reliability coordinator level. Figure 1 captures the daily severity risk index value based on calculation from 2008 to 2012 including the historic significant events used to pilot the calculation. On a yearly basis, these daily performance measurements are sorted in descending order to evaluate the year-on-year performance of the system. Since there is significant disparity between daily values calculated, the curve is depicted using a logarithmic scale. Table 1 lists the top 10 SRIOLD days for 2012, with the triggering event recorded for the day. The Performance Analysis Subcommittee, in its previous State of Reliability Reports, has used this graphic to provide a quantitative graph that supports the assessment of the daily performance of the bulk power system, which has apparently been beneficial. It displays the full range of the performance for each day of the year, in a descending impact order, and allows for year on year comparisons of performance. Since it is sorted in a descending impact order, the left side of the chart generally displays the performance of the days that were most taxing to the bulk power system, while the right side of the chart displays those days which had limited impacts across the bulk power system. Finally, the central, more linear section of the chart, displays the “normal day” impacts within the bulk power system. This graphic has been updated to reflect the proposed changes in calculating SRIbps and is shown in Figure 2. Table 2 lists the top 10 SRIbps days for 2012, with the triggering events recorded for each day.

1 http://www.nerc.com/docs/pc/rmwg/Integrated_Bulk_Power_System_Risk_Assessment_Concepts_Final.pdf 2 http://www.nerc.com/docs/pc/rmwg/SRI_Equation_Refinement_May6_2011.pdf 3 State of Reliability Report 2013, http://www.nerc.com/pa/RAPA/PA/Performance%20Analysis%20DL/2013_SOR_May%2015.pdf


http://www.nerc.com/docs/pc/rmwg/Integrated_Bulk_Power_System_Risk_Assessment_Concepts_Final.pdf

http://www.nerc.com/docs/pc/rmwg/SRI_Equation_Refinement_May6_2011.pdf

http://www.nerc.com/pa/RAPA/PA/Performance%20Analysis%20DL/2013_SOR_May%2015.pdf

Figure 1: NERC Annual Daily Severity Risk Index (SRIOLD) Sorted Descending

Table 1: 2012 NERC Top 10 SRIOLD Days

Date NERC SRI & Components

Weather- Influenced?

Cause Description Interconnection

SRI Generation Transmission Load Loss

Oct 29 27.89 1.95 1.78 24.16 Hurricane Sandy Eastern

Jun 29 19.94 2.49 1.37 16.08 Thunderstorm Derecho Eastern

Oct 30 6.63 2.76 3.35 0.51 Hurricane Sandy Eastern


Aug 28 4.21 1.65 0.32 2.23 Hurricane Isaac Eastern

Jul 18 4.07 1.90 1.60 0.57 Severe Thunderstorm Eastern

May 29 3.55 1.83 1.36 0.36 Severe Thunderstorm Eastern

Mar 2 3.51 0.98 1.54 0.99 Severe Weather Tornadoes Eastern


Aug 29 3.35 1.28 1.40 0.66 Hurricane Isaac Eastern


Figure 2: NERC Annual Daily Severity Risk Index (SRIbps) Sorted Descending

Table 2: 2012 NERC Top 10 SRIbps Days

Date NERC SRI & Components Weather-

Influenced? Cause

Description Interconnection SRI Generation Transmission Load

Loss


Oct 30 7.15 2.91 3.35 0.90 Hurricane Sandy Eastern Oct 29 7.04 2.05 1.78 3.21 Hurricane Sandy Eastern




May 29 3.38 1.92 1.36 0.09 Severe Weather Eastern

Dec 20 3.37 1.60 0.67 1.10 Winter Storm Eastern

Aug 8 3.34 1.15 2.13 0.06

Extreme

Heat/thunderstorm

Western/Eastern

Dec 21 3.32 1.54 1.23 0.55 Winter Storm Western


Recommendation for Enhancement of OLD SRI (SRIOLD) The SRIOLD was originally designed to measure the impact of all system events that result in loss of transmission lines, generating units, or load loss regardless of whether the load loss was a result of loss of supply at the transmission level or generation level or was a direct result of outages at the distribution level. One of the recommendations for enhancing the SRI was to revise it so that it better represents events resulting in load loss as the result of a loss of supply from the transmission or generation facilities that make up the bulk power system, rather than the loss of distribution system components. This focuses our attention on those events that directly apply to the performance of the transmission system and generation resources to which NERC’s reliability objectives apply. Based on this recommendation, PAS is proposing an enhancement to the OLD SRI (SRIOLD) which is referred to in this document as bulk power system SRI (SRIbps). The SRIbps refers to the SRI where the load loss component of the daily SRI is more indicative of transmission or generation related events which result in loss of service to distribution customers. All other components of SRIbps would remain the same except for the load represented in the load loss component. This places the emphasis on load loss events that were caused by bulk power system facilities.


Generation, Transmission and Distribution-related Load Loss Segregation One of the recommendations which PAS has evaluated is the method by which load loss is calculated and integrated into the SRI results. Based upon early work done by PAS, the OC and PC suggested that load loss events be weighted. Methods to determine the magnitude of load loss events were limited. Initially PAS collected load loss values from records maintained by the Office of Electricity Delivery & Energy Reliability of the U.S Department of Energy via Form OE-417.4 However, load loss values were available only on notable days, specifically those days in which a reporting threshold was exceeded. A further complication is that load loss data from Form OE-417 does not differentiate load loss due to generation, transmission or distribution sources. Using this data for load loss potentially overestimates the impact of bulk power system performance on the days which meet the filing criteria of Form OE-417. Finally, the manner in which the reported load loss values were used did not reflect whether the day’s results were predominantly caused by weather or other external forces. Since the purpose of SRI is to indicate the performance of bulk power system, distribution impacts should be excluded from this calculation. Since 2011, the IEEE Distribution Reliability Working Group (DRWG) Benchmark Study has provided the industry with data to evaluate the performance of power delivery to distribution customers, as measured by industry reliability metrics. Analysis of the data collected in this study demonstrated that the load loss on many of these reportable days was predominantly the result of significant distribution outages, thus corroborating the need to modify the means for calculating the load loss component. The benchmark data collected by the IEEE Distribution Reliability Working Group is a better source of load loss data for the SRI than the load loss data extracted from Form OE-417 reports, since it captures the effects from both distribution outages as well as those created upstream of the distribution system. Further, it contains data for every day of the year, not just those days in which a reporting threshold was exceeded. It is expected that in the future analysis can be performed using the IEEE standard definition of a major event5, evaluating its relevance for the bulk power system. This could address the concern expressed by the OC and PC regarding the effect of external influences or other occurrences, such as weather.

Bulk Power System SRI (SRIbps) Concept and Calculation SRIbps is a metric in which the load loss component only captures load loss resulting from transmission or generation sources, notably the bulk power system. All other components of SRIbps are the same as the previously published and discussed SRIOLD. SRIbps does not take in account load lost due to distribution system sources. Therefore this approach corrects the effects that arise from use of Form OE-417 data, as discussed above. Based on data from the IEEE DRWG Benchmark Study6, various approaches to distinguish load loss due to generation or transmission6 have been used to calculate SRIbps. This study is performed annually and is believed to encompass the largest dataset across North America using industry-standard reliability metrics. It currently comprises almost one hundred million customers and has been conducted annually since 2003. The Distribution Reliability Working Group, which sponsors this study, has approved that the data can be supplied anonymously for calculations as are proposed here. Modifications to the submittal calendar are expected to align with the production of the State of Reliability Report.

Bulk Power System SRI (SRIbps) Equation

4 http://energy.gov/oe/information-center/reporting/electric-disturbance-events-oe-417 The Electric Emergency Incident and Disturbance Report (Form OE-417) collects information on electric incidents and emergencies. The Department of Energy uses the information to fulfill its overall national security and other energy emergency management responsibilities, as well as for analytical purposes. 5 IEEE 1366-2012 IEEE Guide for Electric Power Distribution Reliability Indices establishes methods for determining that a major event has occurred using industry standard reliability metrics. 6 The IEEE DRWG Annual Benchmark Study does not explicitly distinguish between customer interruptions that are the result of generation or transmission; rather it identifies source outages that cause distribution customer interruptions. Appendix B. IEEE Reliability Benchmark Data, http://grouper.ieee.org/groups/td/dist/sd/doc/ http://www.smartgrid.gov/sites/default/files/doc/files/Distribution%20Reliability%20Report%20-%20Final.pdf


http://energy.gov/oe/information-center/reporting/electric-disturbance-events-oe-417

http://grouper.ieee.org/groups/td/dist/sd/doc/

http://www.smartgrid.gov/sites/default/files/doc/files/Distribution%20Reliability%20Report%20-%20Final.pdf

The PAS proposes the following method to calculate the load loss due to transmission or generation sources; calculation changes from SRIOLD to SRIbps are indicated with highlights:

SRIbps = [(RPL) × wL × (MW

L) + w

T × (N

T) + w

G × (N

G)] X 1000

Where, SRIbps = Severity Risk Index for specified event (assumed to span one day), wL = 60% , weighting of load loss, MWL = normalized MW of bpsL in percent,

𝑏𝑏𝑏𝑏𝑏𝑏𝐿𝐿 = �𝑀𝑀𝑀𝑀𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝐶𝐶/𝐷𝐷 � × (𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏)

Where,

𝑏𝑏𝑏𝑏𝑏𝑏𝐿𝐿 = load loss due to transmission or generation sources (MW) for the day 𝑀𝑀𝑀𝑀𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = daily peak load (MW) is aggregated at NERC level obtained from FERC 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝐶𝐶/𝐷𝐷 = Total Customer served for the day obtained from IEEE benchmark data6 𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏 = Customers Interrupted due to transmission or generation sources for the day obtained from IEEE benchmark data

wT = 30% - weighting of transmission lines lost, NT = normalized number of transmission lines lost in percent obtained from TADS7 reports wG = 10% - weighting of generators lost, NG = normalized number of generators lost in percent obtained from GADS8 reports RPL = load Restoration Promptness Level:

RPL = 1/4, if TCAIDI < 50, RPL = 2/4, if 50 <= TCAIDI < 100, RPL = 3/4, if 100 <= TCAIDI < 200, RPL = 4/4, if TCAIDI >=200. TCAIDI = Transmission (or Generation Source) Customer Average Interruption Duration (in minutes) obtained from IEEE benchmark data9

Additional information on formula and weighting factors above can be found at http://www.nerc.com/docs/pc/rmwg/SRI_Equation_Refinement_May6_2011.pdf

SRI Comparison for Specific Significant Days SRIOLD, while providing meaningful measurement of the daily performance of the bulk power system, created concern within certain audiences about the influence of the load loss events that were largely impacting the distribution system. The effect was confirmed after further analysis showed that the majority of reports filed under OE-417 were found to be associated with significant distribution system interruptions. The transition to SRIbps is intended as a corrective measure. In order to determine whether such modification leads to better datasets for industry analysis, noteworthy events, which comprised approximately 9 days, were selected as case studies for comparison and were chosen to test “boundary conditions” for bulk power system impacts. These included February 2-5, 2011, which occurred primarily in the Texas Reliability Entity (TRE), and was related to extreme and extended cold that resulted in generation plant interruptions, related

7 http://www.nerc.com/pa/RAPA/tads/Pages/default.aspx 8 http://www.nerc.com/pa/RAPA/gads/Pages/default.aspx 9 Appendix A. Reliability Indices, http://www.smartgrid.gov/sites/default/files/doc/files/Distribution%20Reliability%20Report%20-%20Final.pdf



http://www.nerc.com/pa/RAPA/tads/Pages/default.aspx

http://www.nerc.com/pa/RAPA/gads/Pages/default.aspx



to the extreme temperatures. Next, on September 8, 2011, the Western Electric Coordination Council (WECC) reliability region experienced a substantial transmission-related event during periods of elevated temperature. On June 29, 2012, a substantial linear convective weather system, called a Derecho10, passed through Illinois, Indiana, Ohio, continuing to the mid-Atlantic states, causing significant damage, largely to distribution system facilities. Finally, on October 29, 2012, Hurricane Sandy struck the eastern seaboard, with damage concentrated in New York and New Jersey and primarily affecting the local distribution systems; industry reports included October 30, 2012 impacts as well. Table 2 below compares SRI values with both approaches, notably the SRIOLD and SRIbps. For the June 29 and October 29, 2012 events which were primarily caused by extreme weather and resulted in distribution system damage, as expected, the performance index as measured by the SRIbps score has been reduced. The opposite effect is experienced on February 2 and September 8, 2011 events, since the load losses were the result of bulk power system performance, the SRIbps thus SRIbps is higher than the SRIOLD. The intent of the SRI is to measure the severity of the impact of load loss due to bulk power system elements on the grid. As a result, it is believed this refinement to the SRI approach provides a more meaningful measure of the performance of the bulk power system, and delivers additional emphasis on the proper days.

Table 3: SRI comparison for selected significant days.

Date SRIOLD SRIbps

2/2/2011 – Cold Weather Event 10.3 10.8

9/8/2011 – Southwest Blackout 8.7 14.0

6/29/2012 – Thunder Storm Derecho 20.1 8.9

10/29/2012 – Hurricane Sandy 28.0 7.0

SRIOLD and SRIbps : Comparative Charts

This section shows charts and tables that illustrate the differences between the SRIOLD and the proposed SRIbps observed on days with extreme SRI values. Figures 3 and 4 show the values of both SRIs for 70 days with the highest SRIOLD and the highest SRIbps, respectively across the 5 year history, from 2008-2012. The charts below reflect the fact that the majority of days do not have load loss values, but solely reflect transmission and generation outages that result in a score for the day. Then, when load loss events have been captured, they are generally inflated substantially by the impact of distribution loss of load events.

Figure 3: SRIOLD vs. SRIbps for 70 days with the highest SRIOLD values (2008-2012)

The deviation of the blue line from the red line in figure 3 shows that the worst regular SRI scores were not heavily influenced by transmission or generation load loss events

10 http://www.spc.noaa.gov/misc/AbtDerechos/derechofacts.htm By definition, if the swath of wind damage extends for more than 240 miles (about 400 kilometers), includes wind gusts of at least 58 mph (93 km/h) along most of its length, and several, well-separated 75 mph (121 km/h) or greater gusts, then the event may be classified as a derecho.


http://www.spc.noaa.gov/misc/AbtDerechos/derechofacts.htm

Figure 4: SRIOLD vs. SRIbps for 70 days with the biggest SRIbps values (2008-2012)

The deviation of the red line from the blue line in figure 4 shows that that only a few days (when the red line does not depart from the blue line) are the result of transmission or generation load loss events. This lack of correlation is the primary driver for the refinement to the load loss data collection process. Table 4 lists ten dates with the largest values of SRIOLD and SRIbps. There are seven common entries in these lists; however, the order of the dates differs. The events which are highly influenced by the load loss due to distribution sources ranks higher in the list of SRIOLD. Whereas, events which are highly influenced by the load loss due to transmission or generation sources ranks higher in the list of bulk power system SRI (SRIbps). In particular, the day with the largest SRIOLD for the five years, October 29, 2012 (Hurricane Sandy), ranks fifth with respect to SRIbps; conversely, the day with the largest SRIbps for the five years, September 8, 2011 (Southwest Blackout Event), ranks only eighth with respect to SRIOLD.

Table 4: Top 10 days for both approaches


Conclusion of Analyses of SRIOLD and SRIbps and Recommendation Analyses of five years of data using SRIOLD and SRIbps, pairwise comparisons of values and assessment of top impacting days (for 2012 and for the five year history) were performed to determine the impact of modifying the method of incorporating load loss data into the severity risk score. Additional analysis was performed using a variety of statistical tests, as outlined in Appendix A. The results of these tests demonstrate that statistical results of SRIOLD are materially similar to the statistical results of SRIbps. Thus, no bias is being introduced statistically as a result of the proposed change. However, other statistical results demonstrate the benefit of transitioning from SRIOLD to SRIbps, notably the Delta comparison discussed in Appendix A highlights this benefit. Days that were predominantly weather influenced, and generally impacted the distribution system are de-emphasized with SRIbps. Days that were predominantly bulk power system events, especially those that related to load loss within the bulk power system are highlighted with the modified calculation. Additionally, incorporating load loss events on days where no event reports had occurred previously allows for a more comprehensive dataset for the industry to evaluate. It is recommended that the industry modify the calculation method to incorporate the effect of bulk power system load loss events by transitioning from SRIOLD to SRIbps. It is further recommended this change be effective in the 2014 State of Reliability Report.


APPENDIX A Statistical Analyses Conducted In this appendix, statistical properties of these variables are investigated and statistically significant trends are described. Both indices, SRIOLD and SRIBPS, indicate consistency in the performance of the bulk power system, as measured by the severity risk index over the 5-year period. Further, the analysis confirms statistically significant difference between SRIOLD and SRIBPS: SRIOLD is on average smaller than SRIBPS. Results of the significant changes by year and by season are obtained for SRIOLD and SRIBPS with some similarities and some differences between them. For example, for both indices, summer values are statistically significantly greater than any other season; and winter is greater than fall. However, only for SRIBPS, spring values are statistically significantly greater in fall. Test on Homogeneity of variance shows essentially similar dispersion (variance) of SRIOLD and SRIBPS.

SRIOLD and SRIbps : Descriptive Statistics and Paired t-test The 2008-2012 SRIOLD dataset contains 1827 daily observations. The basic descriptive statistics are shown in Table 5 and the histogram is shown in Figure 5.

Table 5: Basic descriptive statistics SRIOLD

Figure 5: Histogram of SRIOLD

N Mean Std Dev Median Minimum Maximum Sum1827 1.646 1.138 1.462 0.347 27.994 3007.350

SRIOLD


The 2008-2012 SRIbps dataset also contains 1827 daily observations. The basic descriptive statistics are shown in Table 6 and the histogram is shown in Figure 6.

Table 6: Basic descriptive statistics of SRIbps

Figure 6: Histogram of SRIbps

Next, the statistical analysis identified significant differences between SRIOLD and SRIbps. Because these two variables are calculated for the same set of days and are significantly correlated (the correlation 0.755 with p-value <0.0001), we apply a paired t-test on the difference in daily observations. The null hypothesis about the equality of expected values must be rejected (p=0.0026) and the alternative hypothesis that the expected old SRI is smaller than the expected SRIbps is accepted. For a statistical comparison of the variances of SRIOLD and SRIbps, Brow and Forsythe’s test for Homogeneity of variances is applied. It results in the conclusion that we cannot reject the null hypothesis on the equality of the variances of the distributions of old SRI and SRIBPS. SRIbps. This analysis, if conducted as the sole means of comparing SRIOLD to SRIbps, would lead to the conclusion that on a given day, we can expect a smaller value of SRIOLD than SRIbps. (the expected value of SRIOLD is smaller than the expected value of SRIbps). Since this result didn’t fully support the expected results, additional analysis was undertaken, where the 1827 occurrences were separated into those days which had load loss events previously as reported through OE-417 (334 days of the 1827, or 18%) versus those which had no load loss data (1493 days of the 1827, or 82. The goal of this test is to investigate whether the relationship between the indices is similar on days without loss of load (we name them days of Type 1) and on days with loss of load used for the SRIOLD calculations (days of Type 2). First, we define a new variable, Delta, equal to the difference of SRIOLD than SRIbps. The previous test confirmed that the expected value of Delta is negative (with p-value 0.0026). Next we considered the distribution of Delta separately for Type 1 and Type 2 days; the results are shown in Table 7 and the histogram showing Delta for Type 1 and Type 2 is shown in Figure 7.

N Mean Std Dev Median Minimum Maximum Sum1827 1.699 0.762 0.479 1.593 13.971 3104.460

SRIBPS


Figure 7: Distribution of Delta

Table 7: Sample Statistics for Delta

The statistics reveal the noteworthy difference in Delta for days of different types. On days of Type 1 (without loss of load) SRIOLD is on average smaller than SRIbps;; on days of Type 2 (with loss of load) SRIOLD is on average greater than SRIbps. Series of ANOVA tests resulted in the following conclusions:

1. There is a highly significant difference in distribution of Delta on days of Type 1 and days of Type 2 (i.e. the shapes of the histograms are different);

2. Hypothesis on the homogeneity of the variances of Delta for these subsets has to be rejected (i.e. the spread of values of Delta is statistically significantly greater on days of Type 2);

3. Hypothesis of the equality of the expected values of Delta for these subsets has to be rejected (i.e. one expects to have greater difference between SRIOLD than SRIbps on days with loss of load than on days without loss of load);

4. T-test on the sign of Delta on days of Type 1 results in rejection of the null hypothesis on the zero expected value of Delta (i.e. we accept the alternative hypothesis that on days without loss of load the expected SRIOLD is smaller than the expected SRIbps:

5. T-test on the sign of Delta on days of Type 2 results in rejection of the null hypothesis on the zero expected value of Delta (i.e. we accept the alternative hypothesis that on days with loss of load the expected SRIOLD is greater than the expected SRIbps

Mean Std. Dev1 1493 -0.15 0.152 334 0.36 1.67

DeltaType of Day N


In summary, while SRIbps would lead to a slightly higher value on any given day (or about 80% of the calculated values), for a day where an SRIOLD incorporated an OE-417 report, or for the corresponding 20% of calculated values, the resulting value would be substantially lower. These tests demonstrate precisely the importance of migrating from SRIOLD to SRIbps.

SRIOLD: Time Trend, Seasonal and Annual Changes This section presents the SRIOLD time trend by running the correlation analysis and the linear regression that relates time and the SRI value. A positive (negative) slope of the linear regression line, or time trend line, indicates that, on average, the SRIOLD

values increase (decrease) in time. Additionally, the test on significance of the regression (or, equivalently, significance of the correlation between SRIOLD and the time variable) detects whether the positive (negative) slope has been observed by chance or its value is statistically significant and, therefore, points out to a declining (improving) performance, as measured by the index. Scatter plot and the linear regression line are shown in Figure 8.

Figure 8: Scatter plot and the linear regression line for SRIOLD

The linear regression line has a small positive slope (0.00002525); however, the regression is not statistically significant (p=0.6169). Equivalently, the correlation analysis yields the positive correlation of 0.012 between time variable and SRIOLD, which is not statistically significant (i.e. the test on zero correlation fails to reject the null hypothesis with the same p-value 0.6169). Thus, we conclude that the positive slope of the trend line very likely occurred by chance and does not provide statistically significant evidence about declining performance of the system; in the other words, on average, the SRIOLD remained consistent from 2008 to 2012. Figure 9 reveals noticeable seasonal changes in the SRIOLD. They can be studied via time series analysis or by applying a straightforward ANOVA tests. We investigated the seasonal impact on SRI by running one-way ANOVA with a four-level variable for season11. The test results in a highly statistically significant dependence of the SRI on the season (the test on the equality of the excepted SRI for all seasons should be rejected with p<0.0001). The sample parameters by season are listed in Table 8.

11 For this study, seasons are defined as follows: Winter from December 1st to February 28th (or 29th); Spring from March 1st to May 31st; Summer from June 1st to August 31st; and Fall from September 1st to November 30th .

SRIOLD


Table 8: Sample parameters of SRIOLD by season

Fisher’s Least Square Difference test that compares all pairs of season results in the following conclusion: at the 5% significance level, Summer SRIOLD is greater than SRIOLD for any other season, and in Winter SRIOLD is greater than in fall. All other pairs have essentially similar expected SRIOLD value. Finally, the statistical analysis of the annual changes in the Old SRI was performed. The sample parameters by year are listed in Table 9.

Table 9: Sample parameters of SRIOLD by year

The one-way ANOVA test results in a highly statistically significant dependence of the SRIOLD on year (the test on the equality of the excepted Old SRI for the five years should be rejected with p<0.008). Finally, Fisher’s Least Square Difference test that compares all pairs of year’s results in the following conclusion: at the 5% significance level, in 2011 SRIOLD is smaller than SRI for 2009, 2010, 2012; in 2009 SRIOLD is smaller than SRIOLD in 2012. All other pairs of years have essentially similar expected SRIOLD value. SRIbps : Time Trend, Seasonal and Annual Changes This section presents the SRIbps time trend by running the correlation analysis and the linear regression that relates time and the SRIbps value. A positive (negative) slope of the linear regression line, or time trend line, indicates that, on average, the SRIbps values increase (decrease) in time. Additionally, the test on significance of the regression (or, equivalently, significance of the correlation between SRIbps and the time variable) detects whether the positive (negative) slope has been observed by chance or its value is statistically significant and, therefore, points out to a declining (improving) performance, as measured by the index. The scatter plot and the linear regression line are presented in Figure 9.

Mean Std DevWinter 452 1.604 0.878Spring 460 1.552 0.662Summer 460 1.974 1.182Fall 455 1.452 1.557

N Old SRISeason

Mean Std Dev2008 366 1.695 0.8262009 365 1.566 0.6552010 365 1.679 0.6742011 365 1.505 1.2682012 366 1.785 1.806

Year N Old SRI

SRIOLD

SRIOLD


Figure 9: Scatter plot and the linear regression line for SRIbps

The linear regression line has a small negative slope (-0.00006141); however, the regression is not statistically significant (p=0.069). Equivalently, the correlation analysis yields the negative correlation of -0.0425 between time variable and SRIbps, which is not statistically significant (i.e. the test on zero correlation fails to reject the null hypothesis with the same p-value 0.069). Thus, we conclude that the negative slope of the trend line very likely occurred by chance and does not provide statistically significant evidence about improving performance of the system; in the other words, on average, the SRIbps remained consistent from 2008 to 2012. Note that while the main conclusions about time trends of SRIbps and SRIbps are the same, the numerical results for the indices differ. First, slopes of their time trend lines have different signs, negative and positive, respectively. Even though these values are not statistically significant to indicate an improvement or a decline of the system, there is a difference in the p-values of the tests that led to this conclusion. P-value of 0.6169 means a high confidence in a horizontal time trend for SRIOLD, while p-value of 0.069 does not provide this confidence and is “almost” significant to reject the hypothesis on a horizontal time trend for SRIbps (if we have tested the hypothesis not at the significance level 5%, but at the significance level 6.9%, we would have to reject it). Figure 10 reveals noticeable seasonal changes in SRIbps. The box plot of the SRIbps observations by season12 is shown in Figure 10 (1 stands for winter, 2 for spring, 3 for Summer, and 4 for Fall values; a number next to an outlier is the day number in the five-year dataset).

12 For this study, seasons are defined as follows: Winter from December 1st to February 28th (or 29th); Spring from March 1st to May 31st; Summer from June 1st to August 31st; and Fall from September 1st to November 30th .

SRIBPS


Figure 10

We investigated the seasonal impact on SRIbps by running one-way ANOVA with a four-level variable for season. The test results in a highly statistically significant dependence of SRIbps on the season (the test on the equality of the excepted SRIbps for all seasons should be rejected with p<0.001). The sample parameters by season are listed in the Table 10.

Table 10: Sample parameters of SRIbps by season

Fisher’s Least Square Difference test that compares all pairs of seasons results in the following conclusion: at the 5% significance level, Summer SRIbps is greater than SRIbps for any other season, in Spring SRIbps is greater than in Fall, and in Winter SRIbps is greater than in Fall. All other pairs have essentially similar expected daily SRIbps value. To compare a dispersion (or spread) of the seasonal SRIbps , we apply Brown-Forsythe’s test for homogeneity of variances for its seasonal samples. At the 5% significance level, Fall SRIbps has a greater variance than Winter SRIbps, and Spring SRIbps has a smaller variance than both Summer and Winter SRIbps. Statistically significantly greater variance means a greater spread of values of the population. Seemingly surprising acceptance of the null hypothesis for the seasons with two extreme values of the sample standard deviation, Spring and Fall, can be explained by the “irregularity” (in particular, asymmetry and heavy tails) of these distributions. Note that our choice of Brown-Forsythe’s test for homogeneity of variances is also explained by its robustness under non-normality of distributions. Finally, the statistical analysis of the annual changes in SRIbps was performed. The sample parameters by year are listed in Table 11.

Table 11: Sample parameters of SRIbps by year

Mean Std DevWinter 452 1.669 0.810Spring 460 1.644 0.521Summer 460 2.017 0.714Fall 455 1.464 0.856

N SRI(BPS)SeasonSRIBPS


The one-way ANOVA test results in a highly statistically significant dependence of SRIbps on year (the test on the equality of the excepted SRIbps for the five years should be rejected with p<0.0001). Finally, Fisher’s Least Square Difference test that compares all pairs of years results in the following conclusion: at the 5% significance level, the 2011 SRIbps is smaller than SRIbps for any other year; the 2009 SRIbps is smaller than SRIbps in 2008 and 2012. All other pairs of years have essentially similar expected SRIbps value.

Mean Std Dev2008 366 1.801 0.6742009 365 1.664 0.5282010 365 1.742 0.6112011 365 1.504 1.0412012 366 1.785 0.813

N SRI(BPS)Year SRIBPS


References 1. IEEE 1366-2012 IEEE Guide for Electric Power Distribution Reliability Indices establishes methods for determining that a major

event has occurred using industry standard reliability metrics.http://www.nerc.com/docs/pc/rmwg/SRI_Equation_Refinement_May6_2011.pdf

2. The IEEE DRWG Annual Benchmark Study does not explicitly distinguish between customer interruptions that are the result of generation or transmission; rather it identifies source outages that cause distribution customer interruptions. Appendix B. IEEE Reliability Benchmark Data, http://www.smartgrid.gov/sites/default/files/doc/files/Distribution%20Reliability%20Report%20-%20Final.pdf

3. Appendix A. Reliability Indices, http://www.smartgrid.gov/sites/default/files/doc/files/Distribution%20Reliability%20Report%20-%20Final.pdf

4. Richard D. Christie. Statistical classification of Major event days in Distribution system reliability. IEEE Transactions on Power delivery, vol. 18, No 4, October 2003.

5. 1366™ IEEE Guide for Electric Power Distribution Reliability Indices. IEEE Power Engineering Society, 2004, 2012. 6. IEEE Benchmark Year 2013 Results for 2012 Data. Draft Results for Performance Analysis Subcommittee Risk Team

Discussion. August 1, 2013. 7. Douglas C. Montgomery and George C. Runger. Applied Statistics and Probability for Engineers. Fifth edition. Wiley,

2011. 8. W. van der Vaart. Asymptotic Statistics. Cambridge University Press, 1998.





Wind GADS White Paper

Introduction Commercial wind development began in the early 1980’s with small 15 – 45 kW turbines. Over the next 30 years turbine size has increased to as much as 3.6MW with the largest plant containing 338 turbines, for a total of 845MW. In 2012, wind generation provided 3.5% of U.S. generation up from 2.9% in 2011. The North American Electric Reliability Corporation (NERC) Generating Availability Data System (GADS) was developed for conventional generation in 1982. Data from this system has been used, with much success, for equipment reliability, availability and risk-informed analysis by the industry. It has also provided bench marking and bulk system reliability data. Mandatory reporting for conventional technologies began in 2012. In 2011 the first Wind GADS Data Reporting Instructions (DRI) were completed with voluntary reporting requirements.

Wind Data Reporting Instructions In 2007, several wind industry representatives approached NERC with the request to develop standards and a voluntary reporting system for Wind. A team of industry representatives was assembled to develop a DRI for wind. In April 2011 the first GADS Wind DRI was posted on the NERC WEB site with voluntary report requirements. The industry has had two years to evaluate this document and identify additional needs or clarifications. As a result NERC reconvened the Wind GADS Sub-Team in early 2013. Substantial enhancements were identified; derates, delays, reserve shutdown, plant boundaries clarified, addition equipment codes, moving tables and examples to the appendix, modification of equations, multiple overlapping outage examples, data quality guidelines, data reporting levels, roll-up methods, revised figures and document history. The first draft of the Wind GADS DRI V2.0 document was completed in December 2013. The draft still requires the Wind GADS sub-team final review, NERC Performance Analysis Sub-Committee (PAS) review, NERC Planning Committee (PC) review, a public comment period and a period to cycle back through to take into account revisions from above.

Mandatory Wind Data Collection In 2010, a Task Force was assembled to evaluate the need for mandatory reporting of performance and design data to GADS for bulk power reliability. The Wind Sub-Task Force team’s report recommended that mandatory reporting begin January 1, 2015 for plants greater than or equal to 75MW and a commissioning date of January 1, 2005 or later. Of the three reporting levels, the basic level would be mandatory. The size and commissioning date qualifications were due to the inability of older systems to comply with reporting without substantial costly system modifications. While thermal/hydro GADS reporting was made mandatory, the PC elected to defer mandatory reporting for wind for further evaluation. In February 2013, the Wind GADS Sub-Team revised its recommendation for mandatory to allow a phased in approach. Plants 200MW and greater 2016, 100MW and greater 2017 and 75MW and greater 2018 all with a commissioning date of January 1, 2005 or later would be subject of mandatory reporting. The phased approached was recommended, because there is no experience in wind reporting on a national basis at this point, there is no NERC software developed for collecting or reporting wind data and there are no NERC trained staff to handle wind reporting.

Wind GADS White Paper Page 1

The Case for Mandatory Wind (GADS) Reporting Wind continues to grow in the U.S. exceeding 60GW installed capacity in 2012. This represents 28% growth in one year. The National Renewable Energy Laboratory (NREL) report “Renewable Electricity Futures Study” (2012) indicates that as much as 80% of our energy could come from renewable sources by 2050. 37% of this capacity would come from wind resources. The mix of renewable resources will have a significant impact on transmission management and distribution. The impact of wind is already being felt in some distribution regions. The variable nature of wind impacts planning and reserve requirements. Implementing mandatory reporting of availability and configuration data earlier will decrease the implementation cost for the wind plant managers and provide historical performance data that can be used to manage the bulk power system reliability. Also, collecting data early in the growth phase of wind development will help bulk power supply managers understand management issues earlier and have the data to respond more quickly. Wind Plant managers would be able to benchmark and understand equipment reliability across the nation.

The contribution of wind to the overall U.S. generation continues to climb. Wind contributed 3.5% of U.S. generation in 2013 and is expected to exceed hydro by 2017. See Figure 1.

For wind managers and developers the overall implementation cost of mandatory reporting will increase the longer mandatory reporting is delayed. Implementation requires clear guidelines (DRI), while SCADA systems currently in use may require modification and development of reporting software.

Wind is expected to be a major energy resource, moving from the current 3.5% to 37% of the U.S. generation in the next 36 years. The mix of renewable resources will have a significant impact on transmission management and distribution. Implementing mandatory reporting of availability and configuration data early will decrease the implementation cost for the wind plant managers and provide historical performance data that can be used to manage the bulk power system reliability. Also, collecting data early in the growth phase of wind development will help bulk power supply managers understand management issues earlier and have the data to respond quicker. The positive for the Wind Plant manager is the ability to benchmark and understand equipment reliability across the nation.

Figure 1 - Percent U.S. Energy Contribution from Wind


Consequences of Delaying Mandatory Reporting During the early voluntary testing period for the first Wind DRI, about 3% of the Wind Plants participated. After the Wind DRI Ver. 1.0 was posted with voluntary reporting requirements there was no increase in the participation rate. Several companies have expressed the desire to contribute but are waiting for the latest Wind DRI revision to finalize internal reporting software. The GADS DRI systems really act as the historian for power generation and performance in the US. This history tells us where we have been and points toward where we are going. Knowing where we are headed allows us to implement course corrections and improve support structures prior to need. The revised Wind DRI is the key to this effort as well as providing standardized language and processes used across the industry.

Implementation of Mandatory Reporting for Wind The following three implementation challenges need to be addressed before implementation can occur:

1. Process – Completion of the revised Wind DRI depend upon the perceived urgency by the PC. If the PC review and public comment go smoothly, mandatory reporting for wind could occur as early as 2016. If there are delays or considerable public input, the start of mandatory reporting could be delayed until 2017 or 2018.

2. Budget – Funds are needed for software development, implementation, training and maintenance. The NERC budget process is complete for 2014 and there are no funds allocated for Wind mandatory reporting. If the DRI is completed in 2014, funds could be budgeted for 2015. Getting funds in the 2015 budget is problematic because the DRI may not be complete prior to the beginning of the 2015 budget cycle. In this case reporting would be delayed until 2017.

3. Implementation – Implementation itself requires several steps: 1) A finalized Wind DRI 2) Fully developed software specifications 3) Completed data collection and reporting software development 4) Testing the software with actual plant data 5) Training for NERC staff and data reporters. Although many of the metrics used in wind are the same as conventional GADS metrics, the underlying process is different. Thermal / Hydro GADS use events to assess downtime, whereas Wind assesses downtime by equipment code. There are too many events in wind to enter without adding a significant burden to the plant overhead. So the initial development through training may take longer due to lack of experience with the new process.

Table 1 - Installed Cumulative Wind Plant Capacity Distribution

Installed Capacity (MW) 2007 2008 2009 2010 2011 2012

2013 est.

2014 est.

2015 est.

2016 est.

2017 est.

<25 48 93 143 208 255 340 435 535 637 744 854 25-49.9 51 64 77 83 96 114 133 153 173 195 218 50-74.9 33 50 67 77 86 98 118 140 162 187 214 75-99.9 23 38 48 58 66 81 97 117 138 166 198

100-149.9 29 52 71 81 93 125 158 191 224 259 295 150-199.9 10 25 37 43 55 67 87 109 127 146 165

>200 22 23 28 33 38 56 75 98 112 128 145 Total 216 345 471 583 689 881 1103 1343 1573 1825 2089

Note: 2007 to 2012 derived from AWEA Market Reports.


Table 1 shows the cumulative number of Wind Plants for various installed MW capacities. The table not only demonstrates the rapid growth in wind but also the number of plants for each implementation phase (Yellow) (January 2016 approximately 112 - 200MW plus, 421 – 100 to 200MW plants in 2017 and 270 in 2018). Phasing in the Wind Plants allows time for tuning and experience to build prior to the greater mass of plants entering the system.

Recommendations Wind generation continues to grow at a robust pace and will probably exceed hydro generation around 2017. As a result some bulk power distributors are already seeing impacts to their planning and reserve requirements. The challenge for transmission managers and power distributors is to integrate the variable nature of wind generation into the energy mix. As a result new and powerful forecasting tools are being developed to anticipate the variability of wind. Wind GADS plays an essential role in understanding the reliability of wind assets, the rate of wind development in a particular transmission area and when rate / reliability become critical to planning and reserves. NERC GADS will effectively become the historian. Without history utilities cannot plan for the future, and with incomplete history the planning activity cannot be accurate. A phased in approach is recommended beginning in 2016 or 2017.

Likely Objections Issues and concerns were previously expressed in the Mandatory Reporting Task Force – Wind Sub-Group report and those concerns have not changed.

1. Cost of implementation. Wind plant performance systems are currently being developed and implemented. The industry would like to use a standard frame work to build those systems around. Delaying the Wind DRI will force these systems to be remediated at some time, adding to O&M cost.

2. Data Security – As the industry becomes more competitive, its methods, processes, procedures and performance are increasingly what define top performers. The leaking of data can impact the competitive advantage of an organization.

3. Opening the Flood Gate – There is increasing industry concern over perceived creeping demand for data and information resources by the Federal Government. Would mandatory reporting provide a foothold for more onerous requests down the road?

4. Audits – Will mandatory reporting ultimately lead to increased compliance burden (i.e. audits) down the road?

5. Fines – Will there be fines for late reporting or failure to report or audit findings as part of mandatory reporting.


Proposed Work Plan Voluntary Reporting Enhancements

1. Develop Wind White Paper to present to the PAS and then to the PC. 2. Complete the first draft of the Wind GADS DRI Ver. 2.0 – Target Date December 2013. 3. Distribute the revised document to the GADS Wind Sub-Team 4. Schedule a face to face meeting with the GADS Wind sub-team 1st quarter 2014 to review and

finalize the Draft Wind GADS DRI Ver. 2.0 5. Update the Draft Wind GADS DRI Ver.2.0 with comments and or suggestions from the GADS Wind

Sub-Team. Need to consider off-shore wind equipment codes during this meeting. Draft Wind GADS DRI Ver. 2.1.

6. Schedule a phone meeting of the GADS Wind Sub-Team the 2nd quarter 2014 for a final review of the Draft Wind GADS DRI Ver. 2.1.

7. Update the Draft Wind GADS DRI Ver. 2.1 with comments and or revisions from the GADS Wind Sub-Team. Draft Wind GADS DRI Ver. 2.2.

8. Submit Draft Wind GADS DRI Ver. 2.2 to the GADS Working Group for review and approval. Mandatory Reporting Steps

9. If no changes by the GADSWG forward to the PAS for review and then to the PC. If comments, cycle back through the GADS Wind Sub-Team.

10. If no changes by the PC, move to public comments. If comments by the PC, cycle back through the GADS Wind Sub-Team.

11. If comments from the public, cycle back through the GADS Wind Sub-Team. 12. After the reviews are completed, final approval of the PC and Wind GADS DRI Ver. XX is posted on

the NERC web site. 13. Submit mandatory reporting recommendations from the GADS Wind Sub-Team to the PC for review,

acceptance or rejection. 14. If accepted, notification needs to be posted on the NERC web site and the industry notified. 15. If reporting by wind is rejected a decision will need to be made whether there will be voluntary

reporting or will the Wind DRI just become the industry standards. 16. NERC – Budget for wind software, training and maintenance of GADS wind performance data. 17. Create training guidelines for downtime allocation and data submittal. 18. Start working on Wind GADS DRI Ver. 3.0 which will primarily deal with commercial energy metrics.


FMDRAT Report February 10, 2012

A Report on

Assessing the Need for Introducing Demand Response Functions and Entities to the NERC

Reliability Functional Model

Prepared by: Functional Model Demand Response Advisory Team

Executive Summary

The Functional Model Working Group Demand Response Advisory Team (FMDRAT) has completed an assessment of the need to include Demand Response (DR) functions and associated functional entities either in the NERC Functional Model (FM) or as an Applicable Entity for NERC Reliability Standards.

The FMDRAT assessed a number of key issues related to the role and reliability impacts of DR in the planning and operation horizons. This assessment leads to the following key conclusions and recommendations:

1) DR is generally considered in BES planning and operations from the perspective of resource adequacy assessment and operating reserve determination. Long‐term planners, operational planners and operators do take into account the amount of DR under contractual agreement or participated in operating reserve market to adjust resource needs to meet forecast system demand and reserve requirements. Since DR itself is not an active facility or component like a generator, its “dispatch” action is initiated upon receiving instructions from the operating authorities under pre‐ determined system conditions. Compared to sudden load increase and generator tripping, DR’s spontaneous performance or failure to perform as instructed does not pose adverse reliability impacts on the BES for which there is no recourse. Providing DR offered by entities—classified as DR owners or DR operators—is not materially different than other dispatchable resources and would not impact BES planning and operations if these functions were to be added to the Functional Model. Hence, there is not a need at this time to include DR in the Functional Model to describe its role in contributing to BES reliability.

2) Reliability standards are not required to enforce DR compliance with commercial agreements or obligations. Imposing reliability standards to force compliance with commercial agreements would be inappropriate, may not achieve the desired outcome, and in fact may discourage entities from participating in DR programs.Imposing reliability standards to force entities responsible for DR operations to comply with commercial agreements would be inappropriate, may not achieve the desired outcome, and in fact may discourage entities from participating in DR programs. There is thus no urgency or need to develop reliability standards to ensure compliance with what is essentially a business arrangement with commercial mechanisms already in place to drive the desired outcome.

3) The NERC technical committees, including the Operating, Planning, and Critical Infrastructure Committee, continue to monitor DR development and identify if and when


DR technology and penetration levels create a unique impact on BES reliability.The FMWG should continue to monitor DR development and identify if and when DR

technology and penetration levels create a unique impact on BES reliability.


1.0 Introduction

The Functional Model Working Group Demand Response Advisory Team (FMDRAT) has completed an assessment of the need to include Demand Response (DR) functions and associated functional entities either in the NERC Functional Model (FM) or as an Applicable Entity for NERC Standards. The issues considered by the FMDRAT and the key findings after discussing these issues are summarized in this report for consideration by the Functional Model Working Group (FMWG).

The FMDRAT is made up of 14 members appointed by the NERC Standards Committee. The FMDRAT’s roster is included as Attachment 1.

2.0 Background

In 2008, the FMWG set up a small advisory team to assess the need to create a DR function and a DR entity. That advisory team concluded that such a function and related functional entity were not justified at that time. The Advisory Team also suggested that the FMWG reconsider the issue when developing Functional Model Version 5 (FM V5). The advisory team recommended consideration of assigning such functions and responsibilities to functions and entities already defined in the FM.

The FMWG reconsidered the issue in its development of FM V5, and again concluded that there was no justification for defining a DR function and entity in the FM V5 Model. The NERC Planning Committee at its December 8‐9, 2009, meeting, when approving the FM V5, requested the FMWG reassess the need to include a DR Functional Entity in FM V6. Below is the excerpt from the Planning Committee’s meeting minutes:

Functional Model Version 5: FMWG Chair Jim Cyrulewski presented an overview of the Functional Model, version 5. On a motion by John Simpson, the PC approved V5, without modification, the technical content of two documents: Reliability Functional Model, Function Definitions and Functional Entities and Reliability Functional Model Technical Document.

The primary discussion focused on what was not in version 5: a functional entity (or entities) responsible for demand resources. Mr. Cyrulewski noted that when the FMWG presents version 5 to the Standards Committee (SC) in January 2010 for approval, it will be recommending a new subgroup be formed to address the demand resources function so that it can be incorporated in version 6. John Simpson suggested that the PC’s Resource Issues Subcommittee be involved in that effort.

The Standards Committee in response to the FMWG’s request approved the formation of the FMDRAT to address the Planning Committee’s request. The FMDRAT was formed in May 2010, and from September 2010 to February 2011 completed its assignment to assess the need for a DR function and entity. This report presents the FMDRAT’s assessment and recommendations.


3.0 Key Issues Addressed by the FMDRAT

The FMDRAT began its tasks by identifying and compiling a list of potential reliability impacts associated with the participation of DR. The issues were important because the Functional Model is a general description of the primary reliability tasks that need to be performed to ensure reliability of the Bulk Electric System (BES).

Presented below is a summary of the FMDRAT’s assessment of each of the identified key issues.

3.1 Reliability Impact of DR ‐ Does the change in energy use from a DR

asset or from an aggregation of DR assets create any unique reliability impact?

Demand Response (DR) is a temporary change in electricity usage by a Demand Resource in response to market or reliability conditions.1 Demand Response is regarded as a “dispatchable” resource (as opposed to energy efficiency, which is always “on”) whose deployment is driven by pre‐determined system conditions or reliability event criteria by an operating entity. The system operator typically provides instruction to the DR provider for deployment of DR assets. Additionally, DR providers may self‐schedule DR asset deployment, as in the case for economic dispatch in some regions.

A DR asset or aggregator that functions according to operating conditions as defined by prior agreements poses no impact to reliability because its impacts are analyzed and assessed in the Operating Plans of the respective Transmission Operator (TOP) and Balancing Authority (BA).

The TOP and BA plan in advance to meet system load, including load that is represented or controlled by DR entities. TOPs and BAs have knowledge of all relevant conditions and agreements, and plan operations accordingly for the load to be served with or without contribution from DR.

To the BA, load is a composite value (i.e., not locational) and a forecast can be developed for how much capacity is required to meet that load. Contractual arrangements with DR providers are accounted for in the BA’s operating plans.

To the TOP, load is locational and it is based on historic load bus values. The DR control of load does not change the location of the basic load; rather, the availability of DR provides the TOP with another option to control congestion and to maintain reliability.

From a MW change perspective, DR mis‐performance does have some reliability impact on the BES but such impact is not expected to be at a level that will create an Adverse Reliability Impact for which there is no remedy. TThe impacts from a failure of DR to respond on the power system are no different from a situation where a Generator Operator does not generate to its cleared energy quantity or does not respond to requests to raise generation. At present, there are no reliability standards that mandate a Generator Operator to comply with thecommercial agreementsbusiness agreements. However, there are reliability standards that impact GOPs.2 There are mechanisms in some areas (e.g. in some organized

1 North American Energy Standards Board, Wholesale Electric Quadrant definition, 2010 2 Examples include (but are not limited to): IRO‐010‐1a (R3. Each … Generator Owner, Generator Operator…, shall provide data and

Commented [CMSEH1]: I think we can use the Glossary definition of Adverse Reliability Impact so long as we leave it as a capitalized term.

Commented [JM2]: This should be defined up front. It is used throughout, and has a specific meaning: which we believe is cascading, uncontrolled, instability.

Commented [CMSEH3]: I think that we can delete the prior sentence, or leave in the note that there are some operations‐related GO standards. I prefer deleting the prior, since it may not be technically accurate. Under TOP‐001, a GOP must comply with a TOP reliability directive (which would certainly be a directive to alter generator output (either in MW or MVAR).

Commented [vo4]: This is not a direct comparison and I suggest deleting. I would also be okay deleting the previous sentence if others agree.


markets) to levy a penalty on 1 North American Energy Standards Board, Wholesale Electric Quadrant definition, 2010

information, as specified, to the Reliability Coordinator(s) with which it has a reliability relationship) and TOP‐001‐1a (R3. Each… Generator Operator shall comply with reliability directives issued and identified as such by its Transmission Operator… unless such action would violate safety, equipment, regulatory, or statutory requirements.

Formatted: Indent: Left: 0.56", Right: 0.22", Line spacing: Multiple 1.15 li


There are mechanisms in some areas (e.g. in some organized markets) to levy a penalty on the Generator Operator for not meeting its commitment or requested output, but this is a commercial arrangement which falls outside of the scope of the NERC Functional Model or reliability standards. BAs and TOPs are similarly free to prescribe penalties for comparable failures of DR. Such penalty structures are not currently described in the Functional Model, nor are there reliability standards developed to enforce compliance to such penalties.

Observation 1: DR may be considered a dispatchable resource as compared to energy efficiency, which is always “on.”. Iit is also generally regarded as a load whose contractual arrangement is to be reduced in response to operating instructions or as triggered by market mechanism, thus providing the intended reliability benefit to system planners and operatorsis well‐known to the operators. At present, there does not appear to be any adverse reliability impact on the BES unique to DR resources where there is no recourse either for the DR’s reduction of load as planned or requested, or the DR's failure to reduce load as planned or requested. There does not appear to be any DR impact on BES reliability that is materially different than that of other dispatchable resources.

3.2 Reliance on DR to provide Operating Reserves

In some organized markets, DR may participate in the reserve market. In non‐organized markets, DR may enter into contractual arrangements with the host utility to provide reserve capability. The FMDRAT assessed that the BA was responsible for ensuring adequate reserves in the operations time frame, and the BA was required to understand the characteristics of the DR resources regardless of the market setup, and the BA was required to develop the necessary recourses to guard against DR’s failure to perform. Again, this situation is no different than generators not providing operating reserves. At present, there are no reliability standards that mandate a Generator Operator to provide the needed reserves as procured or requested by the Balancing Authority.

To manage the potential risk that DR fails to provide the dispatched or self‐scheduled reserve quantity agreed upon, some organized markets apply a discount factor to the amount of reserves offered by a DR resource, while some organized markets limit DR participation to 30‐minute reserve services. Still others do not count on the DR to begin with, but as load drops off, the responsible entity backs down the generation loaded in response to the activation in order to maintain adequate operating reserves.

Similar measures were determined by the FMDRAT to be adopted in non‐organized markets through contractual arrangements.

Observation 2: TOPs or BAs are responsible for managing the load and supply balance in their control areas. Dispatchable DR resources are generally considered in resource adequacy and operating reserve assessments in the operational planning time frame. However, it does not appear that DR presents any new or different unique risks to the BES compared to any other dispatchable resource available to the TOP or BA. All responsible entities have measures in place to guard against the possibility that any dispatchable resource does not fulfill its obligations to provide the agreed amount of reserves. There are no adverse reliability impacts on the BES for which there is no recourse when DR resources do not perform as planned or requested to provide the needed reserve.

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3.3 DR resources’ obligations to support resource planning

Many planning entities consider DR in their mid‐term and long‐term resource planning processes. Some Planning entities consider DR as a resource to help meet the reserve margin requirement that is determined by either the traditional loss‐of‐load expectation (LOLE) process or by other commonly used methodologies.

Projected available DR may be applied as an available resource to help meet a reserve margin requirement, or applied as an offset to the long‐term load forecast. Some planning entities apply a forced outage rate to the DR, similar to dispatchable generators, and simulate DR performance in LOLE calculations. In each case, some uncertainty exists around long‐term DR resource availability due to the short term contractual nature of DR assets as compared to the expected life of a generation asset. Some entities conduct more frequent resource adequacy assessments as the planning horizon approaches the near‐term. An additional DR functional entity will not change the current role or responsibility of the planning coordinator or the resource planner.

Observation 3: Some entities consider DR in long‐term planning and its treatment varies from one entity to another. However, owing to the long lead time in the planning process, there is uncertainty as to whether or not the status of the DR will remain unchanged as it approaches real time. An additional DR functional entity will not change the current role or responsibility of the planning coordinator or the resource planner.

3.4 Need for reliability standards to enforce compliance with contractual agreements/obligations

At present, DR is usually arranged via contractual agreements or market mechanisms such as pricing thresholds, reserve offerings, or forward capacity auctions. In these arrangements, penalties are levied if commercial or contractual obligations are not met. These mechanisms are similar to generators bidding into and being dispatched in an energy market and getting paid the market price or another pre‐determined price based on the amount of generation provided. In such cases generators would not be paid (and in some cases assessed with additional penalties) if they failed to generate at the agreed upon or committed level. Given these contractual or commercial payment/penalty mechanisms, there do not appear to be gaps that would require the development and enforcement of reliability standards to achieve the desired DR performance. Imposing reliability standards to force compliance to commercial agreements is inappropriate, may not actually achieve the desired outcome, and may in fact discourage load from participating in DR programs.

The FMDRAT further assessed whether DR is a fundamental component or product of the BES. DR can provide some flexibility in both the long‐term and operational planning time frames, to the extent that the responsible entities can choose which loads continue to be


supplied. As such, DR may be considered a derivative product that should continue to be handled by commercial arrangements, not reliability standards.

Observation 4: Reliability standards are not required to enforce DR to comply with contractual commercial agreements or obligations since DR participation is essentially a commercial arrangement. There are little or no material reliability impacts if DR fails to perform as agreed to or as requested for which there is no recourse (from Observations 1 and 2, above). Imposing reliability standards to force compliance to commercial agreements may not achieve the desired outcome of ensuring long‐term reliability and may discourage entities from participating in DR programs.

3.5 DR Ownership and Operations – roles and relationships with others

In consideration of the possibility of introducing DR functions and entities to the Functional Model, the FMDRAT developed a draft set of tasks describing a Demand Response Ownership function and the relationship between the DR Owner and others. The FMDRAT also developed a draft set of tasks for a Demand Response Operations function and the relationship between the DR Operator and others. The objective of this exercise was to compare the primary functions between the two types of resource providers.

The FMDRAT concluded that a parallel to the tasks and relationships developed for the Generator Ownership and Generator Operations and their respective functional entities could be drawn for DR. The draft list of tasks and relationships for the DR Ownership and Operation functions and for the DR Owner and DR Operator is provided in Appendix A for information only. The FMDRAT did not finalize or accept the list provided in Appendix A in light of the FMDRAT’s assessment that introducing DR functions and associated entities to the Functional is not required at this time. The list is provided herein only as a matter of record for future reference and is not part of the FMDRAT’s recommendation at this time.

3.6 Conclusion of Majority Position

A near‐unanimous consensus of the FMDRAT agreed with the analysis made for each of the key issues and the corresponding assessments detailed in this section of this report. The same majority agreed that that there is not a demonstrated need to introduce DR functions and entities to the Functional Model at this time.

4.0 Minority Position

The key counter‐arguments centre center on the comparable obligations between DR Owner/Operator and Generator Owner/Operator (GO/GOP). At present, there are a number of reliability standards that apply to GOs and GOPs. DR providers may offer their product into energy or ancillary services markets and receive compensation for successful performance. They should bear the same obligations as their generation counterparts and hence should have a comparable set of reliability standards imposed on the DR Owners and

Commented [CMSEH5]: I would really like to see this sentence deleted. First the report is about registration not the imposition of Reliability Standards. Second, it is speculative (may/may not) and therefore not really relevant to the ultimate recommendations.


Operators. However, if DR is not introduced to the Functional Model and if DR were required to meet the same reliability standards, then a number of standards currently applied to GO and GOP, as listed in Appendix B, should be removed from the NERC reliability standards.

The FMDRAT assessed these minority views and arrived at the following assessments:

Apart from the fact that both generation and DR provide resources to the BES, there are some fundamental differences between them. Generators are a fundamental part of the integrated power system; they provide primary products for BES reliability – energy and ancillary services. Generators do change output in reaction to system changes and their changes are largely governed by their inherent physical characteristics and auxiliary device settings. These characteristics and settings need to be verified and modelled, and the simulated generator performance needs to be assessed against specific standards criteria to ensure that any adverse effects are self‐contained or isolated without propagating to other parts of the BES which could result in uncontrolled or cascade tripping. It is largely on this basis, to ensure acceptable generator performance, that reliability standards are developed and imposed on GOs and GOPs. 3

DR is a derivative or supplementary part or product of the power system, with specific rules for participation in BES operations. DR augments the capabilities of the BES thus increasing the effective utilization of the BES, but it does not expand the capability of the system to serve more loads, unlike its generator counterpart.

DR changes in load are inherently independent of system changes. Therefore, reliability standards are not needed to ensure acceptable performance as in the case of their generator counterpart. Commercial arrangements and compensation/penalty mechanisms are in place to govern DR contractual obligations and are sufficient to drive the desired behaviour when DR is called upon to act. Imposing reliability standards to enforce such behaviour is inappropriate and unnecessary and may not actually achieve the desired outcome. Observation 5: DR is a derivative or supplementary part or product of the power system, with specific rules for participation in BES operations. DR must be verified and assessed in planning models, similar to a generator. DR augments the capabilities of the BES, thus increasing the effective utilization of the BES, but does not increase the total installed capacity of the system, unlike its generator counterpart.

As to the request to remove the listed reliability standards for the GOs and GOPs, the FMDRAT did not agree to a position since such a determination was not part of our its charter.

5.0 Conclusions Observations and Recommendations

The FMDRAT assessed a number of key issues related to the role and reliability impacts of DR in the planning and operation horizons. The assessment leads to the following conclusionsobservations:

1. DR is generally considered in BES planning and operations from the perspective of resource

3 Reliability Standard MOD‐025 requires the verification of the real and reactive power capability of generators to “ensure that accurate information is available for planning models used to assess Bulk Electric System (BES) reliability”.

Commented [JM6]: There is some concern about the accuracy of this statement: For example, in PJM the Capacity Emergency Transfer Limit (CETL) deliverability study includes the contribution from all PJM load management programs (including DR). Thus, the availability of this DR serves to allow the system to serve all firm loads. It is not unlike the interstate natural gas pipelines which rely on interruptible loads to serve all firm customers during times of peak utilization. Recently, we observed DR activation in one operating area to serve more load in another area. More firm load can be added to the system as a result of more DR resources. Essentially, there is a lot of disagreement with this sentence. Rightfully so, there are many valid points of view on this. Therefore, we think this should be deleted or modified. However, this is the FMDRAT view so we only have this suggestion: "DR augments the capabilities of the BES thus increasing the effective utilization of the BES, but does not increase the total installed capacity of the system, unlike its generator counterpart."

Commented [vo7]: I agree with John’s comment above. I would support the removal of the deleted text.


adequacy assessment and operating reserve determination. Long‐term planners, operational planners and operators do take into account the amount of DR under contractual agreement or participated in operating reserve market to adjust resource needs to meet forecast system demand and reserve requirements. Since DR itself is not an active facility or component like a generator, its “dispatch” action is initiated upon receiving instructions from the operating authorities under pre‐determined system conditions. Compared to sudden load increase and


generator tripping, DR’s spontaneous performance or failure to perform as instructed does not pose

adverse reliability impacts on the BES for which there is no recourse.At present, there does not appear to be any evidence suggesting a potential adverse reliability impact on the BES unique to DR. Providing DR as offered by entities to— be classified as DR owners or DR Ooperators—is not materially different than other dispatchable resources and would not impact BES planning and operations if these functions were to be added to the Functional Model.

2. TOPs or BAs are responsible for managing the load and supply balance in their control areas.

Dispatchable DR resources are generally considered in resource adequacy and operating reserve assessments in the operational planning time frame. However, it does not appear that DR presents any new or different risks to the BES compared to the longstanding load management programs adminsteredadministered by existing Registered Entities and any other dispatchable resource available to the TOP or BA.All responsible entities have some measures in place to guard against the possibility that a DR

resource does not fulfill its obligations to provide the agreed amount of reserves.

3. For long‐term planning, most entities include contributions from DR to some extent. Uncertainties associated with DR’s long‐term commitment to remain “dispatchable” are typically addressed by applying a discount factor or probability analysis to DR’s availability in resource adequacy assessments.

4. In operational planning, there are no known entities that count on DR as a critical component of their operational plans. An additional DR functional entity will not change the current role or responsibility of the planning coordinator, resource planner, or operations planner.

54. Reliability standards are not required to enforce DR compliance with contractual commercial agreements or obligations. There are little or no reliability impacts caused by the failure of DR resources to perform as agreed to or as requested. Therefore iImposing reliability standards to force compliance with commercial agreements would be inappropriate, may not achieve the desired outcome, and in fact may discourage entities from participating in DR programs.

65. DR is a reactive component and a derivative product of the power system ; DR augments the

capabilities of the BES, thus increasing the effective utilization of the BES, but does not increase the total installed capacity of the system. it augments the capabilities of the BES thus increasing the effective utilization of the BES but it does not expand the system’s capability to serve more load and DR does not move spontaneously or in response to system changes for which reliability standards might be needed to ensure acceptable performance. Having commercial arrangements and compensation/penalty mechanisms in place to govern their contractual obligations would suffice to drive DR to achieve the desired behavior. Imposing reliability standards to enforce such behavior is extraneous and unnecessary.

Conclusions Observations (1) to through (43) suggest that at the present time there is no need at this time to include a DR entity in the Functional Model to describe its role in contributing to BES reliability. While DR and DR entities participate in electricity markets and are dispatched by system operators—hence, contributing to the reliable operation of the BES—Conclusions Oobservations (54) and (65) suggest that there is no urgency or need to develop reliability standards to ensure compliance with commercial agreements or obligationswhat is essentially a business arrangement with commercial mechanisms in place to drive the desired outcome.

It is on the above basis that the FMDRAT recommends:

1. DR functions and their associated functional entities not be defined and introduced to the

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Functional Model at this time.

2. The NERC technical committees, including the Operating, Planning, and Critical Infrastructure Committee, continue to monitor DR development and identify if and when DR technology and penetration levels create a unique impact on BES reliability.As a future consideration, Tthe NERC Planningstanding Ccommittees may wish toshould continue to participate in or monitor the reliance of the BES on increasing levels of D from a resource and reserve assessment perspective:may wish to couldmay wish to may wish to cR.

Commented [JM9]: The original report recommended "The FMWG continue to monitor DR development and identify if and when DR technology and penetration levels create a unique impact on BES reliability." This recommendation is much more passive. We need direct actions similar to the original recommendation of the FMDRAT. However, since the technical committees are positioned to do these assessments and have the tools, a stronger recommendation is to recommend their participation. We have revised based on the original document and replaced FMWG with


Attachment 1

The Functional Model Demand Response Advisory Team

Name

Company

Ben Li (Chair/Facilitator) Ben Li Associates

1 Albert DiCaprio PJM

2 Phil Davis Schneider Electric

3 Stephen C. Knapp Constellation Energy Commodities Group, Inc.

4 John D. Varnell Tenaska Power Services Co

5 Donna Pratt NYISO

6 Ken Clark (did not participate) Consert, Inc.

7 Aaron Breidenbaugh EnerNOC

8 Wayne Van Liere EON US

9 Ulric Kwan Pacific Gas & Electric Company

10 Eric Winkler, Ph.D. ISO New England

11 Paul Wattles Electric Reliability Council of Texas (ERCOT ISO)

12 John Simpson RRI Energy

13 Andy Satchwell Lawrence Berkeley National Lab

14 Tony Jankowski We Energy


Appendix A

DRAFT List of Perceived Tasks and Relationships for

Demand Response Functions and Entities

(The list is provided for information and for future reference only; it is not part of the FMDRAT’s recommendation at this time.)

Generation Demand Response

Generator Ownership Function

Tasks

Demand Response Ownership Function

Tasks

1. Establish generating facilities ratings, limits, and operating requirements.

2. Design and authorize maintenance of generation plant protective relaying systems, protective relaying systems on the transmission lines connecting the generation plant to the transmission system, and Special Protection Systems.

3. Maintains owned generating facilities.

4. Provide verified generating facility performance characteristics / data.

Functional Entity – Generator Owner

The functional entity that owns and maintains generating units.

Relationships with Others

1. Provides generator information to the Transmission Operator, Reliability Coordinator, Balancing Authority, Transmission Planner, and Resource Planner.

2. Provides unit maintenance schedules and unit retirement plans to the Transmission Operator, Balancing Authority, Transmission Planner, and Resource Planner.

1. Establish demand response facility ratings, limits, and operating requirements.

2. Design and authorize maintenance of demand response facilities and associated control devices.

3. Maintains owned demand response facilities.

4. Provide verified demand response facility performance characteristics / data.

Functional Entity – Demand Response Owner The functional entity that owns and maintains demand response facilities.

Relationships with Others

1. Provides demand response information to the Transmission Operator, Reliability Coordinator, Balancing Authority, Transmission Planner, and Resource Planner.

2. Provides demand response facility maintenance schedules to the Transmission Operator, Balancing Authority, Transmission Planner, and Resource Planner.


3. Develops an interconnection agreement with

Transmission Owner on a facility basis.

4. Receives approval or denial of transmission service request from Transmission Service Provider.

5. Provides reliability related services to Purchasing‐Selling Entity pursuant to agreement.

6. Reports the annual maintenance plan to the Reliability Coordinator, Balancing Authority and Transmission Operator.

7. Revises the generation maintenance plans as requested by the Reliability Coordinator.

Function – Generator Operation

Tasks

1. Formulate daily generation plan.

2. Report operating and availability status of units and related equipment, such as automatic voltage regulators.

3. Operate generators to provide real and reactive power or reliability‐related services per contracts or arrangements.

4. Monitor the status of generating facilities.

5. Support Interconnection frequency.

Functional Entity – Generator Operator

The functional entity that operates generating

3. Reports the annual maintenance plan to the Reliability Coordinator, Balancing Authority and Transmission Operator.

4. Revises the demand resource facility maintenance plans as requested by the Reliability Coordinator.

Function – Demand Response Operation Tasks

1. Formulate daily demand response resource plan.

2. Report operating and availability status of demand response related equipment and control devices.

3. Operate demand response facility control devices or otherwise implement demand reduction or demand increase in response to instructions or according to contract arrangements.

4. Monitor the status of demand response facilities.

Functional Entity – Demand Resource Operator The functional entity that operates demand


unit(s) and performs the functions of supplying energy and reliability related services.

Relationship with Others

Ahead of Time

1. Operate generators to provide real and reactive power or reliability‐related services per contracts or arrangements.

2. Provides operating and availability status of generating units to Balancing Authority and Transmission Operators for reliability analysis.

3. Reports status of automatic voltage or frequency regulating equipment to Transmission Operators.

4. Provides operational data to Reliability Coordinator.

5. Receives reliability analyses from Reliability Coordinator.

6. Receives notice from Purchasing‐Selling Entity if Arranged Interchange approved or denied.

7. Receives reliability alerts from Reliability Coordinator.

8. Receives notification of transmission system problems from Transmission Operators.

response facilities and performs the functions of curtailing or increasing demand in response to instructions or in accordance with contractual arrangement. Relationship with Others

Ahead of Time

1. Implement demand reduction or consumption increase in response to instructions or according to contract arrangements.

2. Provides operating and availability status of demand response to Balancing Authority, Transmission Operator and Reliability Coordinator for reliability analysis.

3. Provides operational data to Reliability Coordinator.

4. Receives reliability analyses from Reliability Coordinator.


Real Time

9. Provides Real‐time operating information to the Transmission Operators and the required Balancing Authority.

10. Adjusts real and reactive power as directed by the Balancing Authority and Transmission Operators.

Real Time

5. Provides Real‐time operating information to the Transmission Operators and the required Balancing Authority.

6. Adjusts demand in response to instructions or according to contract agreements.

Post Real Time

7. Provide operating information required Balancing Authority for settlement purposes


Appendix B

Minority Views

List of NERC Reliability Standards that should be removed if DR is not Assigned the Same Obligations as GO/GOP

On the basis of comparable treatment of “supply” resources used to balance load and supply in both the planning horizon and the real time operating horizon, a BA may choose between DR and traditional generation resources to meet the load obligations on the grid. As increased use is made of DR to meet certain load requirements, lower commitments are made of traditional generation supply. This is fine as long as the DR “supply” shows up when the BA calls on it.

Penalizing a DR that doesn’t perform as agreed to or requested by penalize it via market mechanism is not acceptable from a reliability perspective. If sufficient DR doesn’t show up and traditional generation resources have not been committed and cannot get on‐line in time to meet the aggregate demand, then some load will have to be curtailed against its desires in order to maintain BES reliability.

If it is indeed our position that whether or not DR responds when called upon that it does not impact reliability, then the following changes ought to be made to the existing reliability standards:

a) CIP Standards: remove Generator Operators from these standards. If it is not important for supply to respond when called on then we don’t need these standards applied to any supply resources.

b) COM‐002: remove Generator Operators from the Applicability. If DR that is used as a supply resource doesn’t need to respond, then GOPs do not need to have communications with the RCs for them to respond either.

c) IRO‐001,‐004,‐005,‐010: remove Generator Owners and Generator Operators from these standards. If it is not important that DR used as a supply resource responds to the directives of the RC, then it should not be important that GO/GOPs respond either. They also should not have to provide information on their capabilities in Day Ahead or Current Day time frames. There also shouldn’t be a need to coordinate any maintenance outages with the RC. After all, if a DR owner or operator can just sit out for a day, then a generator should be able to do the same thing.

d) MOD‐024,‐025: delete these standards. If it is not important to know or qualify the capacity of a DR resource, then we should not have to qualify the capacity of a traditional generation resource either.

e) PRC‐001,‐005: remove Generator Operator and Generator Owner from these standards. If it is not important to reliability that DR operate properly when called on, then we should not have to coordinate protective relays or do protection system maintenance for traditional generation resources either.

f) TOP‐001,‐002,‐003,‐006: remove Generator Operators from these standards. If it is not important that DR which is being counted on by the BA to respond to directives from the RC, then we shouldn’t need to have GOPs respond either. There shouldn’t be a need to coordinate normal operations planning with the RC, or coordinate outage schedules with the


RC, or provide notice to the RC of any resources that are available, or not available for dispatch.

As more and more DR is included in the dispatch stack and the planning and operating horizon, fewer real generation resources are included to meet the aggregate load obligations on the grid. It is certainly important to the BA and the RC that the real generation resources can be counted on to perform when called. As DR replaces those real generation resources, it should be important that they respond as well. Comparable reliability standard requirements should be in place for DR resources as are in place for Generator Owners and Operators.

FMDRAT-PC Meeting Summary January 7, 2014 North American Electric Reliability Corporation 3353 Peachtree Road NE, Suite 600 – North Tower Atlanta, GA 30326 Attendees: Dave Weaver, Brian Evan-Mongeon, Steven Huber, Mark Kuras, Ben Li, Patti Metro, Amir Najafzadeh, Vince Ordax, John Seelke, Tom Siegrist, John Varnell, Al Dicaprio, Aaron Breidenbaugh, Tony Jankowski, Donna Pratt, Mallory Huggins, Laura Hussey, Soo Jin Kim, and John Moura Summary: On November 12, 2012, an advisory team for the Functional Model Working Group (FMWG) released a revised draft report entitled “Assessing the Need for Introducing Demand Response Functions and Entities to the NERC Reliability Functional Model.” In January 2013, the Standards Committee asked the FMWG and the Planning Committee (“PC”) to work together to address the different views on the technical conclusions within the report. In July 2013, the PC drafted a letter to FMWG refuting several items and asking for reconsideration some of the conclusory statements within the report. The actions precipitated the need for a meeting between the two entities to reconcile the report findings. The January 7, 2014 meeting opened with an initial discussion on whether Demand Response (“DR”) has an impact on reliability. Members from both the Functional Model Demand Response Advisory Team (“FMDRAT”) and the PC agreed that Demand Response does have an impact on reliability. However, several related items were brought up for further discussion, and the following is an overview of the multiple discussions between the two teams: 1. Categorizing DR Providers

In determining whether DR has a reliability impact, several individuals commented that DR providers

have unique attributes that can operate in a similar fashion or overlap with a GOP or LSE. The overlapping attribute that was discussed the most at the meeting is the ability to control load. The point was raised as to whether LSEs needed communication prior to a DR curtailment of load or dispatch response. This further led to discussions on how DR providers might not fit solely within one of the Functional Model categories. The question was also brought up as to whether certain large customers, such as AT&T, should be labeled as DR. One person noted that the actions of a large industrial customer does not impact the grid, and those types of curtailment are no different than turning off a switch in a home. A majority of DR comes from industrial customers that may not want to engage in mandatory registration. Another comment from the PC was that DR resources have few hurdles for bidding into the reserve market, and a possible solution offered was to generically register DR providers as a “resource” and treat those entities like a GOP.

2. Should DR Providers Become a New Functional Entity

Members of the FMDRAT reiterated that there is no need to add DR functions to the functional

model until the DR markets and providers have matured fully and a definitive need is identified. This led to a discussion on how to reconcile not forcing a registration of DR providers with the scenario in TRE where DR providers are registered as LSEs. The group as a whole concluded that mandatory registration was not required at this point, but there was a consensus that the DR providers should be monitored going forward to evaluate if mandatory registration would be necessary in the future.

3. How Does DR Affect “Planning?”

The concept of planning in general was discussed, and some members of the FMDRAT and PC brought up that planning could be long-term planning or a shorter-term implementation. Some FMDRAT members held the view that DR information is available to the BA, and that information would suffice to aid resource and reserve assessments in operational planning. A PC member responded that although DR is registered with a BA, those resources have to be de-rated for performance. Further, DR “values” are not necessarily the values that are used by the BA in planning. The FMDRAT indicated that this was considered in its DR assessment, and briefly presented in the report. One member of the FMDRAT also wanted to delineate between adequacy and reliability, and believed the two groups used those terms interchangeably when discussing DR. Conclusion Both the FMDRAT and PC members agreed that a path forward is to have the groups reach a consensus on the report by applying some wording changes without changing the key findings and recommendations of the report. The PC would then have the responsibility of monitoring DR going forward, and if the PC recommends mandatory registration in the future, another task can be initiated to address the issue further. Next Steps:

• A revised report will be provided to John Moura to disseminate among the representatives of the PC by January 28, 2014.

• The PC will provide a response to Ben Li that will be disseminated to the FMDRAT by February 7, 2014.

• On a day between February 13 and February 18, 2014, both groups will hold a 2-hour conference call to come to a consensus on the report.

FMDRAT-PC Meeting Minutes – January 7, 2014 2

Eastern Interconnection Frequency Initiative Whitepaper Date: October 28, 2013

Prepared by Members of the NERC Resource Subcommittee

Preface:

Members of the NERC Resource Subcommittee, who are representatives of the Eastern Interconnection, are working with Balancing Authorities within the same interconnection on a voluntary basis to support a pilot program in an effort to improve frequency response. Frequency Response is defined as automatic and sustained change in the power consumption or output of a device such as generator that occurs within 5-20 seconds of and is in a direction to oppose a change in the Interconnection Frequency. While it has been determined that the Eastern Interconnection has generally sufficient frequency response as a whole, there are clues that point to issues with generator governor settings. The sponsors of this initiative believe that proper and consistent governor settings are the low hanging fruit to allay concerns raised by the Federal Energy Regulatory Commission (FERC) as to past trends in frequency response and the differing appearance of frequency in the East, compared to other Interconnections.

Prior to 2010, frequency response has been declining in the East when it should have been increasing with increasing customer demand and the addition of complimenting generation. Additionally, post-event frequency typically exhibits a “lazy L” shape likely caused by set point control overriding the initial response provided by governors.

Source “Frequency Response Initiative Report”, October 30, 2012 Example of “lazy L” a common frequency response characteristic of Eastern Interconnection Source “CERTS NERC Interconnection Frequency

Events May 2013 ”, October 30, 2012

The initiative focus is on the existing generator fleet with respect to 1) the completeness and accuracy of the data provided in the 2010 NERC Generator Survey 2) improving their frequency response capabilities, and 3) for Balancing Authorities and/ or Reliability Coordinators to install tool(s) to monitor individual generator performance within their authority of control and communicate performance results to the individual generators. Introduction: Frequency Response has been the focus of increased attention, analysis, development of standards, and deliberation by stakeholders. The foundation of this complex issue is the performance of governors. The starting point to address the issue is to have a firm understanding of current governor settings. The Resources Subcommittee believes the logical place to begin is to confirm generator data, make changes to settings where feasible and to share tools that can measure governor response.

Frequency Control To understand the role Frequency Response plays in system reliability, it is important to understand the different components of frequency control and the individual components of Primary Frequency Control, also known as Frequency Response. It is also important to understand how those individual components relate to each other.

Frequency control can be divided into four overlapping windows of time:

Primary Frequency Control (Frequency Response) – Actions provided by the Interconnection to arrest and stabilize frequency in response to frequency deviations. Primary Control comes from automatic generator governor response, load response (typically from motors), and other devices that provide an immediate response based on local (device-level) control systems.

Secondary Frequency Control – Actions provided by an individual Balancing Authority or its Reserve Sharing Group to correct the resource – load unbalance that created the original frequency deviation, which will restore both Scheduled Frequency and Primary Frequency Response. Secondary Control comes from either manual or automated dispatch from a centralized control system.

Tertiary Frequency Control – Actions provided by Balancing Authorities on a balanced basis that are coordinated so there is a net zero effect on Area Control Error (ACE). Examples of Tertiary Control include dispatching generation to serve native load; economic dispatch; dispatching generation to affect Interchange; and re-dispatching generation. Tertiary Control actions are intended to replace Secondary Control Response by reconfiguring reserves.

Eastern Interconnection Frequency Response Initiative 2

Time Control – This includes small offsets to scheduled frequency to keep long term average frequency at 60 Hz.

Primary Frequency Control – Frequency Response Primary Frequency Control, also known generally as Frequency Response, is the first stage of overall frequency control and is the response of resources and load to arrest that locally sensed changes in frequency. Primary Frequency Response is automatic, is not driven by any centralized system, and begins within seconds after the frequency changes rather than minutes. Different resources, loads, and systems provide Primary Frequency Response with different response times, based on current system conditions such as total resource/load mix and characteristics.

The NERC Glossary of Terms defines Frequency Response in two parts as:

• (Equipment) The ability of a system or elements of the system to react or respond to a change in system frequency.

• (System) The sum of the change in demand, plus the change in generation, divided by the change in frequency, expressed in megawatts per 0.1 Hertz (MW/0.1 Hz).

As noted above, Frequency Response is the characteristic of load and generation within Balancing Authorities and Interconnections that reacts or responds with changes in power to variations in the load-resource balance that appear as changes to system frequency. Because the loss of a large generator is much more likely than a sudden loss of an equivalent amount of load, Frequency Response is typically discussed in the context of a loss of generation.

2010 NERC Generator Survey Of those that responded to the 2010 NERC Generator survey data in the Eastern Interconnection, only approximately 57% (Figure 1) provided a generator dead band setting. The remaining 43% provided no responses or responses that did not provide the governor settings (e.g. “Looking into” or “Researching this”). Additionally of those that did respond with a dead band value, the majority of those responses reported values close to or equal to zero or exceeded the historical NERC Policy 1 value of 36 mHz. (Figure 2)


Figure 1 Figure 2

The first goal of this voluntary initiative is to have Eastern Interconnection Balancing Authorities provide an updated Generator Survey Form from their existing generator fleet with confirmed generator data. The data will then be transferred to central repository to be used by Modeling Working Groups. The second goal is to target dates for resetting dead bands to a common target range:

• dead band of +/- 36 mHz, • droop settings of 3% -5% depending on turbine type, • continuous, proportional( non-step) implementation of the response • appropriate operating modes to provide frequency response, and • appropriate outer-loop controls (distributed controls) settings to avoid primary frequency

response withdrawal The third goal to develop and share tools to measure individual generator performance on multiple frequency events and provide performance metrics results to their generation fleet in an effort to continue to improve Frequency Response. Current Activities: Several entities have agreed to support this initiative, including MISO, PJM, Duke Energy, TVA, FPL, SCE&G, and SPP. The current plan is: • All individual generators (including nuclear) 400 MW or larger, BAs will request generators to provide a

completed generator survey of governor settings and related data by January 1, 2014. The 400 MW threshold limits this to roughly 7% of all generators in the East.

• All individual generators(excluding nuclear and combined cycle steam turbines) 400 MW or larger, BA’s will request generators to modify all dead bands greater than 0.036 Hz to at most 0.036 Hz and install proportional response if feasible by June 1, 2014. If a generator is unable to meet this timing, the generator is asked to provide reasonable target date to complete this task.


• All individual generators 100 MW to 400 MW, BA’s will request generators to provide a completed generator survey of current governor settings and related data by July 1, 2014.

• All individual generators 100 MW to 400 MW, BA’s will request generators to modify all dead bands greater than 0.036 Hz to at most 0.036 Hz and install proportional response by November 1, 2014. If a generator is unable to meet this timing, the generator is asked to provide reasonable target date to complete this task.


(Figure 3)

Related Documents and Links: “Frequency Response Initiative Report” October 30, 2012 2010 NERC Generator Data Survey


Revision History: Date

Version Number

Reason/Comments

10/28/2013

1.0

Initial Version – “Eastern Interconnection Frequency Initiative”


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