Operation of Power Flow Controllers --Computational ...
Transcript of Operation of Power Flow Controllers --Computational ...
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OperationofPowerFlowControllers:ComputationalEfficiencyandMarket
Participation
Mostafa [email protected]
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SummaryofResearchProjects
• PowerSystemOperationDuringWindstorms– CollaborativeResearchwithDepartmentofCivilEngineering
• Computationally-EfficientAlgorithmDesignforOperationofPowerFlowControllers– TransmissionSwitching(ARPA-EProject)– ControllableReactance(TCSC,SmartWireGrid)
• MarketDesignforFlexibleTransmission
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Motivation
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Economicsizeoftheindustry:$350billion
Transmissionsystemisunderstress
Transmissionbottleneckscreateeconomicinefficiency
Transmissionsystemneedstobeupgraded• Improvedeconomicefficiency• Reliability-motivatedupgrades
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TransmissionBottlenecks
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• Option1:buildmorelines• Option2:powerflowcontrol
Congested
NotCongested
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CongestionCostinUSISO/RTOs
5
00.20.40.60.81
1.21.41.6
ISOCongestionCosts– 2015
CAISO ERCOT ISO-NE MISO NYISO PJM SPP
$B
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Choices
BuildingNewTransmission
Lines
MoreEfficientUtilizationoftheExistingGrid– PowerFlowControl
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Moreefficientutilizationoftheexistingnetworkischeaperandparamount!
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ResearchandDevelopmentEfforts
• ARPA-EGENIinitiative:Over40milliondollarsforpowercontrolhardwareandsoftware
Hardware:• Smartwiregriddevice• FlexibleACTransmissionSystem(FACTS)
Software:• Transmissionswitching(TS)
– Fastconvergence– QualityACsolution– Dynamicstabilityanalysis
• EnhancedFACTSadjustment(notsupportedbyARPA-E)– Samebenefits– Moreorlessthesameconcerns
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PowerFlowPhysics
Vj∠θ jVi∠θi B
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F = B(θ j −θi )DCPowerFlowEquation
ThisisalinearapproximationofACpowerflowequation:• Relativelyaccurate• Facilitatesefficientcomputation
Susceptance
Electricityflowsaccordingtothelawsofphysics,noteconomics!
F
8
VariableImpedanceFACTSBmin ≤ B ≤ Bmax PowerElectronicsFk = Bk (θ j −θi )
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EconomicExample
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1 2G1:Cheap G2:Expensive
Load:250MW
Capacity:100MW,X
Capacity:150MW,X
100MW
100MW
200MW
50MW
100MW
140MW
240MW
10MW
CostReduction!
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Technology– TCSC
• Thyristor-ControlledSeriesCompensator
10
8 Thyristor control | Thyristor Controlled Series Compensation
In a TCSC, the whole capacitor bank or alternatively, a section of it, is provided with a parallel thyristor controlled inductor which circulates current pulses that add in phase with the line current so as to boost the capacitive voltage beyond the level that would be obtained by the line current alone. Each thyristor is triggered once per cycle and has a conduction interval that is shorter than half a cycle of the rated mains frequency. By controlling the additional voltage to be proportional to the line current, the TCSC will be seen by the transmission system as having a virtually increased reac-tance beyond the physical reactance of the capacitor.
The thyristor valve is integrated in the capacitor overvoltage protection scheme. It replaces the Fast Protective Device and allows a reduction of the rating of the protective parallel varistor.
Thyristor control
Thyristor-controlled segment of a series capacitor.
Xv
a) b)
uc
uc
iL xc
iLiv iv
TCSC in steady-state, a) circuit b) waveforms.
ABB– TCSCbrochure EPRITechConference
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Technology– SmartWireGrid
• SmartWireGridDevice
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PowerRouterbrochure
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EconomicImpacts
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Congested
Not Congested
Withpowerflowcontrol,cheaperresourcescanreplacethelocalexpensiveresources.
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ResearchObjective
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• Challenges:1. Computationalcomplexity(limitedtime)2. Powerflowcontrollersareapartofthe
transmissionnetwork• Regulated• Noincentivetooperateinasociallyoptimalway
• Goal:Designamarketmechanismthatwouldallowpowerelectronicstoparticipateinthemarket!
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COMPUTATIONALCOMPLEXITY
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ComputationalComplexity– DCOPF
• DCOPF– LinearProgram(LP)𝑚𝑖𝑛∑ 𝑐'𝑃'' (1)
𝑃')*+ ≤ 𝑃' ≤ 𝑃')-. ∀𝑔 (2)−𝐹3456≤ 𝐹3 ≤ 𝐹3456 ∀𝑘 (3)𝐹3 − 𝐵9 𝜃+ − 𝜃) = 0 ∀𝑘 (4)∑ 𝑃99∈>? + − ∑ 𝑃99∈>@ + + ∑ 𝑃''∈' + = 𝑑+ ∀𝑛 (5)
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LinearProgram
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VariableImpedanceFACTS
• VariableimpedanceFACTS
• Providepowerflowcontrol• CreatenonlinearitiesinDCoptimalpowerflow
LinearProgramè Non-LinearProgram(MixedIntegerProgram)
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Vn∠θn Vm∠θm
Rk + jXk jXmin ≤ jXv ≤ jXmax
NonlinearequationBmin ≤ B ≤ Bmax Susceptance isavariableFk = Bk (θ j −θi )
ComputationalBurden
NoFACTSsetpointadjustmentwithinEMSorMMSsoftware
Infrequentadhocadjustments
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ComputationalComplexity–NLP/MIP
NonConvex(MIP)
Convex(LP)
Convex(LP)
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Fk = Bk (Δθk )Bkmin ≤ Bk ≤ Bk
max
Fk
Fkmax
Fkmin
Δθk
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WhatifweknewwhichB&Btreenodeistheoptimalnode?
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EngineeringInsight
• Weonlyneedtoknowthedirectionofthepowerflow
• Weknowthisdirectionformajorlines(COI)
• Evenifwedonotknowthedirection,wecanrunatwo-stageDCOPFandidentifyit.
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Convex(LP)
Convex(LP)
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Fk = Bk (Δθk )Bkmin ≤ Bk ≤ Bk
max
Fk
Fkmax
Fkmin
Δθk
KnowingthedirectionwouldreducethecomplexitytoaLP
Thisisaheuristic
Optimalityisnotguaranteed!
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SCEDCostSavingsIEEE118-BusSystem
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0 10 20 30 401200
1400
1600
1800
2000
2200
Number of Lines With FACTS
Cos
t ($/
h)
2%5%
10%
20%30%
70%90%
50%
0%10%20%30%40%50%60%70%80%90%
0% 50% 100%
Saving
s
FACTSCapacity
5 10 15 20Number ofFACTS:
OptimalFACTSPlacement:>98%Optimal
LocatedonMoreHeavilyUtilizedLines:100%Optimal
Savingsarecalculatedcomparedtoatransportationmodel
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SCEDCostSavingsPolishSystem
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0
10
20
30
40
50
60
70
80
90
0% 20% 40% 60% 80% 100%
%ofP
oten
tialSavings
FACTSReactanceControlRange
5 10 15 20Number ofFACTS:
LocatedonMoreHeavilyUtilizedLines:100%Optimal
Savingsarecalculatedcomparedtoatransportationmodel
~2,400buses
~2,900branches
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ComputationalTime
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0 100 200 300 400 500 600
LPMIP
ComputationalTime(ms)
MIPAverage
LPAverage
Max:4630s
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CorrectiveAdjustments
• Incorrectiveadjustmentswehaveevenbetterinsightaboutthedirectionofthepowerflow:pre- orpost- contingencyflows
• Goal:minimizationofpost-contingencynetworkviolations
• OptimalutilizationofFACTSinrecoursestateonly
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Contingency Analysis
SCUC
Contingency Analysis
Calculate the difference in Network Violations
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CorrectiveResultsIEEE118-BusSystem
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25 10 20 30 50 70 900
20
40
60
80
100
Reactance Change (%)
Viol
atio
n R
educ
tion
(%)
LPMILP
5 Lines10 Lines
15 Lines20 Lines LocatedonMore
HeavilyUtilizedLines:100%Optimal
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ShiftFactorStructure
• IndustryimplementationsofSCUCandSCEDdonotuse𝐵𝜃structure;theyusePTDFs.– Noneedtomodelallthevoltageangles– Noneedtocalculatealltheflows– Significantlyfastercomparedto𝐵𝜃
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from to𝑏9 + 𝛥𝑏9
from to𝑏9
𝑓9𝛥𝑏9
𝑏9 𝑓9
𝛥𝑏9
𝑏9
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InjectionModelofReactanceControl
• Again,weendupwithaMixed-IntegerLinearProgram!• Wecanusethesameengineeringinsighttoconvertthis
toaLP.• Similarmethodcanbeusedtogeneratecontingency
constraints.
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from to𝑏9 + 𝛥𝑏9
from to𝑏9
𝑓9𝛥𝑏9
𝑏9 𝑓9
𝛥𝑏9
𝑏9
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Results– PolishSystem
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𝑩𝜽 versusPTDF 𝐅𝐢𝐱𝐞𝐝𝐯𝐞𝐫𝐬𝐮𝐬𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞𝐓𝐡𝐫𝐞𝐬𝐡𝐨𝐥𝐝𝐬
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MARKETPARTICIAPTION
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O’Neill’sCompleteMarketProposal
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1 2G1:Cheap G2:Expensive
Load:250MW
Capacity:100MW,X
Capacity:150MW,X
100MW
200MW
50MW
100MW
• PositiveexternalityinDr.O’Neill’scompletemarketproposal:• Paymenttoline:(Flow)x(LMPDifference)
• Nomatterwhichlinechangesthereactance,itisalwaysthesecondlinethatwillcarrymorepowerandgetpaid!
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ProposedPaymentSystem
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Fk = Bk (θ j −θi ) (Sk )
Sk (θ j −θi )×ΔBkMarginalValue:Price Quantity
• Usethesusceptance pricetocalculatethemarginalvalueofsusceptance:
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TwoNodeSystem
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G1 G2
-700p.u.,400MW
-700p.u.,450MWd2
MC2=P2+50($/MWh)MC1=$30/MWh
FACTScontrolrange:2%
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MarginalValue
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SAHRAEI-ARDAKANI AND BLUMSACK: TRANSFER CAPABILITY IMPROVEMENT 3711
Fig. 10. Cost savings due to the improved transfer capability offered by theFACTS devices.
Fig. 11. Congestion rent in thousands of dollars with and without FACTSdevices.
payments are much smaller than the congestion rent shown inFig. 11, making the market revenue adequate.Fig. 12 shows the payments to the FACTS owners. FACTS
revenue is clearly smaller than the congestion rent shown inFig. 11. According to the proof presented in Section III-A, pay-ments to the owner of should be less than the con-gestion rent collected by line, 2 since the FACTS adjustmentaims at increasing the flow of this line by increasing the abso-lute value of its susceptance. However, revenue adequacy is notguaranteed for , since the susceptance adjustment isin the opposite direction. Despite the absence of proof, FACTSpayments are much smaller than the congestion rent shown inFig. 11, making the market revenue adequate.Fig. 13 provides additional insight into how the market would
work. The marginal value of FACTS capacity for the device in-stalled on line one is depicted in the figure. This is depicted ac-cording to (8). It shows how the price is set at different levelsof demand. In this example, each per unit change in the sus-ceptance of the line would decrease the marginal value forminga negatively sloped curve. Note that the slope may very well
Fig. 12. Payments to the FACTS owners. Solid lines indicate perfect competi-tion, while dashed lines show Cournot results.
Fig. 13. Marginal value and supply functions for FACTS capacity installed onthe line one at different levels of load.
be positive due to the non-convexities of the market. In a per-fectly competitive market, FACTS owners would submit a bidof $0/p.u., which would be a horizontal line at zero until theircapacity is exhausted; then, it would be a vertical line, meaningthat they cannot offer more adjustment at any price. When theload is low and FACTS capacity is enough to relieve the conges-tion, the price would be zero. For example the market results ina price of $0 per p.u. for demand of 805MW. However for largeenough demand, that FACTS capacity is not enough for removalof the congestion, the marginal value of the FACTS adjustmentsets the price. For example, for the load of 830 MW, the pricewould be $18.11 per p.u. change in the susceptance of the firstline.Under Cournot competition, the players find their optimal ca-
pacity to offer to the market based on the marginal value func-tion. Thus, even when the FACTS capacity is enough to relievethe congestion, the device owners would strategically withholdsome of their capacity to keep the marginal value positive. If thecongestion is removed this value would go down to zero.
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𝑆9(𝜃* − 𝜃[)
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RevenueAdequacy
• Congestionrentisthepaymentsource
• Revenueadequateiftheadjustmentsleadtoincreasedloading!
• Notnecessarilyguaranteedinallcases
• Itishighlyunlikelythatthemarketisrevenueinadequate
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IEEE118BusSystem2FACTSDevices
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Conclusions
• MathematicalrepresentationofOPFwithFACTS:NLP
• WereformulatedtheNLPtoaMILP;usingourknowledgeofelectricityflowphysics,wereformulatetheproblemtoanLP
• TheLPheuristicisextremelyeffective:itfoundtheoptimalsolutionmorethan98%ofthetime.
• Theheuristicisextremelyfast(LP)andwouldnotaddtothecomplexityoftheOPFproblem
• Wedesignedacompensationmechanismtosignalenhancedoperationofthedevices.
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Thankyou!Questions?
36
Mostafa [email protected]
• M.Sahraei-ArdakaniandK.Hedman,“AFastLPApproachforEnhancedUtilizationofVariableImpedanceBasedFACTSDevices,”IEEETransactionsonPowerSystems
• M.Sahraei-ArdakaniandK.Hedman,“Day-AheadCorrectiveAdjustmentofFACTSReactance:ALinearProgrammingApproach,”IEEETransactionsonPowerSystems
• M.Sahraei-ArdakaniandS.Blumsack,“TransferCapabilityImprovementthroughMarket-BasedOperationofSeriesFACTSDevices,”IEEETransactionsonPowerSystem
• M.Sahraei-ArdakaniandK.Hedman,“ComputationallyEfficientAdjustmentofFACTSSetPointsinDCOptimalPowerFlowwithShiftFactorStructure,”IEEETransactions onPowerSystems
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