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Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND NO. 2011-XXXXP
AssessingPowerSystemResiliencetoAdverseWeatherEvents
AndreaStaidINFORMSComputingSocietyConference
January17,2017
WhatisInfrastructureResilience?
Definitions:§ Theabilitytoprepareforandadapttochangingconditionsand
withstandandrecoverrapidlyfromdisruptions.– PresidentialPolicyDirective21§ Theabilitytoreducethemagnitudeand/ordurationofdisruptive
events.– NationalInfrastructureAdvisoryCouncil§ Anticipate– Identifyandplanforadverseevents(deliberateattacks,accidents,
ornaturaldisasters)§ Absorb– Continueoperatingaftershockstosystem§ Adapt– Adjustsysteminrealtimetominimizeadverseimpacts§ Recover– Returnsystemoperationstonormalstateasfastaspossible
Howisitdifferentfromreliability?§ Resilience focusesonlow-probability,high-consequenceevents§ Reliability focusesonhigh-probability,low-consequenceevents
§ Day-to-Dayoperations2
Howtoimproveresilience?
§ Mustbeabletomeasureit!§ Usearisk-basedapproach
§ Identifythreatsofconcern§ Resilienttowhat?Improvementsmustbetargetedataspecifictypeofthreat.
§ Increasedresilienceagainsthurricanesmaynothelpwithresiliencetoterrorattacks.Approachisgenerallythreat-specific.
§ Assesslikelihoodofsystemdisruptiongivenathreat§ Evaluateconsequencesofdisruption
§ Moreresilientsystemswillminimizelikelihoodofdisruption,severityofconsequences,orboth
§ Needaquantifiablemetricgivenaspecificthreat
3
ResiliencyAnalysisProcess
§ Frameworkforquantificationofpowersystemresilience§ Thisframeworkenablesdecisionmakingtoobtain
demonstrableresilienceimprovements
4
Prob
abilit
y of
Con
sequ
ence
s [$
] G
iven
Thr
eat X
Consequences [$]
Reduced Expected Financial Consequence
Reduced Risk
Baseline System Resilience
Resilience of System after Improvements Improvements must
cost significantly less than E-E’
E’(C) E(C)
§ Resultingresiliencemetricsareprobabilistic
§ Theframeworkisflexible:§ Canhandledifferenttypes
ofthreats§ Providesinformationfor
differenttypesofdecisionmakers
ResilienceFramework
5
Define Resilience
Goals
Define System & Resilience
Metrics
CharacterizeThreats
Determine Level of Disruption
Define & Apply System Models
Calculate Consequence
Evaluate Resilience
Improvements
Populate
Define Resilience
Goals
Define System & Resilience
Metrics
CharacterizeThreats
Determine Level
of Disruption
Define & Apply System Models
Calculate Consequence
Evaluate Resilience
Improvements
Create
ScenarioAnalysis:IdentifyThreats
6
§ Whatisthepossiblethreatspace?
§ Consequences(andprotectivemeasures)canvarydrasticallyamongthreats.
§ Focusonimprovingsystemresilienceagainstanindividualthreat.
ScenarioAnalysis:CharacterizeIndividualThreat
§ Givenhigh-levelthreatcharacterization,thenextstepistofurtherrefinethedescriptionofthespecificthreats
… …
Historicalinformationandforecastmodelsusedtoguidespecificationofpossibleeventsandtheirrelativelikelihoods
p1 p2 pn
Category4,north-of-peninsulastormtrack
Category5,eyetracksovermetropolitanarea
Category2,landfallathightide
…
ScenarioAnalysis:DisruptingtheSystem
… …p1 p2 pn
Category4,north-of-peninsulastormtrack
Category5,eyetracksovermetropolitanarea
Category2,landfallathightide
……
Givenaspecificmanifestationofadisruptionevent,wethenspecifyadistribution ofinfrastructureimpacts
DamageRealizationN
DamageRealizationK
Assumeuniformprobabilities
Forexample:1. Normaldistributionofgeneratorfailures,
withu=20,s=52. Normaldistributionoflinefailures,with
u=40,s=7
§ Thefinalstepistotranslatedisruptioneventsintosystemimpacts
CaseStudy– AEPAdverseWeather
9
§ AmericanElectricPower(AEP)isalargeelectricutilityintheU.S.§ Serving5.4millioncustomersin11states§ OwnslargesttransmissionnetworkintheU.S.– Morethan40,000
miles– and31GWofgeneratingcapacity
§ Interestedinimprovingresiliencytoadverseweathereventsintheir‘East’territory
ImprovementOptions
§ Transmissionsystemresiliencycanbeimprovedby:§ Hardeninglines
§ Reducinglikelihoodoffailureforindividuallines§ Long-termplanningdecision
§ Generatorre-dispatchand/ortransmissionswitching§ Inadvanceofastorm,re-dispatchsystemtomaintainpowertocustomers
§ Real-timedecisionswhenadverseweatherinforecast
§ Demonstratebenefitofpossibleactionusingscenariosofweatherevents§ Compareproactivedecisionsto‘businessasusual’case
10
AnalyzingSystemResponse
§ Wouldtherebeabenefittore-dispatchingthesysteminadvanceofastorm?Canthisincreaseresilience?
§ Usehistoricalstormstobuildscenariosofrealisticfuturestorms§ Futurestormsunlikelytoexactlymirrorpaststorms,butsystem
weaknessescanbecaptured§ Historicalprobabilityofoutagevariesdrasticallyacrosslines
§ Scenariogenerationforthreeapplicationareas:§ Identifycandidatesforlinehardening§ Demonstratevalueofreal-timegeneratorre-dispatch§ Developtoolforreal-timeuseinadvanceofoncomingweather
§ Inwork
11
Number of Outages per Circuit
Density
0 20 40 60 80 100
0.00
0.02
0.04
0.06
0.08
0.10
Number of Outages per Event (Log Scale)
Density
0 1 2 3 4 5
0.0
0.2
0.4
0.6
0.8
1.0
AvailableData§ Transmissioncircuitoutagedatafrom~1990– 2015§ Mostcircuits seeveryfewoutages,butsomehavemany§ Moststorms causeveryfewoutages,butsomestormsresultin
hugenumbers(e.g.,June2012Derecho,SuperstormSandy)
12
ScenarioGeneration
§ Needtorepresentspectrumofstormdamage:§ Randomsamplingfor‘standard’scenarios§ Augmentedsamplingfor‘worstcase’scenarios
§ Forcelargernumberofoutages(uptoselectedmaximum),stillusingbaselineprobabilitiestosample
§ Complications:§ Probabilitiesoflineoutagescanbeconditionalontypeofweather
§ But,realdataismessy,mostoftheoutagecause-codescannotbetrusted§ Somestormeventshavespatiallydistributedoutages,othersvery
concentrated§ Needto‘force’scenariostorepresentbothtypes.Don’tyethaveenoughdatatodothiswell
§ Outagesmustrepresentcascadingfailuresinsystem§ Linkscenariosto‘contingency’dataonpropagatingfailures§ Givesrealisticrepresentationofhowsystemwouldhandleanoutage 13
AnticipativeOperations§ StochasticOptimizationallowingforanticipativeoperations
§ Basedonuncertainscenariosofadverseweatherimpact,howbesttore-dispatchgeneratorstominimizelossofload?
14
BASELINE RE-DISPATCH
NextSteps– Real-TimeTool
§ Createscenariosforuseinreal-timedecisionsasstormapproaches
§ Linkweatherdatatoprobabilityofoutage;Circuitoutagewillbeafunctionof:§ Windspeed,Precipitation,Temperature,Lightningforecast,etc.
§ Generatescenariosbasedonuncertaintyinweatherforecast,stormtrack,andprobabilityoffailure
§ Simulatesuggesteddecisionsforre-dispatchonAEPsystemforactualweatherevents
§ Quantifychangeinconsequences(lossofload)acrosssystem
15
SomeFinalThoughts
§ Demonstratedbenefittosystemoperatorsbybeingproactivewhenitcomestoweather
§ Inordertoquantifythechangesinsystemresiliencetoadverseweather,wechosetodevelopscenariosof‘stormevents’representedbytransmissionlinefailures
§ Workingtowardsareal-timetool;movesfromtheoreticallyimprovingresiliencetoofferingsuggestedactionstominimizelossofload§ Highlydata-dependent,shouldbeexciting!
16
Questions?
Contact:[email protected]
Acknowledgements:AmericanElectricPowerforprovidingthedataDHSandDOE/EPSAforfunding
17