GRID MODERNIZATION INITIATIVEPEER REVIEWGMLC 1.4.17 – Extreme Event Modeling

RUSSELL BENT

4/4/17 1

April 18-20Sheraton Pentagon City – Arlington, VA

Planning and Design

4/4/17Planning and Design 2

ProjectDescription• Naturalandman-madeextremeeventspose

enormousthreats

• CascadingandN-kmodelinghavelargegaps• Inadequatemodeling

• Reliabilitystandards(NERCStandardTPL-001-4)challenged

• Computationalefficiency• Considerablespeeduprequiredfor

fasterthanrealtimeplanning• N-kcontingencyanalysis

• Existingk=3analysismisseslarge-scaleadversaryattacks

• Neglectshighlikelihoodfailures

ValuePropositionü Identifyextremeeventriskpriorto

eventoccurrenceü Planproactively

ProjectObjectivesü Aprototypesetoftoolsforefficient

cascademodelingandprobabilisticN-kidentification.

ü Toolsthatare500xfasterthanexistingindustrycascadesimulationpackages

ü Identifytheworst(probabilistic)kcontingencieswherekistwiceasbigasexistingpractices

ü Demonstrationonalarge-scalesystem(WECC)

Extreme Event ModelingHigh Level Summary

4/4/17Planning and Design 3

PROJECTFUNDING

Lab FY16$ FY17$ FY18$LANL 155K 130K 145KPNNL 210K 235K 180KLLNL 160K 260K 210KANL 125K 95K 125KORNL 125K 95K 125KBNL 50K 45K 45KNREL 50K 45K 45KSNL 125K 95K 125K

ProjectParticipantsandRoles• RussellBent (LANL):PI,TaskLeadfor3.4:Most

probableN-kidentification• YuriMakarov (PNNL):+1,TaskLeadfor1.1:

Integratingmultipletemporalscales,1.2:InadequateModeling—IntegratingProtectionSystemmodels

• LiangMin (LLNL):TaskLeadfor1.3:Integratingrenewables,2.3:Parallelcomputingformassivedynamiccontingency

• Junjian Qi (ANL):TaskLeadfor2.1:Predictingcriticalcascadingpath

• Yilu Liu (ORNL):TaskLeadfor2.2:ModelReductionTechniques

• Meng Yue (BNL):TaskLeadfor3.1:ComponentFailureProbabilities

• KaraClark (NREL):TaskLeadfor3.2:MitigationPlanModeling

• Jean-PaulWatson (SNL):TaskLeadfor3.3:WorstCaseN-kidentification

Extreme Event ModelingProject Team

IndustryandAcademicPartners: GMLC,NERC,FERC,IEEECascadingFailureWorkingGroup,DominionVirginiaPower,PJM,ERCOT,UTK• Webinarparticipation• Powersystemdata

4/4/17Planning and Design 4

PrimaryMYPPGoal:A10%reductionintheeconomiccostsofpoweroutagesby2025

ExecutiveSummary:greaterresiliencetohazardsofalltype

MYPPActivities▶ PlanningandDesignActivity2:DevelopingandAdapting

ToolsforImprovingReliability andResilience▶ SubActivity5.3:ModelingforExtremeEvents

◼ Task5.2.3:Developmethodologiestosimulatecascadingeventsandprotectionsystemsandimprovesolutiontimesby500xviascalablecomputationalmathalgorithmsandautomationtechniques.IncludeprobabilisticapproachesinN-kcontingencyanalysis● ProjectDeliverable: Toolsthatare500xfasterthanexisting

industrycascadesimulationpackages(2019)● ProjectDeliverable: Toolsthatidentifytheworst

(probabilistic)kcontingencieswherekistwiceasbigasexistingpractices

◼ Task5.2.4:Developtoolsneededtoperforminterconnectionlevelanalysisofextremeeventssuchasweather,EMP,GMD,andcyberandphysicalattacks.● Projectdevelopmentsareanecessaryfoundationforfuture

toolsandcapabilitiesformitigatingtheconsequencesofsucheventsandmodelingofsourcesofextremeevents

Extreme Event ModelingRelationship to Grid Modernization MYPP

PlanningandDesign

5.3:ModelingforExtremeEvents

5.3.3:SimulatingCascadesandN-k

5.3.4:InterconnectionLevelAnalysis

*Asofhttps://energy.gov/sites/prod/files/2016/01/f28/Grid%20Modernization%20Multi-Year%20Program%20Plan.pdf

Planning and Design

Extreme Event ModelingApproach

▶ CascadeModeling:InadequateModeling◼ Integratingmultipletemporalscales

● Description: Developnewmethodsformodelingphenomenaatdifferenttimemultipletimescales

● KeyIssues: Fundamentallydifferentmethodsusedatdifferenttimescales,difficulttointegrate

● Novelty: Uniquehybridapproachforcombiningphenomenaandmathematicsatdifferenttimescales

◼ Integratingprotectionsystemmodels● Description: DevelopmodelsofZone3protection● KeyIssues: Theextentandorderingofprotectionexecutionis

oftenunknown● Novelty: Newmethodsforestimatingthebehaviorof

protectionduringcascades.◼ IntegratingRenewables

● Description: Developmathematicalmodelsandimplementationsoflong-termwinddynamics

● KeyIssues: Nostabilitysimulationplatformthatcombinescomputationalcapabilitieswithmodelsneededforassessingtheimplicationsofwindenergyresourcesdynamics

● Novelty: newmathematicalmodelsofwinddynamicssuitableforcascades

▶ CascadeModeling:ComputationalEfficiency◼ Predictingcriticalcascadingpaths

● Description: Developstatisticalmethodsforidentifyingcascadingpaths

● KeyIssues: Thenumberofpossiblecascadeevolutionscanbetolargetoenumerate

● Novelty: Modelsandsoftwaretoolsthatstatisticallycharacterizecomponentinteractionsthatsignificantlylimitthenumbercascadeevolutionsthatneedtobesimulation

◼ ModelReductiontechniques● Description: Methodsandsoftwareforreducingthesizeof

networks● KeyIssues: Networkmodelscanbetoolargeforexhaustive

cascademodeling● Novelty: New approaches for model reduction based on

measurement data4/4/17 5

◼ Parallelcomputingformassivedynamiccontingencyanalysis● Description: LeverageHPCtoimproveefficiencyofcascade

modeling● KeyIssues: Thenumberofcascadesaretoomanyto

enumerateserially● Novelty: ExtensiveleveragingofDOEandlabinvestmentsin

HPCtoimprovecomputationby500x▶ ProbabilisticN-k

◼ Componentfailureprobabilities● Description: Developprobabilisticmodelsofcomponent

failurebasedondata● KeyIssues: Utilitiescurrentlydonothaverigorousapproaches

forbuildprobabilisticmodelsoffailure● Novelty: FormalprobabilitiesforN-k

◼ Systemfailureprobabilities● Description: Developprobabilisticmodelsofsystemfailures

basedduringextremeevents● KeyIssues: Dataissparseforexamplesofextremeevent

systemfailures● Novelty: Formalprobabilisticofextremeeventsystemfailures

◼ Worst-CaseN-kIdentification● Description: Toolsforidentifyingsetsofkcomponentfailures

withthebiggestimpact● KeyIssues: Itiscomputationallyintractabletofindk>3worst

failures● Novelty: Newapproachesfordoublingthesizeofk

◼ MostprobableN-kIdentification● Description: Toolsforidentifyingsetsofkcomponentfailures

whoseprobabilisticoutcomeisworst.● KeyIssues: Computationallyverydifficulttofindsetsoflargek● Novelty: ToolsthatcombineprobabilisticmodelswithN-k

optimization

4/4/17 6

Milestone (FY16-FY18) Status DueDateProtectivemeasuresapproachidentifiedandastrategyforimplementationinDCATcompleted

Complete 10/1/16

ImplementationofprotectivemeasuresinDCAT Complete 1/1/17

Reportdetailingsurveyofpastoutagesandextremeevents

Complete– DeliveredonGMLCsharesite 1/1/17

Cascademodelingdemonstrates10xofcascadesimulationsascomparedtoexistingtools

Started:Work focusedondevelopingunderlyingHPCarchitecture

10/1/17

ScaleN-kapproachestonetworksthatare10xlargerthanexistingtoolscanhandle

Started:InitialN-ksoftwareframeworkdevelopedinPyomo

10/1/17

Cascademodelingdemonstrates100xofcascadesimulationsascomparedtoexistingtools

Notstarted 10/1/18

Opensourceprototypereleasethat1)Integratesmultipletemporalscales,protectionsystemmodeling,andrenewablesintocascademodels,2)demonstrates500xspeedupofcascadesimulationsascomparedtoexistingtools,and3)improvescomputationofN-kbyincreasingkbytwiceasmuchoverexistingpractices.

Notstarted 4/1/19

Planning and Design

Extreme Event ModelingKey Project Milestones

4/4/17Planning and Design 7

▶ TechnicalInsightsandAccomplishments◼ Extremeeventstrategydocument

● Gapsinextrememodeling,directionsforaddressinggaps

◼ DynamicContingencyAnalysisTool(DCAT)● Hybridmodels,zone3protection,ACOPF

◼ SurveyofPastOutagesandExtremeEvents● Lackofstatisticaldataandrigorousanalysisofthe

datacanleadtomisleadingorevenerroneousinformationformakingdecisions

◼ PredictingCascadingPaths● Cascadingpathreductioncanleadto100Xspeed

up

◼ N-kContingencyAnalysis● Developedmethodsforcomputingexact

deterministicN-ksolutions,torealisticN-kpowerflowmodelsfromAmericanElectricPower

● DemonstratedthatprobabilisticN-kiscomplimentarytodeterministicN-k

Extreme Event ModelingAccomplishments to Date

ProbabilisticN-k

NERC-definedextremeevents

ComponentandSystemFailure

ProbabilityAssessment

Renewabledynamicsassessment

Steady-state

analysis

Dynamicanalysis

Protectionsystemmodeling

HybridApproach+HPCImplementation

ImprovedModels

NERCstandard

compliance

Preventivemeasurestomitigatecascading

Nearrealtime

cascadingriskassessment

Blackoutriskreduction

4/4/17Planning and Design 8

▶ Stakeholderengagement◼ Industrywebinars

● June16,2016,Jan.25,2017● FERC,Caiso,IdahoPower,MISO,PLM,DOM,SPP,

NERC,DVP◼ DCATsharedwithIdahoPower,NERC,andERCOT◼ ModelReduction

● TheARXtransferfunctionapproachhasbeenappliedtoameasurementbasedoscillationdampingcontroltoolfortheNYPAsystem.

● ResearchstagefortheTERNA(Italy)Grid.

▶ Publications◼ X.Zhang,Y.Xue,Y.Liu,J.Chai,L.Zhu,andY.Liu,Measurement-based

SystemDynamicReductionUsingTransferFunctionModels,submittedto2017NorthAmericanPowerSymposium(NAPS),Morgantown,WV,Sept.17-19,2017.

◼ Q.Huang,B.Vyakaranam,R.Diao,Y.Makarov,N.Samaan,M.Vallem,andE.Pajuelo,ModelingZone-3ProtectionwithGenericRelayModelsforDynamicContingencyAnalysis,PESGeneralMeeting,2017

◼ Wenyun Ju,KaiSun,andJunjian Qi,Multi-LayerInteractionGraphforAnalysisandMitigationofCascadingOutages,IEEEJournalonEmergingandSelectedTopicsinCircuitsandSystems,underreview

◼ J.Qi.EfficientEstimationofComponentInteractionsforCascadingFailureAnalysisbyEMAlgorithm,IEEETransactionsonPowerSystems,underreview.

◼ A.Florita,M.Folgueras,E.Wenger,V.Gevorgian,andK.Clark.GridFrequencyExtremeEventAnalysisandModelingintheWesternInterconnections.SolarandWindIntegrationWorkshop,underreview.

Extreme Event ModelingAccomplishments to Date

Identifiedtop7keycomponentsandtop13keylinks using400cascadesarethesameasthoseusing41,000cascades

ExampleAccomplishment:Statisticalmodelingofcomponentinteractionscanreducethenumberofcascadesimulationsbyafactorof100

4/4/17Planning and Design 9

Extreme Event ModelingResponse to December 2016 Program Review

Recommendation Response

Pleaseprovidethe“StrategyDocument”dueinApriltotheDOEprogrammanagersbeforethepeerreviewinApril2017.

Strategy DocumentwillbeprovidedtoDOEprogrammanagersnolaterthanApril7,2016

BeforetheApril2017peerreview,pleaseidentifyatleastonestrongutilitypartnerthatyoucanworkwithtoevaluateyournewmodels

The teamhasselectedWECCastheutilitypartnerfornewmodels• 2025planningmodelwithdynamics• EaseofNDAprocess• Dataacquisitionprocessdocumented• LANL,PNNL,LLNL,ANLhaveaccess,other

labsarefollowingprocess.PleasemakesuretocollaboratewiththeMetricsAnalysisteam(project1.1).Ofparticularinterestwillbethereportdetailingsurveyofpastoutagesandextremeevents.ContinuecollaborationswithNewOrleans(1.3.11).

WillreachouttoMetricsAnalysisTeaminFY17 Q2-Q3withSurveyofPastOutagesandExtremeEvents thatwascompletedinDec.2016.

4/4/17Planning and Design 10

▶ GM0076: Emergency monitoring and controls through new technologies and analytics◼ This project addresses a new generation of protection systems based on

advanced analytics, frequent measurements, HPC and fast controls. ◼ Project collaborations focus on mitigation of extreme events.

▶ GM0074: Models and methods for assessing the value of HVDC and MVDC technologies in modern power grids◼ This project addresses the use of HVDC for AC grid services. ◼ Collaborations focus on the potential of DC modulation to stabilize extreme events and

restore the system after disturbances.▶ GM0057: LPNORM A LANL PNNL and NRECA Optimal Resiliency Model

◼ This project focuses on resilient distribution system design. ◼ Collaborations are focused on integrating probabilistic N-k fundamentals into resilient design.

▶ GM0111: Protection and Dynamic Modeling Simulation Analysis and Visualization of Cascading Failures◼ This project focuses on advancing the state-of-art in dynamic and protection

system modeling. ◼ Collaborations are focused on connecting the modeling to cascading failure analysis

▶ 1.4.18 Computational Science for Grid Management◼ This project is focused on foundational computational frameworks. ◼ Collaborations are focused on generating use cases for the computational framework and

possible future activities that could leverage the framework (cascade mitigation)▶ GMLC Planning and Design

◼ Data and Software Working Group

Communications▶ Regularindustrywebinars

◼ June16,2016,Jan.25,2017▶ IEEECascadingFailureWorkingGroup▶ Feb2017PresentationofDCATatERCOTDynamicWorking

Group▶ June2017:InvitedPresentationatNERCPowerSystemModeling

Workshop

Extreme Event ModelingProject Integration and Collaboration

ExtremeEvent

Modeling:1.4.17

GM0076

GM0074

GM01111.4.18

GM0057

4/4/17Planning and Design 11

Extreme Event ModelingNext Steps and Future Plans

ProbabilisticN-k

NERC-definedextremeevents

ComponentandSystemFailure

ProbabilityAssessment

Renewabledynamicsassessment

Steady-state

analysis

Dynamicanalysis

Protectionsystemmodeling

HybridApproach+HPCImplementation

ImprovedModels

NERCstandard

compliance

Preventivemeasurestomitigatecascading

Nearrealtime

cascadingriskassessment

Blackoutriskreduction

• April2017– April2018highlights• Comprehensiveimplementationofcorrective

actionsthroughoptimization• Developacombineddynamic

simulation/protectionmodelforWECCcoordinatedwithBPAandGE

• Demonstrate10xtimespeedupofdynamiccontingencyanalysis(HPC)

• Majormilestoneformeetingnearrealtimeanalysiscapabilities

• 10xscalabilityofN-kapproaches• Significantlylargerkfailuresthanstate-of-the-

art• ExtremeEventModelingFollowons

• Strategydocumentoutlinespossiblefutureactivities

• Mitigationcapabilitydevelopmentissignificant• RequiredtomeetMYPPgoal:10%reductionin

theeconomiccostsofpoweroutagesby2025• Resilience• Integratingancillaryservicesintocascading

modelsofrenewables• SOWsenttoDOEPMs

4/4/17Planning and Design 12

Extreme Event ModelingTechnical Details – Model Reduction

▶ Dynamic model reduction is required to meet both time-critical and accuracy requirements. Conventional methods in commercial software can only be used for offline system analysis and cannot cope with the fast-changing nature of dynamic grids.

▶ We develop measurement based model reduction approaches, which offer the advantages of highly accurate system dynamics of external behaviors in real time, and significantly increased simulation speed.

▶ Specifically, frequency-domain transfer function models with much reduced orders are derived and identified based on real-time phasor measurements. Performances have been tested on a 23-bus system, the NPCC system and the EI system.

0 5 10 15 20

59.94

59.96

59.98

60

60.02

60.04

Time (s)

Fre

quen

cy (H

z)

Original ModelSecond-order TFFirst-order TFZero-order TF

0 5 10 15 201.018

1.022

1.026

1.03

1.034

1.038

Time (s)

Vol

tage

(p.u

.)

Original ModelSecond-order TFFirst-order TFZero-order TF

4/4/17Planning and Design 13

Extreme Event ModelingTechnical Details-Predicting critical cascading path

▶ InteractionsbetweencomponentsestimatedbyExpectationMaximizationAlgorithm

▶ Probabilisticinteractionmodelsimulationbasedonestimatedinteractions

▶ Efficiencyimprovement:aspeedupof100.61x byinteractionmodel

Identifiedtop7keycomponentsandtop13keylinks using400cascadesarethesameasthoseusing41,000cascades

Probabilitydistributionoflineoutagesfromtheinteractionmodelsimulationmatcheswellwiththatfromdetailedcascadingfailuremodelsimulation

Goodmitigationeffect(greatlyreducedprobabilityforlarge-scalecascadingfailures)byremovingthetop10keylinks

[qi15]J.Qi,K.Sun,andS.Mei,“Aninteractionmodelforsimulationandmitigationofcascadingfailures,”IEEETrans.PowerSyst., vol.30,no.2,pp.804-819,Mar.2015.

4/4/17Planning and Design 14

▶ System Failures◼ Overview: Analyzing historical extreme event data and developing tools and methods to identify

predictors leading to extreme events. ◼ Significance: Tool developed will aid utilities in their efforts to predict and plan for extreme events in

an operational context.◼ Existing Efforts: Probabilistic N-k event analysis and cascade modeling, with two sub-foci:

● Causal Impact Simulation: Wind ramping extreme events simulated for the Western Interconnection to produce test dataset.

● Response Analysis: Probability modeling of frequency events obtained at NREL for the Western Interconnection; validated (or not) with NERC records of extreme (frequency) events.

◼ Success: A priori probability models leading to accurate predictions of extreme events relative to a posteriori realizations.

◼ Suggested R&D: Energy policy / investment model for the appropriate level of grid robustness in the face of extreme events.

▶ Component Failures◼ Development of a compendious and expandable repository for outage data for transmission circuits,

transformers, generators, and common mode outages from many disparate sources◼ Investigation of data poolability issues◼ Development of statistical distributions and a tool for outages of different grid components.

Extreme Event ModelingTechnical Details-Data collection and probabilistic modeling of component and system failures

4/4/17Planning and Design 15

▶ Surveyofstudiesandmodelsthatarelinkedtoslowlyvaryingdynamicsofrenewables

▶ Extended-termdynamicsimulationstobeusedincascadinganalysisusingLLNL’sopen-sourcepowertransmissionsystemsimulatorGridDyn

▶ Simulationofrepresentativerenewablevariabilityandrampevents

▶ ImplementationofWECCType-3andType-4genericwindturbinegenerator(WTG)modelsonGridDyn,alongtheirwithgenerator/converter,convertercontrol,andpitchcontrolmodels

▶ AnalysisoftheimpactofthetwoWTGmodelsondynamicperformanceofpowersystems

Extreme Event ModelingTechnical Details-Renewable Energy Modeling Appendix I: Wind-Turbine Generation (WTG) Technologies

46

(c) Type 3 Wind Turbine-Generator: Double-Fed Asynchronous Generator

(d) Type 4 Wind Turbine-Generator: Full Power Conversion

3

3GEAR BOX

WOUND ROTORINDUCTION GENERATOR TRANSFORMER

GRID

IGBT POWER CONVERTORS

3

GEAR BOX

SYNCHRONOUSGENERATOR TRANSFORMER

GRID

Rectifier

33

IGBTInverter

Appendix I: Wind-Turbine Generation (WTG) Technologies

46

(c) Type 3 Wind Turbine-Generator: Double-Fed Asynchronous Generator

(d) Type 4 Wind Turbine-Generator: Full Power Conversion

3

3GEAR BOX

WOUND ROTORINDUCTION GENERATOR TRANSFORMER

GRID

IGBT POWER CONVERTORS

3

GEAR BOX

SYNCHRONOUSGENERATOR TRANSFORMER

GRID

Rectifier

33

IGBTInverter

Type-3 Wind Turbine Generator: Double-Fed Asynchronous Generator

Type-4 Wind Turbine Generator: Full Power Conversion

4/4/17Planning and Design 16

Extreme Event ModelingTechnical Details—Deterministic N-k

TestcasesSmall/Medium:IEEE30/IEEE300Large:AmericanElectricPower(proprietary)

Keyassumption:Intentionaladversary,withfullknowledgeofthesystemstructure/parametersDeterministicmodel:Adversarycandisablekcomponents(generatorsorlines)inthesystemSeveritymeasure:Worst-case loadshedgivenkconcurrentdisablementsofcomponentsPowerflowphysics:Linearized(aka“DC”)powerflow,duetopresenceofbinarydecisionvariablesKeyfindings:• (Exact)Worst-casealgorithmsenable

quantificationofrelativecostsofprotectingagainstintentionalvs.natural/probabilisticadversaries

• Heuristicalgorithmsfailtoidentifyoptimalsolutions,oftenbylargemargins Lossofloadwhenrandomly

disabling4busesintheIEEE300-bussystem,versustheoptimal(worst-

case)attack

4/4/17Planning and Design 17

Extreme Event ModelingTechnical Details—Probabilistic N-k

Probabilisticmodel:eachcomponenthasalinefailureprobabilitySeveritymeasure:ProbabilityofN-kscenarioxLoadshedPowerflowphysics:Convexrelaxation—secondordercone(SOC),DCapproximationandNetworkflowapproximation

Keyfindings:• SOCandDCapproximationproducesimilar

results• ACfeasibilitytestforallmodelsindicatesame

levelofseverity• Betterpower-flowphysics(SOC)yieldsbetter

computationtime• ProbabilisticN-kwilldeliveranalysisthat

complimentsdeterministicN-k

Theworstkfailuresinapowersystemchangedependingontheprobabilisticmodeloffailure

WECC240