Dynamic Monitoring and Decision Systems (DYMONDS) for ... · Just‐in‐Time (JIT)...
Transcript of Dynamic Monitoring and Decision Systems (DYMONDS) for ... · Just‐in‐Time (JIT)...
DynamicMonitoringandDecisionSystems(DYMONDS)forSustainableEnergyServices
MarijaIlic,ProfessorandSRCSmartGridResearchCenterDirector,[email protected]
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
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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
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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]
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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
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LeXie
Example2:WindpredicGon,look‐aheadmanagementusingstorage
ComparetheoutcomeofEDfromboththecentralizedanddistributedMPCapproaches.
IntegraGng>50%Wind
DYMONDS Simulator Scenario 3: + Electric vehicles [6]
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Interchange supply / demand mode by time-varying prices
NiklasRotering
Example3:OpGmalControlofPlug‐in‐ElectricVehicles:Fastvs.Smart
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InformaGonflowforintegraGngPHEVs
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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!