ADS DDVM V1.0 DDVM V1.0.docx · Web viewThe load projection used in the model is typically 1 in 2...

ADS Data Development and Validation Manual (DDVM)

Version 1.0

System Adequacy Planning (SAP) DepartmentJuly 17, 2018

155 North 400 West, Suite 200

Salt Lake City, Utah 84103-1114

W E S T E R N E L E C T R I C I T Y C O O R D I N A T I N G C O U N C I L

ADS Data Development and Validation Manual, Version 1.0

Table of ContentsExecutive Summary............................................................................................................................1

Electric Topology................................................................................................................................2

Load Areas............................................................................................................................................. 2

Regions Within the Western Interconnection.......................................................................................4

Hurdle Rates.......................................................................................................................................... 5

Trading Hubs..........................................................................................................................................5

Transmission and Generation................................................................................................................9

Modeling Branch Ratings.......................................................................................................................9

Paths and Other Transmission Interfaces............................................................................................10

Nomograms......................................................................................................................................... 11

Monitored Lines.................................................................................................................................. 11

Phase Shifters...................................................................................................................................... 11

Resources.........................................................................................................................................12

Resource Modeling Categories............................................................................................................12

Status and Need Categories.................................................................................................................13

Resource Modeling..............................................................................................................................14

Wind and Solar Facilities..................................................................................................................14

Distributed Generation (DG) Facilities.............................................................................................15

Hydroelectric Facilities.....................................................................................................................16

Pumping Loads................................................................................................................................ 18

Pumped Storage.............................................................................................................................. 18

Battery Storage................................................................................................................................18

Thermal Generation Facilities..........................................................................................................19

Co-generation..................................................................................................................................21

VAR Devices..................................................................................................................................... 22

Remote Resources...........................................................................................................................22



Load Data.........................................................................................................................................22

Hourly Loads:.......................................................................................................................................22

Adjustments to (L&R) Data..................................................................................................................23

L&R Data Extrapolation....................................................................................................................23

Energy Efficiency Adjustments.........................................................................................................23

Behind the Meter Photovoltaic (PV) and Distributed Generation (DG) Adjustments......................23

Pumping Loads................................................................................................................................ 23

Demand Response...........................................................................................................................24

Station Service Loads.......................................................................................................................25

Seasonal Bus Distribution................................................................................................................25

Costs and Economics.........................................................................................................................25

Gas Topology and Pricing.....................................................................................................................25

Coal Topology and Pricing................................................................................................................... 26

Other Fuels and Pricing........................................................................................................................26

Emissions Rates by Fuel.......................................................................................................................27

Reference Year for Cost Data...............................................................................................................27

Inflation Rate....................................................................................................................................... 27

Retirement and Extended Outage Assumptions................................................................................28

Renewable Portfolio Standard and Resource Adequacy....................................................................28

Renewable Portfolio Standard (RPS) Compliance Check.....................................................................28

Resource Adequacy (RA) Check...........................................................................................................29

Advanced Modeling..........................................................................................................................30

Reserve Modeling................................................................................................................................30

Flexibility Reserve Modeling................................................................................................................30

Glossary of Terms and Acronyms......................................................................................................33



Executive Summary

The Anchor Data Set (ADS) production cost model (PCM) case represents the expected loads1, resources and transmission topology 10 years in the future for a given reference year. It is a compilation of latest load, resource and transmission topology information developed by Western Planning Regions (WPR) and International Planning Regions (IPR) based on their regional transmission plans. WECC uses GridView as its PCM tool.

A primary goal in developing ADS PCM is to define an expected foundation for the Year 10 study cases included in WECCs Reliability Assessment Committee’s (RAC) annual study program. The case is also used throughout the Western Interconnection for several purposes, including FERC Order 890 and 1000 planning studies by WPR, independent transmission developers’ studies, market studies (e.g., Energy Imbalance Market) and integration studies.

The purpose of this manual is to provide transparency and explanation of components and assumptions needed for modeling and development for an ADS PCM case. The case’s dataset is stored and maintained in GridView (GridView or GV), which is an energy market simulation and analysis software tool distributed by ABB. GridView uses a Microsoft Access database file (GV Case Template.mdb) and numerous text-based shape files (*.DAT) to store the case information. Stakeholders desiring to perform analyses using the ADS PCM case in GridView must obtain software licenses from ABB.

1 The load projection used in the model is typically 1 in 2 years, i.e., actual demand has an equal opportunity (50%) of ex-ceeding or being less than the forecasted demand.


ADS Data Development and Validation Manual, Version 1.0 2

Electric Topology

Load Areas

Description:

The “Load Area” topology is based on the large load centers with consistent load characteristics. In most cases it is analogous to the Balancing Authority (BA) boundaries or the Load-Serving Entity (LSE) (where more granularity is needed) boundaries. Figure 1 shows all the BAs under WECC. Figure 2 shows all the load areas which are included in the topology. Generator-only BAs are not modeled as load areas (as they serve no load) and their generation is assigned to the closest defined load area or to load areas contractually obligated to serve, if such information is available.

Validation & Approval:

Load area topology used in the ADS is validated through the RAC Data Subcommittee & Modeling Subcommittee. As of the creation of this document, both subcommittees are developing specific protocols describing processes used to validate load area topology. WECC will update this document as the Data and Modeling Subcommittees finalize validation protocols.



Figure 1



Figure 2. Load Areas

Regions Within the Western Interconnection

Description:

The regions are modeled to consolidate operational aspects associated with a BA such as hurdle rates2 and reserve requirements, which are explained later in this document. In most cases regions, corresponds to the load areas but with a two-character sub region added to the front of the name (e.g., the Los Angeles Department of Water and Power (LDWP) area is the CA_LDWP region). Only the regional groupings that include multiple load areas are listed in Table 1.

2 Hurdle rates represent the cost to deliver surplus energy among different regions.



Table 1: Regional Groupings

Regional Group Area MembersBS_IPCO IPFE, IPMV, IPTVBS_PACE PAID, PAUT, PAWYCA_CISO CIPB, CIPV, CISC, CISD, VEASW_NVE NEVP, SPPC

Hurdle Rates

Description:

Hurdle rates represent the cost to deliver surplus energy among different regions. Hurdle rates are modeled based on three categories of charges:

1. Tariff rates: trade policy-based charges applied to power transfers between regions.

2. Wheeling rates: charges paid to the owner of a transmission line for the right to transport power across the line.

3. Rates per model validation: interregional charges modeled to encourage reasonable interre-gional transfers. These are set based on stakeholder review of simulation results and their rec-ommendations.

Interregional hurdle rates used are base values and do not include additional charges associated with the California Global Warming Initiative (AB32). Refer case specific notes for details for modeling of rates based on AB32.

Source/Collection:

The tariff rates were derived from the OASIS rates posted by the applicable transmission owners as compiled by the California Independent System Operator (CAISO).


Hurdle rates are validated through the RAC Data Subcommittee and Modeling Subcommittees. These subcommittees coordinate efforts to consider consolidate areas and regions as necessary to facilitate modeling.

Trading Hubs

Description:

In production cost modeling, the primary purpose of a trading hub is to avoid an unrealistic build-up of hurdle rate charges where large concentrations of generation in one area are committed to serve load



in multiple areas. Operationally, trading hubs are generation free-trading zones with no hurdle-rate barriers.

Trading hubs are also modeled at the region level. There are three trading hubs in the Western Interconnection as depicted in Figure 3: Mead (SW_TH_Mead), Palo Verde (SW_TH_PV), and Malin (NW_TH_Malin).

Figure 3. Trading Hubs

A trading hub typically has the following characteristics:

1. A large concentration of generation resources serving multiple control areas;

2. A cluster of buses where the buses could be owned by utilities of different regions;

3. Absence of hurdle rates when power flows within the cluster of buses; when power is exported out of the trading hub; or when neighboring regions export power to the trading hub;

4. Trading hubs are usually located at the boundaries of multiple regions.

In database modeling, both regions and trading hubs are modeled as regions. The differences are:

When power is exported from a region, hurdle rates apply.

When power is exported from a trading hub, hurdle rates do not apply.


-

Palo Verde

Mead

Malin


Note that power imported into a region or trading hub does not incur hurdle rates. Figure 4 shows regions with a trading hub region interfacing between them.

Figure 4. Representation of three RAC regions interfacing with a trading hub

Figure 5 shows the configuration of the Palo Verde trading hub. In this trading hub, Palo Verde and Hassayampa are two central buses. For generators that are directly connected to the hub, the generation buses are also defined as a part of the trading hub. In addition, Jojoba is also included as a special addition, due to Arizona Public Service Co. (APS), the BA operating these buses, having transmission rights from Jojoba to Hassayampa. The Gila River generation serves APS, but it would be charged twice by hurdle rates if the Jojoba bus were not included in the Palo Verde trading hub: APS-to-SRP and SRP-to-PV when Gila River supplies to APS.

Figure 5. Palo Verde Trading Hub



Figure 6 shows the configuration of the Mead trading hub, which consists of Mead 500-kV, 345-kV, and 230-kV buses and the Hoover Power Plant.

Figure 6. Mead Trading Hub

Figure 7 shows the configuration of the Malin trading hub, which consists of the 500-kV intersection of the Bonneville Power Administration, PacifiCorp West, Balancing Authority of Northern California, and the California Independent System Operator (i.e., NW_BPAT, NW_PACW, CA_BANC, and CA_CISO in the model).

Figure 7. Malin Trading Hub




Allocation of specific BAs to regions is validated through the RAC Data Subcommittee and Modeling Subcommittee. As of the creation of this document, both subcommittees are developing specific protocols describing processes used to validate load area topology. WECC will update this document as the Data and Modeling Subcommittees finalize validation protocols.

Transmission and Generation

Description:

The Data and Modeling Subcommittees are responsible for creating a central database of technical information about the Western Interconnection transmission system, including power flow model (PF) of the Western Interconnection. This power flow model is used as the foundation for resource and transmission in the PCM.

The transmission and generation in PCM is created based on a Power Flow model created within GE’s Positive Sequence Load Flow (PSLF) software through the use of EPCL (*.P) files. Several components of the power flow model must be evaluated carefully to ensure compatibility with the PCM model, including but not limited to WECC transfer paths, generator topology, DC line modeling, and orphaned buses.

Source/Collection:

NERC Reliability Standard MOD-032-1 data submitters, Western Planning and International Planning Regions (WPIPR)

Validation/Approval

RAC Modeling Subcommittee

Modeling Branch Ratings

Description:

PF models the normal and emergency branch (line or transformer) ratings for each of the four seasons within its GE PSLF power flow model as shown below:

PF Branch Rating DescriptionRating 1 Summer NormalRating 2 Summer EmergencyRating 3 Winter NormalRating 4 Winter Emergency



PF Branch Rating DescriptionRating 5 Autumn Normal Rating 6 Autumn EmergencyRating 7 Spring NormalRating 8 Spring Emergency

In comparison, GridView allows the user to model only three ratings for each branch.

PCM Branch Rating DescriptionRate A Normal RatingRate B Long Term Emergency RatingRate C Short Term Emergency Rating

Gridview applies the Rating 3 and 4 to Rate A, B respectively and copies Rate 4 into Rate C. Since GridView only stores one season’s ratings, it uses the winter ratings from PF Heavy summer case and de-rates them for the summer season by defining de-rate multiplier for each of A, B and C rating. The summer season is defined from June 1st to September 30th. Table 2 illustrates how the branch ratings and de-rate multiplier are modeled within GridView so they are consistent with those modeled in the PF.

Table 2. Modeling Branch Ratings in GridView model based on GE PSLF power flow model

GridView BranchRating Type

Rating (MW) Summer De-RateMultiplier

Rating A Rating 3 in PF(Winter Normal)

_(Rating 1 in PF_(Rating 3 in PF)

Rating B Rating 4 in PF(Winter Emergency)


Rating C Rating 4 in PF(Winter Emergency)


Paths and Other Transmission Interfaces

Description:



The WECC Path Rating Catalog is used as the starting point for path ratings. The path ratings are modified to capture operating limits for many key paths and to capture rating changes due to addition of transmission projects in 10-year study period. Any path that had an undefined, unrated, or unstudied ratings, values are set to the negative values. Paths with seasonal limits are applied monthly.

Validation/Approval

RAC Data Subcommittee

Nomograms

Description:

Nomograms are graphical tools used to represent the interactions between multiple constraints affecting the flow of electricity on transmission lines. Nomograms are employed where applicable to enforce limits on the summation or subtraction of groups of branches, transfer paths, resources, or aggregate loads.

Source:

WECC receives nomograms applicable to WECC Paths from WPR

Validation/Approval

RAC Modeling and Data Subcommittee

Monitored Lines

Description:

Monitored lines are the branches (transmission lines or transformers) whose constraints are imposed in the GridView simulation. Only interregional transmission lines are monitored and low-voltage transmission lines are ignored. Thus, the primary criteria for designating monitored lines to include all lines at or above 230 kV and all transformers with a lower-side terminal at or above 230 kV. Lower voltage lines and transformers can be monitored on an as needed basis.

Phase Shifters

Description:

Phase shifters are used in transmission operations to control and balance power flow on parallel transmission lines by shifting the phase of the reactive power. In GridView, the phase shifter modeling settings are tuned to minimize the number of phase angle change operations during the year. The model and tuning is done using current or historical phase shifter operations.



Source:

Data regarding phase shifters characteristic is provided by WPR.

Validation/Approval


Resources

Resource Modeling Categories

Description:

The electric transmission topology is imported from the PF directly into the PCM. However, because the PF does not contain all of the generator information associated with production cost. The generators are mapped from the PF to the PCM, rather than a direct import. This PF dataset is created in coordination with Area Coordinators through the NERC MOD-032-1 Process, “Data for Power Systems Modeling and Analysis”, as described in the WECC Data Preparation Manual (DPM).

The resources are modeled in categories as shown in Table 3 based on their operating characteristics. Table 3 shows resource categories and gives a brief description of the methodologies used to model the different types of resources. Additional details on each resource modeling category is discussed in the Resource Modeling section of this manual.

Table 3. Resource Modeling Categories

Resource Modeling Category

Identification Modeling Methodology

1. Hourly Renewable Wind and Solar Hourly shape based on NREL hourly profiles

2. Hourly Hydro Hydro insensitive to load or price Hourly shape3. PLFHTC3 Hydro Hydro sensitive to load and LMP PLF/HTC4. Pump Storage Pump storage or reversible hydro

facilitiesPumped Storage

5. Dispatchable Thermal Conventional resources, such as gas- and coal-fired

Dispatched if it is cost effective and needed

6. Must Run Thermal Biomass, Biogas, Geothermal, Cogeneration, and Combined Heat and Power (CHP)

Thermal that must run if available, with output typically set to a high minimum value

7. Plant Parts Operationally tied units, typically units within a combined cycle plant

Same as Dispatchable Thermal

3 Proportional Load Following Hydrothermal Co-optimization.



Resource Modeling Category

Identification Modeling Methodology

8. DC Line Power flow resources representing DC lines

Hourly shape based on power flow information and approximations of historical data

9. Motor Load Negative generation representing synchronous pump motor loads

Hourly Shape based on historical data

10. Volt-amperes reactive (VAR) Device

Power flow resources representing VAR support devices

Turned off in the model

11. Off-Line Resources that should be considered off-line (e.g., retired, out-of-service, indefinitely on standby)

Turned off in the model

12. Energy Efficiency/ Demand Response

Loads being reduced to reflect EE and DR

EE: Hourly shape based on area load shapesDR: Hourly shape based on DR forecasts

13. Generic Storage Storage facilities with unknown details, Battery Storage

Pumped Storage

14. Misc Hourly Resources that exist or are planned but whose modeling is uncertain or incomplete

Hourly Shape are turned off in model (i.e., all zeroes)

15. Other Hourly Cogeneration Hourly shape

The resources are further modeled within the dataset as subtypes to summarize the Generator Type and Primary Fuel of each resource. Refer to case release notes for all resource subtypes used in the case.

Validation/Approval

RAC Data Sub Committee

Status and Need Categories

Description:

The following categories of status and need are used to describe the availability of resources included in the ADS PF and PCM when modeling the resources:

Existing: those assumed to be online by start of study period. Status 0 (existing).



Incremental: those assumed to be online during study period. This includes the 1 (Under Con-struction), 2 (Pre-Const Reg Approval-Review), 3 (Future-Planned), and 4 (Future-Conceptual) resource project statuses.

Gap: those added to the dataset to fill any “gaps” complying with state and federal policy or other directives.

The generic “gap resources” are unique to GridView modeling and their use in the model is dependent on a variety of factors including, but not limited to:

Meeting Renewable Portfolio Standard targets;

Satisfying resource adequacy and planning reserve; and

Meeting other state-, area-, and region-specific future goals.

Validation/Approval:


Resource Modeling

Wind and Solar Facilities

Description:

Wind and solar generation are modeled as fixed-shape resources in the ADS PCM case with fixed dispatch cost. Fixed-shape resources can be curtailed if the Locational Marginal Price (LMP) is at or below the dispatch cost. This curtailed energy will be spilled or dumped unless the spillage option is disabled. Refer case specific notes for the value used for dispatch cost.

Hourly Shapes

The National Renewable Energy Laboratory (NREL), as part of the Western Wind Dataset effort, created hourly solar and wind meso-scale shapes for roughly 30,000 sites throughout the Western Interconnection. Each NREL profile in the Western Wind Dataset represents a small generation site (2 km by 2 km) and potential wind and solar capabilities calculated by NREL in that small region. The original data is based on extensive meteorological modeling efforts that result in wind speed or irradiance (in the case of solar) data for the specific region that can then be converted to power output.

The solar and wind shapes represent potential power output at an area no further than a 10-mile radius from the geographical location of the generating station. Each solar and wind generator in the radius has a corresponding specific output shape. WECC partnered with various stakeholder groups, as



well as The Pacific Northwest National Laboratory to complete and validate this modeling enhancement.

Both wind and solar shapes were benchmarked utilizing historic output from the corresponding solar and wind facilities. It should be noted; some wind shapes were found to have unimpededly high capacity factors, particularly in the case of shapes located in California. For these shapes, a 20 miles radius instead of 10 miles was used.

The number of NREL profiles that are aggregated to produce a single profile depends on the capacity of wind/solar within the geographic vicinity for which the shape is being created. Each NREL profile has an associated capacity and enough need to be selected to fulfill the required amount of modeled resource capacity within the target geographic vicinity. For example, to model a 300-MW solar or wind plant in the dataset, 300-MW worth of NREL solar or wind profiles are selected and aggregated. The process for creating solar and wind aggregate shapes is the same for both solar and wind profiles. The solar shapes are developed using NREL’s 2009 SIND Toolkit data. The wind shapes are developed using NREL’s 2009 WIND Toolkit data. Due to an assumed inverter loading ratio of 1:1 in the raw NREL data, the aggregated output shape typically only reached 80% of its capacity. To mitigate this issue, a multiplier was applied to each shape to enable the shapes max out at 100%, and in the event of over-statement, be “clipped” at 1 per unit.

Capacity Factors

The net capacity factor is the unitless ratio of an actual electrical energy output over a given period of time to the maximum possible electrical energy output over that period.

In the dataset, there are two different types of profiles used each with it’s own capacity factor: planned and existing. Planned profiles are used to represent wind farms that are not currently “in the ground” or under construction and they are created using WREZ-expected generation data collected by Black & Veatch. Existing profiles are used to represent plants that are currently in operation or under construction.

Source:

The wind and solar profiles are developed by NREL.

Validation/Approval

Wind and Solar hourly profiles and capacity factors are validated through the RAC Production Cost Modeling Data Work Group (PDWG) and approved by RAC Data Subcommittee.

Distributed Generation (DG) Facilities

Description:


https://en.wikipedia.org/wiki/Unitless


The definition of DG here refers to photovoltaic (PV) generation and includes two parts:

Behind-the-meter (BTM) DG – small-scale solar PV installations that individual customers would install to avoid purchasing electricity from an electric utility.

Wholesale DG – PV systems that are connected directly to the electric distribution network and sell the electricity on the wholesale market, typically 1–20 MW and often procured to meet state DG targets.

In creating the ADS PCM data set, WECC assumes that the distributed generation is not included in the L&R load forecasts and DG is modeled as a resource in the dataset. In the dataset, it is treated as “fixed rooftop” solar PV profiles and modeled as fixed-shape resource.

Source:

Behind-the-meter DG is provided by estimates developed by Energy + Environmental Economics (E3) and Lawrence Berkeley National Laboratory (LBNL). The forecast developed is validated against EIA 861 and modified to reflect current policies and utility Integrated Resource Plan (IRP) plans. For California assumptions developed by the CEC in their load forecast is used. Wholesale DG is provided by load and resource submittals, the EIA and IRPs and validated through the generator reconciliation effort.

Validation/Approval


Hydroelectric Facilities

Description:

Hydro generation is a significant resource in the Western Interconnection. Hydro generation is modeled using a variety of methods that attempt to capture the unique operating characteristics of the resource. A combination of fixed hourly shapes based on historical time series, a hydrothermal co-optimization (HTC) technique, and proportional load following (PLF) algorithms were used to model hydro generation for the ADS PCM. Hydro dispatchability constraints due to environmental or other operational factors (e.g., irrigation water deliveries, flood control, environmental release) were captured in the model using minimum and maximum operating levels, monthly energy limits, monthly load proportionality constants (K values), and monthly hydrothermal co-optimization fractions (p factors), when applicable.

The initial modeling parameters were determined on a hydro plant level 4and spread into regions. In all regions, plants were categorized as large (>= 10-MW capacity) or small (< 10-MW capacity). The exception to this was in California, which had a special Renewable Portfolio Standard (RPS) category for

4 A hydro plant may have more than one hydro units



plants with capacities from 10 MW through 30 MW. Plants smaller than 10-MW capacity were rolled up with and modeled as a PLF K=0 large plant.

The plant-level modeling was then spread to unit-level modeling. The hourly shapes and energy targets were spread proportionally based on the nameplate of the units in each plant. PLF and HTC hydro units were assigned the same K values and p factors as their plants because these modeling parameters are measures of responsiveness to load levels and locational marginal prices (LMP) rather than parameters that depend on unit or plant size.

The PLF/HTC modeling methods were used to model most of the hydro generation in the dataset. PLF constants were obtained by regressing historical data and loads for federal projects, or were supplied by plant operators for non-federal projects. Monthly average generation values for both HTC and PLF plants came from the EIA 906/920 data. Smaller plants were modeled using estimated PLF constants and EIA 906/920 generation values.

Plants determined to not follow load historically were modeled using historical hourly shapes. Plants with nameplate capacities of less than 10 MW were rolled up into “state” plants with summed monthly EIA averages; these state “plants” were modeled using PLF K=0 (flat monthly generation).

California small hydro was disaggregated from the conventional hydro to more accurately track its contribution to RPS requirements (this includes plants from 10- through 30-MW capacity).

BC Hydro generation data are determined by BC Hydro’s Generalized Optimization Model using load forecast and average inflows. Generalized Optimization Model results are used to calculate PLF constants for use by the HTC modeling method.

A dispatch cost was applied to projects on the Columbia River that cannot be curtailed. Refer case specific notes for the dispatch cost.

Modeling Hydroelectric Ramp Rates

Many hydroelectric units are technically capable of extremely quick ramping, able to go from zero to full output in as little as 15 minutes; however, many hydroelectric facilities are limited by environmental water usage restrictions (e.g., allowing for fish migration). RAC data subcommittee derived Hydro unit ramp from California ISO Long Term Procurement Plan (LTPP) Study.

Modeling Hydroelectric Reserve Contributions

All hydro plants and their units are limited in their reserve contribution per the following criteria:

If there is only one unit in the plant, then the unit’s contribution to reserves is limited to 50 per-cent of its capacity.



If there are multiple units in the plant, then the proportion of each unit’s capacity that is counted toward reserves is limited to the reciprocal of the number of units in the plant (e.g., 1/5 of each unit’s capacity for plants with five units) or 15 percent of the plant’s total capacity, whichever is greater.

Source:

Monthly average generation values for HTC and PLF plants are obtained from EIA906/920 data. Cali-fornia ISO LTPP study is the basis for hydro unit’s ramps.

Validation/Approval


Pumping Loads

Description:

Pumping load units and plants are modeled as negative generation and associated reductions are applied to the L&R load forecast. The plants modeled with hourly shapes are either missing information regarding the plants’ operational practices or their operation rarely changes from year to year. The primary goal of the modeling is to emulate the historical capabilities of the facilities – i.e., meeting or exceeding their historical power consumptions. Thus, the historical hourly shapes are used.

Source:

The hydro hourly shapes are provided by California ISO

Validation/Approval


Pumped Storage

Description:

Pumped storage facilities were modeled using different modeling techniques based on the facilities operating characteristics in the dataset. Refer case specific notes for detailed on modeling techniques for facilities.

Source:

The source of pumped storage data is Columbia-Grid which in turn assembles the data from various public resources.



Validation/Approval


Battery Storage

Description:

Battery storage has two operating modes:

charge and

discharge.

Battery storage operates like load during charge mode requiring defining operating characteristics: charge rate, charge cost, maximum charge energy and charge efficiency. During discharge mode, battery storage behaves like a thermal unit which requires operating characteristics maximum energy output, maximum discharge hour, discharge cost and discharge rate.

Gridview versions lower than 10.2 did not have Battery Storage modeling functionality. In the cases developed on version lower than 10.2 (including cases that were later released in version 10.2), the battery storage units are molded as Pumped storage facilities in these cases. Refer case specific release notes for further details.

Validation/Approval


Thermal Generation Facilities

The operating parameters for the thermal generation were derived from several sources and are listed in Table 4.

Table 4: Thermal Operating Parameters

Parameter Unit Type Source DescriptionMaximum5 Capacity

Coal-fired Steam

EIA / LRS / PF Closest consensus from the common sources including seasonal ratings

Other Dispatchable

EIA / LRS / PF Closest consensus from the common sources including seasonal ratings

Must-run – California

CAISO NQC Used monthly NQC values

Must-run - Other

LRS

Minimum6 Coal-fired Columbia Grid Based on Heat Rates provided by Columbia Grid

5 Not to exceed values in reference PF case



Parameter Unit Type Source DescriptionCapacity Steam

Other Dispatchable

Columbia Grid Based on Heat Rates provided by Columbia Grid

Must-run EIA Based on the average monthly outputs reported in EIA 923

Must-run No EIA Based on averages for similar types from units with EIA data

Heat Rates All Thermal Stakeholders Calculated by CEC and Columbia GridRamping Uptime / Downtime

All Thermal CAISO CAISO used numbers from ISO’s LTPP testimony

Monthly Minimum and De-Rated Maximum Capacity

Table 5 illustrates how the monthly minimum and de-rated maximum capacities were determined for the different thermal generation facilities.

Table 5. Determining Monthly Minimum and De-Rated Maximum Capacity

Resource Model-ing Category

Location ResourceProject Status

Minimum Ca-pacityBased on….(direct link or per similargenerator type)

De-Rated Maximum Capac-ityBased on….(direct link or per similargenerator type)

Dispatchable Thermal

California 0, 1, or 2(Existing through

Pre-Construc-tion)

2024 ADS casemodeling

Balancing Authorities' WECCLoad and Resource Submittal

Non-California

Must Run Ther-mal

California Average of EIAhistorical dis-

patch

CAISO Net Qualifying Capac-ity (NQC)

Non-California Balancing Authorities' WECCLoad and Resource Submittal

Any 3 or 4(Planned/

Conceptual)

84% of Name-plate

85% of Nameplate

Thermal Economic Assumptions

Heat rates: Calculated by CEC and Columbia Grid from Continuous Emission Monitoring System Continuous Emission Monitoring System (CEMS) data vetted by PDWG

Startup Fuel: Derived from Intertek/APTECH data6 Not to exceed values in reference PF case.



Startup Cost: Derived from Intertek/APTECH data

Var. O&M cost: Derived from Intertek/APTECH data

Ramping costs: Calculated by CAISO

Thermal Outages and Planned Maintenance

Forced outages and scheduled maintenance on coal-fired plants can have a significant impact on congestion, especially when multiple plant outages occur at the same time. RAC data subcommittee uses CEMS data to calculate forced outage rates (FOR) and maintenance rates for coal-fired plants. FORs and scheduled maintenance can vary widely on coal-fired plants. CEMS data provides hourly generator output. Coal-Fired plants are typically "Baseload” and operate all the time, except for maintenance and forced outages. Hours with zero output are considered either scheduled maintenance or forced outages.

Planned Maintenance for most units is scheduled using the Maintenance Scheduler tool built into GridView that schedules the maintenance for each region based on periods of lower loads. The tool allows for predefining maintenance and this option was used to schedule the nuclear refueling outages and a few of the large base-load generators.

Source/Collection:

CEMS data is used to calculate FOR and maintenance rates for coal-fired plants.

Validation/Approval


Co-generation

Description:

Based on the operating characteristics, co-generation units are modeled either as thermal units or hourly resources. Co-generation is modeled as hourly resource when it has been identified to strictly operate for business needs and not for meeting electric demand. Refer case specific note for details on methodology used for a co-generation unit.

Source:

The information regarding the operating characteristics of a co-generation is provided by WPR.

Validation/Approval

PDWG is responsible for validating the modeling assumptions a co-generation unit.



VAR Devices

Description:

VAR devices do not affect the results of PCM simulations as PCM simulations use an optimized DC power flow solution rather than the full AC solution performed by power flow modeling software. However, they do come into play when hours of the PCM simulation are exported into the power flow model and are retained in PCM dataset.

Remote Resources

Description:

Remote resources are defined as resources owned or contracted outside a BA or LSE’s boundaries. Thus, it is necessary to associate remotely owned (or contracted) resources with the participating areas or regions correctly. Modeling of remote resources in GridView requires counting the generation shares for reserves and to deliver the associated energy with no hurdle rate charge (assumes that delivery cost is a fixed cost). The list of remote generators that are modeled in the dataset is dynamic and dependent on stakeholder input. Refer to case specific notes for a list of remote resources with associated area or regions

Validation/Approval


Load Data

Hourly Loads:

Description:

In PCM, loads are modeled as hourly loads for the entire year. Load and Resource (L&R) monthly energy (GWh) and peak (MW) forecast for years 1-10 are submitted to WECC by BAA through NERC Reliability Assessment Subcommittee (RAS). L&R combines Load Serving Entities (LSE) forecasts inside each respective BA and BA in turn provides the forecast to WECC. L&R loads are consistent with the NERC Standard, MOD-032-2 and includes year-to-year annual growth rates.

WECC uses GridView to develop the hourly forecast, starting with the L&R’s monthly peak and energy and applying FERC 714 for the latest year available to break the monthly down to hourly. The steps to develop the forecast is as follows:



1. Start with the monthly peak and energy forecast, submitted to L&R. 2. Correct for Energy Efficiencies (EE) discrepancy 3. Develop assumptions for Load Modifiers and work with relevant parties to agree on what should be

included and where (e.g., distribution of Area EE, DR to the bus; how and where to model BTM-PV_ associated NREL shapes etc.)

4. Back out Pumping Loads from the forecast and model them as resources. 5. Any other relevant adjustments as needed.

Source/Collection:

Load and resource data collected in NERC Reliability Assessment Subcommittee (RAS) L&R process is collected from Balancing Authorities.

Validation/Approval:

PDWG will validate the area hourly forecast to identify and address any anomalies.

Adjustments to (L&R) Data

Description:

This section describes various adjustments required to L&R data collected by WECC. WECC in collaboration with PDWG and WPR is responsible in making these adjustments to the forecast.

L&R Data Extrapolation

Some Balancing Areas’ loads are forecasted only for Dec through March. The months of April-December must be extrapolated to create a full year load forecast, so the missing data is extrapolated using a 3rd degree polynomial fit from the monthly forecasts provided.

Energy Efficiency Adjustments

The loads are adjusted for Energy Efficiency (EE) savings. L&R data submitted by the BAs are reviewed by the Lawrence Berkeley National Laboratory (LBNL) for consistency. These adjustments are subtracted from the BA load energy and peak

Behind the Meter Photovoltaic (PV) and Distributed Generation (DG) Adjustments

BTM-PV is modeled as generator in the dataset and requires that this load be removed from the BA load forecast if included in it.



Pumping Loads

The individual BAs included pumping loads in their L&R information data submittals. These pumps are modeled as generators in the dataset that have both a positive and negative output. Modeling pumping load as a generator requires that the pumping loads be removed from the BA load forecast. Historical pumping data is used to create the reductions in peak and energy.

Demand Response

Demand Response (DR) is defined as customer reduction in electricity usage, such that the customer’s normal consumption pattern is reduced in response to price changes or incentive payments designed to lower electricity use at times of system stress or high market prices.

Demand Response is modeled as an hourly resource that is fed directly into the model.

WECC uses the LBNL Dispatch Tool to develop the hourly DR profiles. The tool requires three user-defined inputs:

maximum monthly DR capacity for each (non-interruptible) DR program type and BA;

hourly energy load for each BA; and

hourly locational marginal prices (LMP) for each BA from GridView.

The Demand Side Management (DSM) related assumptions are developed as a collaborative process between LBNL, WECC and the State and Provincial Steering Committee (SPSC). Two types of demand response (DR) modeling assumptions are required.

DR resource quantities: How much DR is available to be dispatched in any given hour for each load zone

DR dispatch mechanics: When is the DR dispatched and how does it affect hourly loads and peak demand

DR resource quantities are based on non-firm load forecasts reported by balancing authorities to WECC. There are four categories of non-firm load: Interruptible, Direct load control, Pricing and Load as a capacity resource. LBNL compares and validate non-firm load forecasts against utility IRPs, regulatory filings and other public sources and if necessary confirms the adjustments with utility itself. The process yields an adjusted non-firm load forecast.

The goal is to realistically model DR resources within the constraints of WECC’s production cost model. The DR program used for reliability and economic purposes are limited by tariff provisions specifying maximum number of events per month or year. Tariffs also specify multiple, sequential blocks (e.g., 4 to 6 hours) for events. The Approach and assumptions are vetted with WECC PCM Modeling Work Group (PMWG). Following flow chart shows the modeling mechanism of LBNL’s DR dispatch tool.



Station Service Loads

A power plant’s station service load consists of all demand within the power plant facility—i.e., local to the facility’s generators. The station service loads modeled in the power flow case are set to zero. This assumes the L&R load forecasts do not include station service loads and monthly generator capacity de-rates account for station service in all seasons.

Seasonal Bus Distribution

GridView uses the initial power flow case load distribution to determine the transmission topology and to determine the load distribution over which to spread the area-level loads to busses on the system. This distribution can be changed seasonally by applying the bus distribution of those seasonal power flow cases.

Costs and Economics

Gas Topology and Pricing

Description:

RAC subcommittee is responsible for The NG price burner-tip forecasts based on a hybrid model that derives the annual average prices from the CEC North American Market Gas Trade (NAMGas) model and the monthly shapes from the Northwest Power and Conservation Council (NPCC) model. The two models differ in that the council’s model is a regression model that reflects historic usage and accounts for forward-looking factors (e.g., LNG development, Pipe expansions, etc.) exogenously. In contrast, the CEC model, “NAMGas”, starts with the World Gas Trade Model (WGTM) and applies the following changes:

• Reconfigured the California portion of the model to suit the Energy Commission needs



• Removed all non-North American structure

• Added functional nodes to account for approved Liquefied Natural Gas (LNG) imports and exports

• Added nodes needed to represent natural gas demand in the transportation sector

RAC data subcommittee further determines Natural Gas (NG) pricing zones for the PCM dataset. Each gas-fueled generator is assigned one NG fuel zone. Refer to case specific notes for these zones.

Source/Collection:

RAC data subcommittee drives annual average prices using a hybrid mode of CEC annual burner tip prices and NPC monthly shapes.

Validation/Approval


Coal Topology and Pricing

Description:

Coal prices are derived from public domain information (e.g. CEC, NPCC, IRPs etc.). RAC data subcommittee works with these entities to develop a price forecast for 10 and 20 year planning horizons. RAC data subcommittee further determines Coal pricing zones for the PCM dataset. Each coal-fueled generator is assigned one coal fuel zone in the dataset. Refer to case specific notes for these zones.

Source/Collection :

Coal prices used are from California Energy Commission (CEC), EIA860

Validation/Approval


Other Fuels and Pricing

Description:

In addition to the pricing for NG and Coal, prices for other fuels are modeled as a specific fuel type zone in the dataset by RAC data subcommittee. Each alternate fuel type generator is assigned one fuel zone. Refer to case specific notes for these zones.

Source/Collection:



Other fuel prices used are from EIA and NPCC model.

Validation/Approval


Emissions Rates by Fuel

Each fuel is modeled with emissions rates for CO2, NOx, and SO2. RAC data subcommittee collects these rates from various public domains (e.g. CEC, NPCC, CPUC, IRPs, EPA etc.) and develops a methodology to derive a price for these emission rate. Refer to a specific case notes for emission prices used for that case.

Validation/Approval


Reference Year for Cost Data

Description:

Cost data such as fuel prices, variable Operations and Maintenance (O&M) rates, and startup costs are often provided in different year’s dollars and require conversion to a selected base year dollars. These conversions are based on the Moody’s GDP Inflator/Deflator series, licensed to the CEC. Refer to case specific notes for base year used for that case.

Source/Collection:

RAC data subcommittee is responsible for determining the base year.

Validation/Approval


Inflation Rate

RAC data subcommittee makes a determination regarding inflation rates to be used for the 10-year planning period. CEC current GDP deflator series developed using Moddy Analytics data has been used for inflation rates for the planning period.

Source/Collection:

CEC GDP deflator series was used for inflation rates.

Validation/Approval




Retirement and Extended Outage Assumptions

Description:

Retirement Schedule

Information derived from the LRS data submittals, utility IRP postings, state/federal databases, stakeholders, and other sources is used by RAC data subcommittee to develop generation retirement schedules. Ongoing work to identify likely retirements will be important because some generation that is assumed to be available in models will likely be retired for economic or environmental reasons within the planning horizon. Failure to capture these retirements may distort the system dispatch. Table 6 shows the assumptions for all resources with unknown commission and retirement dates. Refer case specific notes for assumptions used in dataset.

Table 6. Assumptions for unknown commission and retirement dates

Resource Project Status Definition0 Existing1 Under Construction2 Pre-Construction3 Future-Planned)4 Future-Conceptual5 No Longer Expected

Retired

Renewable Portfolio Standard and Resource Adequacy

Renewable Portfolio Standard (RPS) Compliance Check

Description:

The RPS compliance check verifies if the resource assumptions fulfill all applicable state RPS goals, both voluntary and required. RPS targets are embedded in PF case and are brought into PCM case from PF case. Refer case specific notes for more details on state RPS goals.

Validation/Approval




Resource Adequacy (RA) Check

Description:

The resource adequacy check is performed on the “pool” level, which is comprised of aggregates of the regions that correspond to the granularity of the planning reserve. The resource adequacy check is a measure of each pool’s ability to meet its peak load with its internal resource capacity and transmission-constrained imports from neighboring pools. The check is used as a way of identifying pools in the dataset that have the potential for supply shortages based on load, generation and transmission inputs.

Reliability modeling has a long history in electric sector resource planning. Loss of load probability ‐ ‐(LOLP) modeling, a modeling framework in which the availability of generation resources is compared against potential system load across a broad range of possible conditions, has been established as the industry standard. As tolerance for loss of load due to generation inadequacy is typically very low, a common standard is “one day in ten years”. Reserve margins are planned such that the expected frequency of firm load curtailment due to inadequate capacity resources does not exceed one event in ten years. Such an approach is necessary to capture the tails of the distribution during which loss of load may occur as shown in Figure 8. WECC has contracted with E3 to conduct a LOLP analysis of the resources using their RECAP tool.

Figure 8. Loss of Load Probability Modeling Framework.



Advanced Modeling

Reserve Modeling

Description:

Reserve is additional capacity (generation and responsive load availability) above that needed to meet actual load demands so that it can be called on to assist if load increases or generation decreases, due to unpredictability or variability of the conditions on power systems. Most power systems are designed so that, under normal conditions, the operating reserve is always at least the capacity of the largest generator plus a fraction of the peak load. Operating reserve is a crucial concept for ensuring that the day-ahead planning of generators' schedule can withstand the uncertainty due to unforeseen variations in the load profile or equipment (generators, transformers, transmission links) faults.

In PCM, Reserves are modeled using three grouping tiers:

Load Areas;

Regions; and

Combined Areas and Regions.

Within the Combined Areas and Regions tier, there are groupings of Reserve Sharing Groups (RSG) that define the more complex reserve requirements (i.e., those that allow several ways in which portions of the Western Interconnection can share resource capacity to ensure reliability of the system). The modeling reflects the new FERC Order 789.

Validation/Approval


Flexibility Reserve Modeling

Description:

Flexibility reserves are defined as the additional reserves required to manage the variability and uncertainty associated with variable generation resources like wind and solar. Given the high penetration of variable generation in the West, including this additional reserve requirement is an important assumption for the PCM studies. The process uses historical load, wind and solar data to derive equations that predict the variability based on statistical analysis of that data and set aside reserve resources to be able to meet fluctuations in availability due to resource intermittency.

Flexibility reserves have an hourly dispatch with an operating reserve requirement. The spinning and non-spinning reserve requirements (specified as a percent of daily peak load) are combined


https://en.wikipedia.org/wiki/Load_profile

https://en.wikipedia.org/wiki/Unit_commitment_problem_in_electrical_power_production

https://en.wikipedia.org/wiki/Peak_load


with the predefined hourly flexibility reserve to create a composite hourly reserve requirement, as shown in Figure 9. The hourly dispatch of the flexibility reserves was created with ABB’s flex reserve tool.

Figure 9. Composite Hourly Reserve Requirement

Validation/Approval




Disclaimer

WECC receives data used in its analyses from a wide variety of sources. WECC strives to obtain its data from reliable entities and undertakes reasonable efforts to validate the accuracy of the data used. WECC believes the data contained herein and used in its analyses is accurate and reliable. However, WECC disclaims any and all representations, guarantees, warranties, and liability for the information contained herein and any use thereof. Persons who use and rely on the information contained herein do so at their own risk.



Glossary of Terms and Acronyms

Acronym Term Definition

BA Balancing Authority The responsible entity that integrates resource plans ahead of time, maintains load-interchange-generation balance within a Balancing Authority Area, and supports Interconnection frequency in real time.

BTM Behind the Meter An energy generating facility that produces power intended for on-site use in a home, office building, or other commercial facility and that is on the customer’s side of the electric meter.

CEMS Continuous Emission Monitoring System

Used as a tool to monitor flue gas for oxygen, carbon monoxide and carbon dioxide to provide information for combustion control. They are currently used as a means to comply with air emission standards.

DR Demand Response Customer reduction in electricity usage in response to price changes or incentive payments such that the reduction differs from the customer’s normal consumption patterns. DR is designed to lower electricity use at times of system stress or high market prices.

DSM Demand Side Management

PDWG PCM Data Work Group A work group under RAC that is responsible for collecting and verifying data used in the RAC database.

DG Distributed Generation Generation that is consumed near the point of generation rather than being transmitted to a remote load.

EIA Energy Information Agency

A principal agency of the U.S. Federal Statistical System responsible for collecting, analyzing, and disseminating energy information to promote sound policymaking, efficient markets, and public understanding of energy and its interaction with the economy and the environment.

FERC Federal Energy Regulatory Commission

FERC is a United States government agency, established in 1977 to oversee the country's interstate transmission and pricing of a variety of energy resources, including


https://en.wikipedia.org/wiki/Emission_standard

https://en.wikipedia.org/wiki/Combustion

https://en.wikipedia.org/wiki/Carbon_dioxide

https://en.wikipedia.org/wiki/Carbon_monoxide

https://en.wikipedia.org/wiki/Carbon_monoxide

https://en.wikipedia.org/wiki/Oxygen

https://en.wikipedia.org/wiki/Flue_gas



electricity, natural gas and oil.

Hurdle Rate A representation of the cost for transporting power over transmission lines that attempts to align the imperfection of real dispatch with the perfect foresight of model-generated dispatch.

IRP Integrated Resource Plan A comprehensive decision support tool and road map for meeting a company's objective of providing reliable and least-cost electric service to all of its customers while addressing the substantial risks and uncertainties inherent in the electric utility business.

IPR International Planning Region

A planning region in which a designated entity is responsible for creating annual or biennial transmission plans. There are two IPRs in the Western Interconnection: Alberta Electric System Operator (AESO) and the British Columbia Coordinated Planning Group (BCCPG).

LSE Load-Serving Entity An entity that provides electric service to end-users and wholesale customers.

MOD-032 MOD-032 A procedure administered by NERC that establishes consistent modeling data requirements and reporting procedures for development of planning horizon cases necessary to support analysis of the reliability of the interconnected transmission system

NERC North American Electric Reliability Corporation

A not-for-profit international regulatory authority whose mission is to assure the reliability and security of the bulk power system in North America.

PV Photovoltaic A method for generating electric power by using solar cells to convert energy from the sun into a flow of electrons.

PSA Power Supply Assessment An evaluation of generation resource reserve margins for the WECC summer and winter peak hours for the forecast period.

PCM Production Cost Model A modeling tool that dispatches available resources to




meet specified load for each of the hours in a year.

PLF/HTC Proportional Load Following Hydrothermal Co-optimization

A hydro modeling method for improving hourly hydro-generation time series representations in transmission planning studies.

RPS Renewable Portfolio Standard

A regulation that requires the use of energy from renewable energy sources, such as wind, solar, biomass, and geothermal, to meet a specified portion of a state’s electricity requirements.

Load Areas Topology based on large load centers. Analogous to Balancing Authority boundaries or Load Serving Entity boundaries.

Regions Load areas defined at operational level to consolidate operational aspects associated with a BA such as hurdle rates and reserves.

Trading Hub Operational region with generation free trading zones and no hurdle rate barriers.

WPR Western Planning Region A region with reporting obligations defined by FERC Order 1000. The four WPRs within the Western Interconnection are: the California Independent System Operator (CISO), ColumbiaGrid, Norther Tier Transmission Group (NTTG) and WestConnect.


https://en.wikipedia.org/wiki/Geothermal_power

https://en.wikipedia.org/wiki/Biomass

https://en.wikipedia.org/wiki/Solar_power

https://en.wikipedia.org/wiki/Wind_energy

https://en.wikipedia.org/wiki/Renewable_resource

https://en.wikipedia.org/wiki/Energy

https://en.wikipedia.org/wiki/Regulation

ADS DDVM V1.0 DDVM V1.0.docx · Web viewThe load projection used in the model is typically 1 in 2...

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