DynamicMonitoringandDecisionSystems(DYMONDS)forSustainableEnergyServices

MarijaIlic,ProfessorandSRCSmartGridResearchCenterDirector,milic@ece.cmu.edu

Acknowledgment Electric Energy Systems Group (EESG) http://www.eesg.ece.cmu.edu

  A multi-disciplinary group of researchers from across Carnegie Mellon with common interest in electric energy.

  Truly integrated education and research   Interests range across technical, policy, sensing,

communications, computing and much more; emphasis on systems aspects of the changing industry, model-based simulations and decision making/control for predictable performance.

The Energy Research Initiative Vision

3

ERI

Smart Grid1 Photovoltaics2

1: Center for Smart Grid Research at Carnegie Mellon University 2: Center for Photovoltaics Research at Purdue University

3: Potential Future Center in Energy Storage TBD

Energy Storage3

Enabling Integration of Renewable Energy Resources on a Secure, Reliable and Optimized Smart Grid

Smart Grid Research Center Carnegie Mellon University

  Multi-university research collaboration in technologies to enable Smart Grid directed by Carnegie Mellon University

  Initial focus on real-time modeling/simulation and software to control and optimize the Smart Grid and ensure security, reliability and availability of the electricity network with significant renewable energy resources   Leverages CMU expertise in software development, network security and

control systems as well as CMU culture of collaboration across engineering, computer science, public policy and business

  Specific projects to be decided by industry sponsors; initial research thrust areas include:   Smart Grid Simulator for Sustainable Services: Modeling, Analysis,

Simulations and Decision Making Methods   Demand-Side Management in Smart Grids   Transmission and Distribution Management in Smart Grids   Secure Data Management and Mining: Model Validation; Large-Scale Novel

Computing for Smart Grids   New Policy Paradigms

4

Outline

  The boom and busts of energy systems education and research in the US universities

  Major once in a lifetime opportunity for innovation   Electric energy systems as enablers of

sustainable services   Relating Socio-Ecological Systems (SES)

concepts and the role of systems thinking   Some examples of new modeling and control

challenges   The critical need to build on the existing

knowledge

Many boom-and-bust cycles in the US electric energy education

  Boom #1 : The biggest contribution of the 20th century –electrification   Well established programs (even entire departments on electric

power engineering—RPI).   Bust #1: Closing of power engineering programs and labs at leading

universities; education and research on life support.   Boom #2: Restructuring of electric power industry--- economics,

policy disciplines gain recognition. Engineering knowledge assumed.

  Bust #2: Restructuring problems –markets ``not working’’—they never were designed nor implemented to support physics of electric power grids.

  Boom #3: Energy and environment emerge as key social goals. Young minds very excited and motivated to make the vision a reality.

  Bust#3--??? The biggest danger--- overwhelming complexity; change driven by technology breakthroughs, social drivers. A very real danger of not meeting the expectations.

 

State of electric energy systems programs

  Must educate the next generation work force   Must do so in the context of, and centered in,

Electrical and Computer Engineering (ECE)   Must integrate ECE with other academic disciplines   Must also address non-technical issues (policy,

economics)   Recent awareness of an educational void, and a

sense of urgency to innovate and integrate electric energy systems education, into existing curricula

The burden on new leaders   Rethink how to plan, rebuild and operate an

infrastructure which has been turned upside-down from what it used to be

  Leaders must understand   3ϕ physics (the basic foundations)   Modeling of complex systems (architecture-dependent models,

components and their interactions, performance objectives)   Dependence of models on sensors and actuators; design for

desired system performance (defined by economic policy and engineering specifications)

  Numerical methods and algorithms   IT

Smart Grids: Massive Systems Integration Opportunity and Challenge

  NaGonalscaleintegraGonofcoordinatedcomplexsystems: - energysystem(powergrid,powerelectronics,energyresources)‐communica7onsystem(hardwareandprotocols)

‐controlsystem(algorithmsandmassivecomputa7on)‐economicandpolicysystem(regula7onands7mulus)

  Why?On‐lineITenables:‐20%increaseineconomicefficiency(FERCes7mate)‐cost‐effec7veintegra7onofrenewablesandreduc7onofemissions‐differen7atedQualityofServicewithoutsacrificingbasicreliability‐seamlesspreven7onofblackouts‐expandingtheinfrastructure(genera7on,T&D,demandside)formaximumbenefitandminimumintrusion   Who? ‐Hugeintellectualchallenge–mustbeuniversityled(onceinalife7meopportunity)‐industrialpartnersincludeleadingtechnologistsinallfoursystems

‐governmentpartnersmustincludeFERC,NERC,NARUC,DoE,NSF

BringingtheelectronicrevoluGontoenergysystems

Electronictechnologyhasfundamentallyalteredthewaywelive;

  Communica7ons  Commerce  Entertainment …

Whathasbeenthekeytothesechanges?

InformaGonTechnology  IntegratedcircuitshavedevelopedinamannerthatprovidesevergrowinginformaGonhandlingpowerateverdecreasingcost

 KeytothedevelopmentofthistechnologyanditswideadopGon:  theabilitytomodelanddesignthesesystems  theassociateddevelopmentofso4waresystems

+

ApplicaGontoPowerSystems ThecreaGonof“smartgrids”istheapplicaGonofinformaGontechnologytothepowersystemwhilecouplingthiswithanunderstandingofthebusinessandregulatoryenvironment

 CriGcaltothecreaGonof“smartgrids”is;  developmentofmodelsofthepowersystem  developmentofcommandandcontrolsoSware  incorpora7onofsecurity,communica7ons,andsafetysystems

  BEFOREhardwareisdeployed! OurMainApproach‐‐DynamicMonitoringandDecisionSystems(DYMONDS)

DYMONDS‐EnabledPhysicalGrid

Requires:• SoSwaremodels• Control• Security• Sensors• Communica7ons• …

CreaGonof“SmartGrids”

  CleardefiniGonofwhat“SmartGrids”means  Deepunderstandingofthecomplexityofthepowersystem  Abilitytonotsimplyintroduce/developtechnologybuttounderstandtheeffectsofchanges

  Ensure,upfront,security,efficiency,reliability,andintegraGonwithbusiness/regulatoryenvironment

  CreaGngflexibilityandempoweringalllevelsfromproducerstoconsumers

NewDYMONDSFuncGonaliGes

  Just‐in‐Time(JIT)‐‐predicGons;dynamiclook‐aheaddecisionmaking

  Just‐in‐Place(JIP)‐‐distributed,interacGve,mulG‐layered

  Just‐in‐Context(JIC)‐‐‐‐performanceobjecGvesfuncGonoforganizaGonalrules,rights,andresponsibiliGes(3Rs)andsystemcondiGons.

  Sampleexamplesofimprovedperformance—on‐goingworkinEESG(hap://www.eesg.ece.cmu.edu)

Key technical challenges   Establish new modeling paradigms---models driven by

sensing, communications and decision making/automation   Using these new models introduce next generation

dispatch/unit commitment methods and algorithms better suited to manage intermittency (demand active decision maker; topology switching for efficiency)

  Ensure short-term stabilization using on-line sensing and adaptation (for the first time PMUs being deployed in large amounts); renewal of high gain control using power electronics switching; new models;

  Revisit Automatic Generation Control, Automatic Voltage and Flow Control to include the potential of PMU measurements and WAMS-based regulation; demand included as decision variable

Transformational change in objectives of future energy systems

Today’s Transmission Grid Tomorrow’s Transmission Grid

Deliver supply to meet given demand Deliver power to support supply and demand schedules in which both supply and demand have costs assigned

Deliver power assuming a predefined tariff

Deliver electricity at QoS determined by the customers willingness to pay

Deliver power subject to predefined CO2 constraint

Deliver power defined by users’ willingness to pay for CO2

Deliver supply and demand subject to transmission congestion

Schedule supply, demand and transmission capacity (supply, demand and transmission costs assigned); transmission at value

Use storage to balance fast varying supply and demand

Build storage according to customers willingness to pay for being connected to a stable grid

Build new transmission lines for forecast demand

Build new transmission lines to serve customers according to their ex ante (longer-term) contracts for service

DYMONDS Simulator Scenario 2: + Price-responsive demand

[3-5]

8

 Elastic demand that responds to time-varying prices

J.Y.JookWh

$

Example1:AdapGveLoadManagement‐‐scheduling

Secondarylevel

Primarylevel

Ter5arylevel

Demandfunc<on End‐userrate

End‐user

LoadaggregatorI

Bidfunc<on

Marketprice

y(λ)

λ

x(λI) λI

LoadaggregatorII LoadaggregatorIII

…

…

DYMONDS Simulator Scenario 1: + Wind generation [3,4]

 20% / 50% penetration to the system

6

LeXie

Example2:WindpredicGon,look‐aheadmanagementusingstorage

ComparetheoutcomeofEDfromboththecentralizedanddistributedMPCapproaches.

IntegraGng>50%Wind

DYMONDS Simulator Scenario 3: + Electric vehicles [6]

10

 Interchange supply / demand mode by time-varying prices

NiklasRotering

Example3:OpGmalControlofPlug‐in‐ElectricVehicles:Fastvs.Smart

24

InformaGonflowforintegraGngPHEVs

26

DYMONDS Simulator Scenario 5: + PMU-Based Robust Control [7]

ZhijianLiu

P

P

  Automated Voltage Control (AVC) and Automated Flow Control (AFC)   Design Best

Locations of PMUs   Design Feedback

Control Gains

P

P

TwoConvenGonalGenerators

Hydroelectric Generator

Dynamic Equations: Dynamic states for Hydro Generator

x1 = [ωH , δH, P m,H, G]

Transmission line: Constant Reactance

Load: Constant power

TransmissionLine

Transmission Line Equations

Assumptions:

-Voltage magnitudes are 1 p.u.

-Angle δ1,2 is small

OneHydro,OneWind

Hydroelectric Generator

Dynamic Equations:

Wind Generator Dynamic

Equations: Dynamic states for

Hydro Generator x1 = [ωw , δw, λds, λqs,

λdr , λqr]

TransmissionLine

Assumptions:

-Voltage magnitudes are 1 p.u.

-Angle δ1,2 is small

Transmission Line Equations

EquilibriumPoint:Centralized

Step 1: Gather All Dynamic Equations for System, set differential term to zero

Step 2: Solve Simultaneous Equations

Step 3: If multiple solutions, find physically

feasible solution close to initial value for solver algorithm

TwoConvenGonalGenerators

2x Hydroelectric Generator Dynamic Equations: Hydro 1 Hydro 2

Simultaneous Equations:

Transmission Line Equations

OneHydro,OneWind

Hydroelectric Generator

Dynamic Equations:

Simultaneous Equations: Solver: Newton Raphson Method

Transmission Line Equations

Wind Generator Dynamic

Equations:

EquilibriumResults

2bus hydro

Wsys = 1 Wref1 = 1.43 Wref2 = 1.43 PL = 1 Pm1 = 0.8 Pm2 = 0.2 Pe1 0.8000 Pe2 0.2000 w1 1.0019 w2 1.0019 Steps 2.0000

2bus hydro-wind

Wref = 1 Pmh = .8 PL = 1 Pmw = .2

wH 1.0404 wR 1.1297 PeH 0.9872 Pew 0.0128 flux_ds 0.0000

flux_qs -0.9600

flux_dr 0.1404 flux_qr 0.0002

Two Hydro-generator Results One Solution

One Hydro, One Wind Results Feasible Solution*

*multiple equilibria (3), one has high freq (~3 p.u.), one has negative frequency

DistributedEquilibriumSolverEquality Constrained Newton Method

Objective function: f(x) = Σk

n Φk (xk) where x = [P1, … Pn] represents the power flows for each of the n buses in the system

The objective function f(x) tries to minimize the difference

between the local frequency ωn and a frequency setpoint ωsys

Design Φk (xk) to make each module’s frequency independent:

Φk (xk) = (ωk - ωsys )2

Then write ωk as a function of Pk so that the distributed

Newton Step can be written in terms of power flow variables.

DistributedEquilibriumSolverEquality Constrained Newton Method

How to update variable x (power flows)

Source: Jadbabaie, A. , et. Al. “A Distributed Newton Method for Network Optimization

Hk = = Hessian Matrix

= Jacobian Matrix

Ax =b : network constraint hk = Axk - b

  Need Systematic Approach that accounts for Network Constraints   Inter-Area Oscillations   Insufficient Regulation Capacity   Wind Farms far away from the Load Centre

AutomaGcGeneraGonControl:Revisited

0 10 20 30 40 50 60 70 80 90 100-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

Time (sec)

Pg

(p.u

.)

Generator Real Power Output in 5 Bus System

Pg1Pg2Pg3

  Inter-Area Oscillations

  Insufficient Regulation Capacity

  Network Constraints Effecting Regulation of Wind Fluctuations

AutomaGcGeneraGonControl:Revisited

  Hard-to-Predict Imbalances   Load Fluctuations: White Noise

  Wind: Case of Non-Zero Mean Deviations

  A Case of Non-Zero Mean in Wind   Wind as Negative Load: CPS-2 Violated

WindGeneraGon:ASourceofDisturbances

  Measure Imbalances at Multiple locations

v/s

  Static Control Notion   Decentralized Response

  GTG’s Quasi Static Model

  Load Characterization

  Network Constraints

ModelingSystemComponents

  Frequency based Control Model

  Load Parameterization Critical

  An LQG Problem 0 100 200 300 400 500 600 700 800 900 1000-0.2

-0.1

0

0.1

0.2Frequency Deviations at G2 (Hz)

0 100 200 300 400 500 600 700 800 900 1000-0.2

-0.1

0

0.1

0.2

0 100 200 300 400 500 600 700 800 900 1000-0.2

-0.1

0

0.1

0.2

Time(s)

Frequency Sensitivity L4=0.10, L5=0.14 (rad/sec)/p.u.

Frequency Sensitivity L4=0.25, L5=0.25 (rad/sec)/p.u.

Frequency Sensitivity L4=0.40, L5=0.40 (rad/sec)/p.u.

RegulaGonReservePlanning:FrequencyBias

AutomaGcGeneraGon&DemandControl(AGDC)

  Power based Control Model   Active Demand Response   Sensors embedded in Smart Appliances   Potential Source of Regulation   Flexibility in Electric Grid

  Load Characterization

  High Accuracy with Power Model for Distribution System

  Proof of Concept

  Wind and Load Disturbance at L4 and L5

  Actual Power Generation and Consumption

AutomaGcGeneraGon&DemandControl(AGDC)

AGDC:OpenQuesGons

  How much Demand needs to participate to offset the need for one gas power plant ?

  Provide Incentives to encourage the use of Variable Speed Drive’s Technology.

  Trade-off between the need for very accurate sensor (e.g. to sense frequency) and the need for communication (e.g. power based control on load side)

  Studies to realize potential of AGDC in real-world systems

  How do the frequency response specifications for generator and load, as part of AGDC, relate to current industry standards ?

AdvancingTheNewGeneraGonofSmartGridTechnologies

Weare: MovingDYMONDSconceptsforward  PresenGngandbecomingfamiliartoDoE,NIST,FERC,EPRI,NERC

  Developingmodeling,simulaGons,tesGngusingreal‐systemdata

  AssessingpotenGalbenefitsfromimplemenGngJIT,JIPandJICoperaGngandplanningparadigm

 MakingSmartGridAReality!  AhugejobwhichonlycanbedonebydrawingonpreviousR&D,aoerre‐posingtheproblems.ForthefirstGmefeasible!