advanced distribution management system (adms) program · advanced distribution management system...

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Multi-yearProgramPlan2016–2020

OfficeofElectricityDeliveryandEnergyReliabilityAugust2016

AdvancedDistributionManagementSystem(ADMS)Program

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AcknowledgmentTheMulti-YearProgramPlan(MYPP)fortheAdvancedDistributionManagementSystem(ADMS)Program within the U.S. Department of Energy (DOE) Office of Electricity Delivery & EnergyReliability(OE)wasdevelopedthroughtheeffortsoftheTechnicalTeam.TheTechnicalTeamwasledbyEricLightner(OE)andcomprisedofthefollowingnationallaboratorymembers:RonMelton,KevinSchneider,andKaranjitKalsi (PNNL);MuraliBagguandBryanPalmintier(NREL);ScottBackhaus(LANL);DuncanCallaway(LBNL);andLiangMin(LLNL). Significant inputtothedevelopment of the MYPP was received from the DOE ADMS Program Steering Committee,comprisingthefollowingthoughtleadersandexpertsfromindustry:GaryRackliffe(ABB);KevinDing (Centerpoint Energy); Rick Geiger (Cisco Systems); Ted Thomas (Duke Energy); AvnaeshJayantilal(GE);PaulDuncan(GSDEnergyConsultants);RonAmbrosio(IBM),WayneCarr(Milsoft);andScottKoehler(SchneiderElectric).TheDOEfurtheracknowledgesreviewcommentsreceivedfromthefollowingprofessionaland/orutilityassociationsandtheirmemberorganizations: IEEE,EPRI,PSERC,EEI,NRECA,andAPPA.TheircommentshavebeenincorporatedintothiseditionoftheMYPP.Lastly,DOEacknowledgestheeffortsofEnergy&EnvironmentalResourcesGroup,LLC,(E2RG)fortheiroverallsupportindevelopingthisMYPP.

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TableofContents

ExecutiveSummary(DOE)..............................................................................................................iv

1.Introduction................................................................................................................................1

1.1. U.S.DepartmentofEnergy(DOE)DefinitionofADMS...........................................1

1.2. Vision.......................................................................................................................1

1.3. ChallengesandOpportunities.................................................................................2

1.4. MYPPTechnicalAreas.............................................................................................4

1.5. ProgramCoordination.............................................................................................5

1.6. ProgramGoalsandMulti-YearTargets...................................................................6

2.ProgramBenefits........................................................................................................................6

3.TechnicalAreas...........................................................................................................................6

3.1. ADMSDevelopmentPlatform.................................................................................7

3.1.1. Technicalgoalandobjective...............................................................................7

3.1.2. Technicalchallenge.............................................................................................7

3.1.3. Technicalscope...................................................................................................7

3.1.4 Statusofcurrentdevelopment...........................................................................7

3.1.5. Federalrole.........................................................................................................8

3.1.6. Technicalactivitydescriptions.............................................................................8

3.1.7. Milestones.........................................................................................................11

3.2. ADMSTestbed.......................................................................................................13

3.2.1. Technicalgoalandobjectives............................................................................13

3.2.2. Technicalchallenges..........................................................................................14

3.2.3. Technicalscope.................................................................................................15

3.2.4. Statusofcurrentdevelopment.........................................................................18

3.2.5. Federalrole.......................................................................................................19

3.2.6. Technicalactivitydescriptions...........................................................................19

3.2.7. Milestones.........................................................................................................21

3.3. ADMSApplications................................................................................................21

3.3.1. Technicalgoalandobjective.............................................................................21


3.3.3 Technicalscope.................................................................................................23


3.3.5. Federalrole.......................................................................................................24

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3.3.7. Milestones.........................................................................................................26

3.4. ADMSFoundational:AdvancedControl...............................................................27

3.4.1. Technicalgoalandobjective.............................................................................27


3.4.3. Technicalscope.................................................................................................28


3.4.5. Federalrole.......................................................................................................31


3.4.7. Milestones.........................................................................................................33

3.5. ADMSIntegrationwithEMSandBEMS.................................................................36

3.5.1. Technicalgoalandobjectives............................................................................36


3.5.3. Technicalscope.................................................................................................37


3.5.5. Federalrole.......................................................................................................40


3.5.7. Milestones.........................................................................................................42

4.ProgramManagementandStakeholderEngagement.............................................................42

4.1 ProgramPortfolioManagementApproach...............................................................42

4.2 StakeholderEngagement..........................................................................................43

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ExecutiveSummary(DOE)The AdvancedDistributionManagement System (ADMS) Programwas established in FY 2016withintheU.S.DepartmentofEnergy(DOE)OfficeofElectricityDeliveryandEnergyReliability(OE)SmartGridR&DProgram.ThemissionofthisnewProgramistodevelopasoftwareplatformcapableofintegratingcurrentandemergingdistributionutilitydata,includingreal-time,spatialdataofallconnecteddevices,andtosupportdevelopmentofvendorindependentmeasurementand control applications tomanage and optimize distribution utility operations for improvedreliability,resiliency,efficiency,assetprotection,andintegrationofdistributedenergyresources(DERs).Traditionaldistributionmanagementsystems(DMSs)facekeytechnicalchallengesinmanagingand optimizing a modernized grid. These challenges include managing the complexity ofoperatingthedistributionsystemswithincreasinglevelsofvariabilityinbothgenerationandloadassets; implementing a holistic approach to coordinate and manage grid operations fromtransmission, to distribution, and to local energy networks such as microgrids and buildings;utilizingreal-time,spatialdataofallconnecteddevicesindeterminingthegridstateforimprovedoperational planning, protection, control, and optimization; and achieving interoperable andintegratedoperationsbetweenlegacyandnewandemergingsystemsandapplications.TheADMSProgramisdesignedtoovercomethesechallengeswiththefollowingvision:By 2030, electric distribution gridmanagement systemswill be transformed from proprietary,vendor specific products to systems based on an open architecture and standards-based dataexchange thatwill enable integratingandmanagingall assets and functionsacross theutilityenterprise regardlessof vendoror technology. Through this transformation,DMSvendorswilladopt the DOE open, standards-based framework approach in the development of futureADMSproductofferingsandapplicationsforutilitycustomers.TheADMSProgramactivitiesareorganizedintothefollowingfivetechnicalareas:

• ADMSDevelopmentPlatform.Developanopen-sourceADMSapplicationdevelopmentplatformtofacilitatedevelopmentofADMSapplications,integratesuchapplicationsintooperationalsystems,provideatestingandevaluationframeworktoquantifybenefitsofADMSapplications,andprovideanextensibledevelopmentenvironmentthat isopen-sourceandaccessibletoallindustrystakeholders.

• ADMSTestbed.BuildacomprehensiveADMStestbedtotestADMSfunctionalitiesfrom

multiplevendorsthatspanmultipleutilitymanagementanddatacollectionsystems.Thistestbedwill beused for testingofADMSsolutionswithmultiple legacyandadvancedsystemsand for testingofuse cases toenableutilities toevaluate the full benefitsofADMSapplications.

• ADMSApplications.DevelopasuiteofADMSapplications,basedontheADMSplatform,

that:facilitateintegrationofhighlevelsofdistributedrenewables,improveoperationalvisibility,increasereliabilityandresiliency,reduceinfrastructureupgradecosts,facilitatetransmission support services, enhance power quality, enablemicrogrid coordination,andreduceenergycosts,allwhilemaintainingpersonnelandcustomersafety.

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• ADMSFoundational:AdvancedControl.Developcontroltheoryandsystemarchitecture

tocreateconcretedirectionsoftheoreticaldevelopmentconsistentwithdefinedmetrics;developtheorytoenablethedesignandanalysisofcontrolalgorithmsforatleast10,000DERsembeddedindistributionandtransmissionnetworks;andconductat-scaletestingbysimulationtovalidatetheperformanceofthedevelopedcontrolsolutionsandfosterdevelopmentoftransitionplansforindustrydemonstrations.

• ADMS Integration with Energy Management System (EMS) and Building Energy

ManagementSystem(BMS).DevelopanopenframeworktocoordinateEMS/DMS/BMSoperations;anddeployanddemonstratethecoordinatedoperationsintransformingorextendingexistingEMSandDMSapplications.Thisareawillalsoinvestigatemethodsanddevelop software tools that utilize data from sensor networks at different spatial andtemporalscalesto: forecastandmonitorgridhealth,detectincipientfailuresorfaultsandlocatetheirsource,managetheresponsetoagridevent,anddevelopstrategiesformitigatingfutureevents.

TheoutcomesandbenefitsoftheADMSProgram,throughinvestinginthefivetechnicalareas,arereflectedintheSMART(specific,measurable,agreedupon,realistic,andtimely)goaltargetsthathavebeendefinedbytheProgram. Namely,by2030,thedevelopedADMSsolutionswillachieveinteroperableoperationsofallprevailingdistributionutilityapplications,whilemeetingthefollowingspecifictargetsascomparedtothebaselineofutilityoperationswithoutaDMS:

• 40%reductioninDMSdeploymentcostsandtime• 80%reductioninoutagetimeofcriticalandnon-criticalload• 40%increaseinsystemefficiency• Supportoperationswith100%DER

Interimperformancetargetsin2020and2025havealsobeensetbytheProgramtoprogressivelyadvance toward the 2030 targets. Further, the Program will promote adoption of openarchitecture,standards-basedADMSsolutionsbyallDMSvendors,targeting20%,50%,and100%ofvendorshavingADMScapabilitiesin2020,2025,and2030,respectively.TheADMSProgramwillcloselycoordinatewiththeDOEGridModernizationInitiativeactivitiesundertaken by the Grid Modernization Laboratory Consortium and its partners, the DOEAdvancedResearchProjectsAgency-Energyprograms,otherOEprograms,andtheDOEOfficeofEnergyEfficiency&RenewableEnergyprograms.EffectiveindustryengagementwillbekeytothesuccessoftheADMSProgram.TheProgramhasdevisedindustryengagementstrategiesattheProgramlevelandforeachindividualtechnicalarea.ThisMulti-YearProgramPlan(MYPP)furtherdescribesspecificactivitiesand/ortasksineachofthefivetechnicalareasforeachyearfromFY2016through2020.ItisanticipatedthatthisMYPPwill be updated annually to reflect current state of advances, priority needs, and resourcesavailability.

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1.Introduction

1.1. U.S.DepartmentofEnergy(DOE)DefinitionofADMSTheDOEdefinesanadvanceddistributionmanagementsystem(ADMS)asasoftwareplatformcapableofintegratingcurrentandemergingdistributionutilitydata,measurement,andcontrolapplicationsfromvaryingvendors,andutilizingreal-time,spatialdataofallconnecteddevices,tomanageandoptimizedistributionutilityoperationsforimprovedreliability,resiliency,efficiency,assetprotection,andintegrationofdistributedenergyresources(DERs)1.AnADMSdiffersfromatraditionaldistributionmanagementsystem(DMS)inthatitintegratesoperationsacrossthenumeroussystemsandapplicationsthataretypicallyisolatedor,atbest,loosely coupled. These systems and applications include, but are not limited to: EnergyManagement Systems (EMS), Distributed Energy Resource Management System (DERMS),Supervisory Control and Data Acquisition (SCADA), Outage Management Systems (OMS),GraphicalInformationManagementSystems(GIS),AdvancedMeteringInfrastructure(AMI)andMeterDataManagementSystems(MDMS),CustomerInformationSystems(CIS),FaultLocationIsolation and Service Restoration (FLISR), mobile workforce tools, feeder load balancing andoptimization, voltage optimization control, and distribution state estimation. By integratingoperationsacrossallofthesesystemsandapplications,ADMStechnologiesprovideutilitieswithgreater ability to observe and control distribution systems so as to address rising operationalcomplexitieswhileensuringreliableandresilientoperations.

1.2. VisionThevisionoftheDOEADMSProgramis:

By2030,electricdistributiongridmanagementsystemswillbetransformedfromproprietary,vendorspecificproductstosystemsbasedonanopenarchitectureandstandards-baseddataexchangethatwillenableintegratingandmanagingallassetsandfunctionsacrosstheutilityenterpriseregardlessofvendorortechnology.Throughthistransformation,DMSvendorswilladopt the DOE open, standards-based framework approach in the development of futureADMSproductofferingsandapplicationsforutilitycustomers.

AdoptionofADMSbyindustrywillbefacilitatedbyhavingtheProgramactivitiesrespondtothefourdriversforADMSinvestments2describedasfollowswiththeabilitiesunderlyingthedriversproventhroughfielddemonstrations.

1.Resilience: theabilitytowithstandorrecoverfromanaturaldisasterquickly.

1ThetermDERsisusedhereintoincludecleanerandrenewabledistributedgenerationsystems(solarPV,wind,combustionengines,combinedheatandpower,microturbines,microhydropower,andfuelcells),electricvehicles,responsivebuildingloads,energystorage,anddemandresponse.

2ThefourdriversforADMSinvestmentsaredocumentedintheDOEreport,“Voicesofexperience:Insightsintoadvanceddistributionmanagementsystems,”February2015(http://en-ergy.gov/sites/prod/files/2015/02/f19/Voices%20of%20Experience%20-%20Advanced%20Distribu-tion%20Management%20Systems%20February%202015.pdf).

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2.Renewables: the ability to accommodate larger quantities of distributed energyresources.

3.Replacement: theabilitytosupplementlegacysystemsthatareunabletointegratewithnewtechnologiesandthatstaffcannolongersupport.

4.Regulation: the ability to accommodate changes that encourage reliability andefficiency.

1.3. ChallengesandOpportunitiesManaging and optimizing a modernized grid presents new challenges for traditionalDMS solutions. Key technical challenges, which also pose as opportunities for the ADMS toaddressandovercome,areidentifiedbelow:

• Accommodating growing deployment of DERs. The increasing penetration ofDERsintroduceshigherlevelsofvariabilityinbothgenerationandloads,promptingashiftinloadfollowingfromacentralpowerstationoperationalmodeltoadistributedsupplymodeltoeffectivelybalanceelectricitysupplyanddemandinrealtime.Thesechangesare increasing the complexity of operating distribution systems that deliver power toelectricityendusers.DistributionutilitiesfaceadditionalchallengesincoordinatingnewDERsthatmaybeownedbytheutility,third-parties,orutilitycustomers.

Theincreasingpenetrationofrenewablegenerationinmanyelectricaltransmissionanddistributiongridsisdecreasingtheavailabilityoftraditionalformsofgenerationusedtocontrol real power for balancing load and reactive power for regulating voltagemagnitude.ADMShastheopportunitytoleveragealargelatentcapabilityinthegridbycontrolling DERs to supplement and replace the control provided by traditionalgeneration,whileallowingthegridtooperatewithleanerreservemargins.

• Enabling an integrated system approach to electric power system operations andcontrol that spans transmission, distribution, and local energy networks such asbuildingsandmicrogrids.Thecurrentapproachtoelectricpowersystemoperationsandcontrolswasdevelopedoverthelastthreetofourdecadesinapiecemealfashion,withinnarrow functional silos for DMS and EMS software tools that offerminimal ability tocoordinateorinteract.Additionally,end-usetoolssuchasBuildingEnergyManagementSystems (BMSs), DERMS, and microgrid operations and control tools are typicallyindependent from grid control software systems. This approach does notmatch therealityofcomplexsmartgridsystems,whererenewablesandelectricenergystorageinthedistributionnetworkandchangingend-userconsumptionpatternshavesignificantimpactthroughouttheentirenetwork.Additionally,utilitiesneedtheflexibilitytochoosethe vendor solution that best fits their needs, andnot beoverly constrainedby crossplatformintegrationcostissues.

Future grid operations and control systems must be able to monitor, protect, andautonomouslyoptimizetheoperationofthegrid’sinterconnectedelements—fromthecentralanddistributedgeneratorsthroughthehigh-voltagenetworkandmedium-andlow-voltagedistributionsystems,tobuildingautomationsystems,tocontrollersforDERsandmicrogrids,andtoend-useconsumersincludingtheirthermostats,electricvehicles,appliances,andotherhouseholddevices.TheseprovideopportunitiesforADMStoapplyinteroperabilityandintegrationcapabilitiesandtodevelopnext-generationapplications

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for an integrated systemapproach. The integrated systemapproachwill alsohelp to“futureproof”thesesystemsintheeventthattheindustryevolvesinunexpectedways.

• Utilizingreal-time,spatialdataofallconnecteddevicestomanageandoptimizegridoperations. National grid modernization efforts to facilitate adoption of smart gridpracticesinrecentyearshaveincreasedadoptionofmeasurementtechnologies,suchasphasor measurement units (PMUs), smart meters, line sensors, and health conditionmonitors.Thislargeinfluxofdataoffersincreasedcapabilitiestodeterminethebehaviorof the electric system in real time; however, without the proper infrastructure andanalyticscapabilities,itcanoverwhelmdataacquisition,datastorage,anddataanalysissystems.TheopportunityareasforADMSapplicationsdevelopmentincludebigdatamanagement,integration of disparate data types, and advanced analytics. The developed ADMSapplicationswillprovideopen-sourcetoolscapableofhandlingmulti-sourceandmulti-scale data from disparate sources to manage and optimize grid operations in anenvironmentwithpotentiallyhighpenetrationofDERs.

• Achieving interoperable and integrated operations between legacy and new andemergingsystems.Currently,DMSandsomeADMScapabilitiesareprovidedinvendorspecificproducts.OneofthesinglelargestchallengesthatutilitiesfacewhenoperatingaDMSoranADMSsystemistheinterconnectionsandinteroperabilitywithperipheralsystemssuchasGIS,OMS,CIS,AMI,andSCADA,especiallywhenmultiplevendorsareinvolved. The high cost and lengthy time for individual utilities to invest in customsolutions for integrating varying vendor products, including the data historian, canbeprohibitive,particularlyforsmallandmediumsizeutilities.ADMS applicationswithin the individual electric power system vendor tool suites areemerging.GiventheearlynatureofADMSdevelopment,thetimingisopportuneforDOEtointroduceopenarchitecture,standards-basedsolutionstovendorsforacceptanceintheirdevelopmentapproachesofADMSforproductofferingstoutilitycustomers.

• Accommodating new architectural and operational constructs for distributionnetworks.Currently,newdistributionsystemarchitecturesandoperationalmanagementare being developed for distribution networks. These are being put forth in thedistributionsystemplatformprovider(DSPP)modelaspartofNewYork’sReformingtheEnergy Vision activity, and in the distribution system operator (DSO)modelinCalifornia.Furtherworkalongtheselineswilloccurtosupportincreasedlevelsof renewable resourcepenetration indistributionnetworks.3 Thisworkmaynaturallylead to distribution system operational models that require new classes ofADMSapplicationssupportingthenewarchitecturesandassociatedfunctionality.

3DeMartini,Paul,andLorenzoKristov,“DistributionSystemsinaHighDistributedEnergyResourcesFuture,”FutureElectricUtilityRegulationReportNo.2,Oct.2015,LawrenceBerkeleyNationalLaboratory(https://emp.lbl.gov/sites/all/files/FEUR_2%20distribution%20systems%2020151023_1.pdf).

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1.4. MYPPTechnicalAreasTheDOEADMSProgramwasestablishedtodevelopanopenADMSplatform,developnewADMSapplications, validateperformance capabilities, and transfer the open, standards-basedADMSsolutions to industry for adoption. The performance capabilities developed through theDOEprogramwillbevalidatedbothforinteroperableoperationsamongintegratedapplicationsandforimprovedutilityoperations.Theapplicationswillincludethecurrent,fullsuiteofdistributionutilityapplications(distributionautomation,outagemanagement,meterdatamanagement,andenterprisemanagement),aswellasnewapplications(highpenetrationofDERs; integrationoftransmission, distribution, and building energy management systems; integration of multiplemicrogrids; integration with other critical infrastructural systems). The improved utilityoperationswillbevalidatedagainstthefollowingperformancemetrics:reliability;deploymentcosts;reductioninfailedcomponents;reductioninrestorationtime;reductioninsystemlosses;systemefficiency;andpercentageDERintegratedwiththegrid.KeytechnicalareasoftheDOEADMSPrograminclude:

• ADMSDevelopmentPlatform.Developanopen-sourceADMSapplicationdevelopmentplatformtofacilitatedevelopmentofADMSapplications,integratesuchapplicationsintooperationalsystems,provideatestingandevaluationframeworktoquantifybenefitsofADMSapplications,andprovideanextensibledevelopmentenvironmentthat isopen-sourceandaccessibletoallindustrystakeholders.

• ADMSTestbed.BuildacomprehensiveADMStestbedtotestADMSfunctionalitiesfrom

multiplevendorsthatspanmultipleutilitymanagementanddatacollectionsystems.Thistestbedwillbeused for testingofADMSsolutionswithmultiple legacyandadvancedsystemsand for testingofuse cases toenableutilities toevaluate the full benefitsofADMSapplications.

• ADMSApplications.DevelopasuiteofADMSapplications,basedontheADMSplatform,

that:facilitateintegrationofhighlevelsofdistributedrenewables,improveoperationalvisibility,increasereliabilityandresiliency,reduceinfrastructureupgradecosts,facilitatetransmission support services, enhance power quality, enablemicrogrid coordination,andreduceenergycosts,allwhilemaintainingpersonnelandcustomersafety.

• ADMSFoundational:AdvancedControl.Developcontroltheoryandsystemarchitecture

tocreateconcretedirectionsoftheoreticaldevelopmentagainstdefinedmetrics;developtheoryforhierarchical,decentralized,distributed,andrisk-awarecontrolstoenablethedesign and analysis of control algorithms for at least 10,000 DERs embedded indistribution and transmission networks; and conduct at-scale testing by simulation tovalidate performance of the developed control solutions and foster development oftransitionplansforindustrydemonstrations.

• ADMS Integration with EMS and BMS. Develop an open framework to coordinateEMS/DMS/BMSoperations;anddeployanddemonstratethecoordinatedoperationsintransforming or extending existing EMS and DMS applications. This area will alsoinvestigatemethodsanddevelopsoftwaretoolsthatutilizedatafromsensornetworksatdifferentspatialandtemporalscalesto:forecastandmonitorgridhealth,detectincipientfailures or faults and locate their source, manage the response to a grid event, anddevelopstrategiesformitigatingfutureevents.

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1.5. ProgramCoordinationTheADMSProgramsupportsandcontributestotheDOE’sGridModernizationInitiative(GMI)toenableamodernizedgrid.TheGMIresearch,development,anddemonstration(RD&D)agendais described in the DOE GridModernizationMulti-Year Program Plan (MYPP).4 As such, theprogramactivitieswillcloselycoordinatewiththoseamongthemorethan80projectsawardedin 2016 to the Grid Modernization Laboratory Consortium (GMLC) to address the prioritiesidentifiedintheGridModernizationMYPP.ThiscoordinationwithGMLCprojectswillinvolvethe14DOENationalLaboratoriesservingasthecorefortheGMLC,aswellastheirpartners,includingdozens of industry, academia, and state and local government agencies across the country.ExamplesofsomespecificGMLCprojectsforcoordinationbytheADMSprogramarelistedbelow:

GMLC1.1: FoundationalAnalysisforGridModernizationInitiative(forsystemcontrolmetrics)

GMLC1.2.1: GridArchitectureGMLC1.2.2: InteroperabilityGMLC1.2.3: GMLCTestingNetworkGMLC1.2.5 GridSensingandMeasurementStrategyGMLC1.4.1 StandardsandTestProceduresforInterconnectionandInteroperabilityGMLC1.4.2 Definitions,Standards,andTestProceduresforGridServicesGMLC1.4.15 DevelopmentofIntegratedTransmission,Distribution,and

CommunicationModels

TheADMSProgramwillalsocoordinatewithrelated,advancedenergytechnologyR&DeffortsfundedbytheDOEAdvancedResearchProjectsAgency-Energy(ARPA-E)programs.Forexample,ADMS control and integration will leverage work being performed by ARPA-E’s NetworkOptimizedDistributedEnergySystems(NODES)programthatisdevelopinginnovativehardwareandsoftwaresolutionstointegrateandcoordinategeneration,transmission,andend-useenergysystemsatvariouspointsontheelectricgrid. Inaddition,newpowersystemnetworkmodelsand new grid optimization algorithms developed through the ARPA-E’s Generating RealisticInformation for the Development of Distribution and Transmission Algorithms (GRID DATA)programwillbecloselymonitoredforADMSapplications.Existingpartnershipswithutilities,manufacturers,private-sectorresearchinstitutions,andstateand local government agencies will be leveraged for coordination on ADMS planning,development, and implementation. These partnerships include those established underGMLCprojects,throughtheDOEuserfacilities(suchastheElectricityInfrastructureOperationsCenter [EIOC] at Pacific Northwest National Laboratory [PNNL] and the Energy SystemsIntegrationFacility[ESIF]atNationalRenewableEnergyLaboratory[NREL]),andthroughotherexistingDOEprogramsattheOfficesofElectricityDelivery&EnergyReliability(OE)andEnergyEfficiency&RenewableEnergy (EERE). Further, theADMSProgramhasassembledaSteeringCommitteeconsistingofninemembersfromindustry5toguidetheProgramanditsprojectstofurtherstrengthentheworkwithindustrythroughoutallstagesofADMSRD&D.

4DepartmentofEnergy,“GridModernizationMulti-YearProgramPlan,”April2015(http://en-ergy.gov/sites/prod/files/2016/01/f28/Grid%20Modernization%20Multi-Year%20Program%20Plan.pdf).5TheADMSProgramSteeringCommitteecomprisesthoughtleadersandexpertsfromindustryinfieldspertinenttoADMSdevelopmentandapplications.CurrentCommitteemembersarefromthefollowingcompanies:ABB,Inc.;CenterPointEnergy;Cisco;DukeEnergy;GE;GSDEnergyConsultants;IBM;Milsoft;andSchneiderElectric.

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1.6. ProgramGoalsandMulti-YearTargetsBy2030,thedevelopedADMSwillachieveinteroperableoperationsofallprevailingdistributionutilityapplications,whilemeetingthefollowingspecifictargetsascomparedtothebaselineofutilityoperationswithoutaDMS:

• 40%reductioninDMSdeploymentcostsandtime• 80%reductioninoutagetimeofcriticalandnon-criticalload• 40%increaseinsystemefficiency• Supportoperationswith100%DER

Interimperformancetargetsin2020and2025havealsobeensetbytheProgramtoprogressivelyadvance toward the 2030 targets. Further, the Program will promote adoption of openarchitecture,standards-basedADMSsolutionsbyallDMSvendors,targeting20%,50%,and100%ofvendorshavingADMScapabilitiesin2020,2025,and2030,respectively.

2.ProgramBenefitsADMSaimstoreducerestorationandrecoverytimesforoutagesundernormalconditionsandunderall-hazardsevents.Thisleadstoareductionintheeconomiccostsofpoweroutages.Theability of ADMS to operate distribution level assets—owned by utilities, customers, and thirdparties—inamoreflexiblemannerwillreducetheneedforoverlyconservativereservemargins,directlyreducingtheircostsandimprovingsystemefficiencies.Further,ADMSwillprovidetheincreasedobservabilityandcontrollabilityofelectricdistributionsystems,resulting inreducingtheuncertaintyassociatedwithDERs. The increasedobservabilityandcontrollabilitywill alsoallowdistributionutilitiestoadopttechnologiestobettercontroltheirgridforhigherreliability,improvedpowerquality,securityofpeopleanddata,andresiliencytonaturaldisastersandotherthreats. Together, these result in amore reliable, lower cost power system, which, in turn,provideslong-lastingU.S.economicimpact.Hence,throughthenewcapabilitiesandapplicationsenabledbytheADMSProgramefforts,theProgramwilldeliverbenefitstoutilityplanningandoperationsinadvancingeachofthefollowingperformancemetrics:

• costreduction;• reliabilityenhancement;• resilienceenhancement;• systemefficiencyimprovement;and• highDERpenetration.

ThequantitativebenefitstargetedfordeliverybytheDOEADMSProgramaremanifestedintheProgram’s2030goaltargetsandinterimtargetsin2020and2025(SeeSection1.6).

3.TechnicalAreasTheADMSProgramR&Dactivitiesarestructuredintothefivetechnicalareassummarizedintheprevioussection.Eachofthesetechnicalareasisdescribedinfurtherdetailbelow.

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3.1. ADMSDevelopmentPlatform

3.1.1. TechnicalgoalandobjectiveThegoaloftheADMSdevelopmentplatformistoenablecost-effectiveandtimelydevelopment,integration, anddeploymentofnewADMSapplications that takeadvantageof the increasingamountofdatamadeavailablethroughutilitydeploymentsofsmartgriddevicesandsystems.Key to this goal is developing a vendor agnostic interconnection layer so that variousDMS systems, their peripheral systems, and other applications can be more effectivelyinterconnected.AsdescribedintheDOEdocument“VoicesofExperience,”6utilitieswouldliketo reduce integration costs andhave access to best-of-breed solutions frommultiple vendorswhileavoidingstrandinginvestmentsinlegacysystems.

3.1.2. TechnicalchallengeADMSdeploymentsintegratedataandinformationfromavarietyofsystems.Dependingonthesizeoftheutilityandpriorinvestmentsinsystemstosupportdistributionsystemoperations,thedeploymentandintegrationtaskgrowsincomplexity.Keytechnicalchallengesinclude:

• Enablingdeploymentofmulti-vendorADMSapplications.• EstablishinganADMSapplicationdevelopmentmethodologyandenvironmentthatisnot

vendordependentbutthatcanbethesourceofADMSapplicationsusable inmultiplevendorsystems.

• DemonstratingbenefitsofADMSapplications.• Providingmeans of integration of ADMS applications with existing data and systems,

including, but not limited to: EMS, DERMS, SCADA, OMS, GIS, AMI andMDMS, CIS,andFLISR.

3.1.3. TechnicalscopeToacceleratethedeploymentofADMStechnologiesthistechnicalareawillfocusonbuildinganopen-source ADMS application development platform to facilitate development ofADMSapplicationsandovercomethebarrierstoADMSdeployment.Theopen-sourceplatformwillbecalledOSPRREYS(OpenSourcePlatformforReliableandResilientElectricitYSystems).TheOSPRREYS platformwill facilitate ADMS application development and integration of suchapplications intooperational systems;providea testingandevaluation framework toquantifybenefitsofADMSapplications;andprovideanextensibledevelopmentenvironmentthatisopen-sourceandaccessibletoallindustrystakeholders.Additionally,theOSPRREYSplatformwillbereplicableatnationallaboratories,universities,andvendorfacilitiestoensurethatthedevelopedcapabilitiesbenefitthelargestpossiblenumberofindustrystakeholders.

3.1.4 StatusofcurrentdevelopmentCurrentlyADMScapabilitiesareprovidedinvendor-specificproducts.ThereissomemovementbythevendorstoapplycurrentindustrystandardssuchastheCommonInformationModel(CIM),butthisisnotbeingperformeduniformly.ADMS applicationswithin the vendor tool suites are emerging, and independent applicationsoutsidethevendortoolsuitesarealsogainingmomentum.GiventheearlynatureofADMS,there

6Ditto,Ref.2.

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isopportunitytoinfluencethemannerinwhichvendorsapproachprovidingthesecapabilitiestocustomers.Thevendorcommunityisalsosegmentedinseveralwaysthatreflectboththesizeandhistoryofthevendorsaswellasthesizeandnatureofthetargetutilitycustomers. Generallyspeaking,someofthelargerutilitiesarebeginningtoconsidervendorsuppliedtoolsuitesthatmayincludeADMS capabilities, while others are defining their own requirements for such systems withsubsequentplanstobuildsuchanenvironmentandtoolsuitethemselves. Smallandmediumsizeutilitiestypicallyhaveamorelimitedsetofoperationaltoolsthatmaynot,forexample,evenincludeaDMS.AkeychallengeistoenableADMSapplicationsforthesmall-andmedium-sizedutilities.

3.1.5. FederalroleThefederalgovernmenthastheopportunitytoactasaneutralthirdpartydevelopingandmakingavailablevendoragnostictechnologythatisusefultoboththevendorsandutilities.Inaddition,the federal government can help establish the benefits of ADMS applications and relatedtechnologytohelpvendorstofocustheireffortsonproductsthatprovidemaximumbenefittocustomers. Through taking an open architecture, open-source, reference implementationapproach, the federal government enables market competition and the creation of “best ofbreed”applicationsavailabletoallutilities.

3.1.6. TechnicalactivitydescriptionsThe technical activities forADMSPlatformdevelopment anddeploymentwill bebasedon anincrementalsoftwaredevelopmentandreleaseapproach.Suchanapproachestablishesaninitialdocumentationof functionalrequirementsthataretranslated intoanarchitectureandsystemconceptualdesign.Fromthat,thescopeofthefirstreleasecycleisdefined,followedbyphysicaldesignandimplementationofthatscopeoffunctionality.Eachreleasecyclefinisheswithtesting,release,andthenreviewofthefunctionalrequirements,followedbyconceptualdesignandthendefinitionofscopeforthenextrelease.Throughouttheperiodofperformance,thisactivitywillengagewithandreceiveguidancefromanIndustrialAdvisoryBoardcomprisedofindustrystakeholdersfrombothutilitiesandvendors.Theplannedannualface-to-facemeetingswillbeaugmentedbyquarterlywebmeetings.

Year1ThefirstyearinvolvesprojectstartupactivitiesincludingplanningandformationoftheIndustryAdvisory Board along with tasks supporting the design and first development cycle for theADMSPlatform.Twokeytasksare(1)documentationoftheADMSPlatformconceptualdesignand(2)functionalrequirementsspecification.Theconceptualdesignwillincludeahighlevelarchitecture,aninitialversionofwhichisshowninFigure1,andadescriptionofthegeneralfunctionalityandoperationtobeachievedintheADMSPlatform.

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The central feature of Figure 1 is thedevelopmentplatformthatprovidesuserswith the ability to develop ADMSapplications. The platform includesconsiderationforthevariousinterfacestodata or other information; supportingfunctions such as power flow analysis,optimization calculations, and so forth;application user interface definition andmapping; access to a distribution utilitysimulation capability; and platformconfigurationtools.AsshowninFigure1,the platform provides interoperabilitythrough the standards-based data bus,datamodels, anddata ingest or interfacefunctions; and through the data bus andrelated components, interoperations takeplace with the distribution system

simulationtoolsandvariouscommercialdistributionsystemoperationtoolssuchasDMS,OMS,GIS,andEMS.Inthedevelopmentplatform,anInput/Outputfunctionprovidesthedatamodelsanddefinedinterfacetothecommondatabus.Datawillgenerallybeaccessedviathedatabus.Theavailabledatamaycomedirectly fromthedistributionsimulator (cleandata)or fromthedistributionsimulatorviathecommercialtools(morerealistic,noisydata).MajorTaskElementsforyear1include:

• IndustrialAdvisoryBoard(IAB).TheIABwillbeformedduringthefirstquarterofyear1.Aface-to-faceworkshopwiththeboardisplannedneartheendofthefirstquarterorbeginningofthesecondquarter.Subsequently,quarterlywebmeetingswillbeheldwiththeboard.

• ADMSgapanalysis. DMShasexistedinvariousformsforwelloveradecadebutthey

generally take the form of a collection of loosely integrated systems. Before theADMSProgramcandeterminethebestcourseforwarditwillbenecessarytomapoutthestatusofthecurrentgenerationofDMStechnology.ThiswillincludenotonlycoreDMSsystems,butalsoperipheralsystemsthattieintoDMS(forexample,GIS,OMS,CIS,AMI,SCADA, etc.). This task will be completed in cooperation with the IAB to ensure anaccuraterepresentationofcurrentstateoftheart,whichwillallowtheprogramtofocusonthegaps.Themappingofthecurrentstatusofindustrytoolswillbeachievedthroughvariousmeans;thesewillinclude,butnotbelimitedto,conversationswithmembersoftheIAB,conversationswithutilitystakeholders,reviewofvendorpromotionalmaterial,reviewoftradepublications,attendanceattradeshows,anddiscussionswithvendors.

• ADMS platform conceptual design and functional requirements specification. A

conceptualdesignwillbeprepareddocumentingthetechnicalapproachandhigh-levelsystemarchitecturefortheADMSplatform.Thiswillinformthefunctionalrequirementsspecification activity and will be updated based on the results of the functionalrequirements documentation. A key consideration in the conceptual design and thefunctional requirementsdefinitionwill be identification and specificationof interfacesbetween the ADMS platform and an application development environment and the

Figure1.OpenSourcePlatformforResilientElectritYSystems(OSSPREYS)

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subsequentinterfacesneededbyADMSapplicationsinoperationaluse.Oneofthesinglelargest challenges that utilities face when operating an ADMS system is theinterconnectionsand interoperabilitywithperipheral systemssuchasDMS,GIS,OMS,CIS,AMI,andSCADA.

To address the challenges associated with multiple systems provided by differentvendors, and the lack of transmission and distribution (T&D) integration, theADMS platform will be an open-source platform, that is, OSPRREYS, supportingADMSinteroperability.AgeneralgoalforOSPRREYSdevelopmentistoprovideavendoragnosticinterconnectionlayersothatvariousDMSsystems,theirperipheralsystems,andotherapplicationscanbemoreeffectivelyinterconnected.Workinthistaskwillincludeparticipation in industry standards activities such as the CIM and IEC 61850. Thesechallengesanddesigngoalswillbereflectedinthefunctionalrequirementsspecification.

• ADMS platform development operational environment configuration. To support

OSPRREYSdevelopmentanduse,anoperationalenvironmentthatisrepresentativeofadistributionutilityisneeded.ThisenvironmentwillincludecommercialsoftwaresystemsthatwillinteractwiththeADMSplatform.

The software systems of interest include traditional DMS as well as other commonsystemssuchasGIS,OMS,CIS,AMI,andSCADA.Wherepossible,morethanoneinstanceof each systemwill be included to ensure diversity of vendors. Approaching vendorsabout providing their software for the ADMS platform will also be a path to engagevendorsascollaborativepartners.Inadditiontothetypicalsystemsatthedistributionlevel,EMSandBMSwillbeincludedbecauseoftheirimportancetofuturedistributionsystemoperations.

• ADMSplatformimplementation.Fromthecompletesetoffunctionalrequirements,a

setrepresentingcoreADMSplatformfunctionalitywillbeidentifiedforimplementationinthefirstsoftwaredevelopmentandreleasecycle.Adevelopment,testing,andreleasecycleofapproximatelysixmonthsisplanned.Anincrementalapproachallowsforearlyresultsandmanagestheriskinherentinallsuchprojectsofhavinganincompletesetofrequirements.

This implementation activity will include work to extend and adapt GridLAB-D7 withinterfacestothestandards-basedcommondatabusandforconfigurationviathesandboxconfigurationcapabilities.ThespecificdetailsofthesemodificationstoGridLAB-Dwillbedeterminedfromthefunctionalrequirementsspecification.

Year2Year2will continueADMSplatformdevelopmentwith twomoresix-monthdevelopmentandreleasecyclesplanned.Inaddition,workwillbeginonspecifyingADMSapplications,evaluatingthebenefitsofADMSapplications,extendingtoGridLAB-D,anddevelopingatransitionplanformovingtheADMSplatformfromtheDOEeffortstobroaderusebyutilitiesandvendors.

7GridLAB-D™isapowerdistributionsystemsimulationandanalysistooldevelopedbytheDOEatPNNL.Itisavailablefordownload(http://www.gridlabd.org/).

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Year3Akeytaskforyear3willbeafullfirstreleaseoftheADMSplatform.TheADMSplatformwillbedeployedatNRELandWashingtonStateUniversity(WSU)fortheiruseindevelopingatleastoneADMSapplicationeach. Inaddition,anADMSapplicationwillbedevelopedatPNNL. FormalevaluationofthebenefitsoftheADMSapplicationswilltakeplaceinyear3.WorkonGridLAB-Dto enhance its functionality in support of the ADMS platform will continue. Finally,implementationoftheADMStransitionplanwillbegin.

Year4Inyear4,theADMSplatformand/orexampleapplicationswillbedeployedatpartnerutilities.Thiswillprovideadditionalopportunitytoevaluatethebenefitsof theapplicationsdevelopedanddemonstratedinyear3andforutilitiestoprovidefeedbackonuseoftheplatformtodeveloptheirownapplications.Atleastoneeachoflarge,medium,andsmallutilitieswillbetargetedfordeployment.Year4willalsoinvolveextensiveinteractionswiththevendorcommunitytodefineapathforfullinteroperability of OSPRREYS-based applications and functionality into product environments.Requirementswill be defined for processes and technology to support realization of such anoutcome.Thelong-termgoalistheabilitytohaveameanstocertifythatapplicationsdevelopedinanOSPRREYS-compliantADMSapplicationdevelopmenttoolwillruninasimilarlycompliantvendorproduct.

Year5Theexpectedactivitiesinyear5willbeupdatesandimprovementstotheADMSplatformbasedonutilityandvendorfeedbackfromtheyear4deploymentsandtests,alongwithfurtherstepstowardtheabilitytohavecertifiedADMSapplicationsasdescribedunderyear4.

3.1.7. Milestones

Year1MilestoneName/Description EndDate TypeMilestone1:FormalizetheestablishmentoftheIABandscheduleadateforthefirstannualinpersonmeeting.

3monthsafterstart

QuarterlyProgressMeasure

Milestone2:CompletemappingofthecurrentindustrystateoftheartforADMS,includingacompletegapanalysis.

6monthsafterstart


Milestone3:Completespecificationsfortheopen-sourceADMSplatform.

12monthsafterstart


Milestone4:Completeopen-sourcereleaseofOSPRREYSanalyticsengine.

12monthsafterstart


AnnualSMARTMilestone:Completespecificationsfortheopen-sourceADMSPlatform.

12monthsafterstart

AnnualMilestone

Year2MilestoneName/Description EndDate Type

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Milestone1:CompleteinitialdevelopmentandconstructionofOSPRREYSinRichland,WA,includingareleaseoftheopen-sourcecode.ThiswillcoincidewithanIABworkshopinRichland,WA.

18monthsafterstart


Milestone2:CompletethespecificationsfortheinitialADMSapplications,includingthosetobedevelopedatNRELandWSU.

18monthsafterstart


Milestone3:CompletetheQAplanforOSPRREYSandADMSapplications.

18monthsafterstart


Milestone4:Completethedevelopmentofthetransitionplan.

24monthsafterstart



24monthsafterstart


AnnualSMARTMilestone:CompleteconstructionforinitialphaseofOSPRREYSinPNNLEIOCinRichland,WA.Thiswillbeheldinconjunctionwithanin-personmeetingoftheIAB.

24monthsafterstart

AnnualMilestone

Year3MilestoneName/Description EndDate TypeMilestone1:DeployADMSplatformsatNRELandWSU. 30months

afterstartQuarterlyProgressMeasure

Milestone2:CompleteADMSapplicationdevelopment,testing,andevaluationsatNRELandWSU.

30monthsafterstart


Milestone3:Completeoperator-basedevaluationsofADMSapplicationsintheEIOC.

36monthsafterstart



36monthsafterstart


AnnualSMARTMilestone:Executethetransitionplanfortheproject.Thiswillincludeintegratingresultswithtwoutilities,andreleasingalldevelopedopen-sourcecode.

36monthsafterstart

AnnualMilestone

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Year4MilestoneName/Description EndDate TypeMilestone1:ADMSapplicationstandardizationworkshopwithkeystakeholders

42monthsafterstart


Milestone2:DeployADMSapplicationatlargeutility.

48monthsafterstart


Milestone3:DeployADMSapplicationatsmallutility.

48monthsafterstart


Year5MilestoneName/Description EndDate TypeMilestone1:CompleterequirementsandplanforindustrysupportedcertifiedADMSapplicationdevelopment.

54monthsafterstart


Milestone2:UpdateADMSPlatformopen-sourcereleasebasedonfeedbackfromvendorandutilityusers.

60monthsafterstart


3.2. ADMSTestbed

3.2.1. TechnicalgoalandobjectivesThis technical area will establish a vendor-neutral ADMS testbed to accelerate industrydevelopmentandadoptionofADMScapabilitiesforthenextdecadeandbeyond.Thetestbedwillenableutilitypartners,vendors,andresearcherstoevaluateexistingandfutureADMSusecases in a test setting that provides a realistic combination of multiple utility managementsystemsandfieldequipment.TheactivitywillworkcloselywithanIndustrySteeringGroup(ISG)toensurethatelectricutility(“utility”)needsaremetandusecasesarerealisticandvaluable.The ADMS testbed will allow utilities and vendors alike to evaluate: (1) the impact ofADMSfunctionsonsystemoperations;(2)interoperabilityamongADMSsystemcomponents;(3)interactionswithhardwaredevices;(4)integrationchallengesofADMSwithlegacysystems;and(5)ADMSvulnerabilityandresiliency. Thetestbedwillprovidea less-expensiveandlower-riskalternativetoapilotdeployment,plustheabilitytosimulatecontingencyscenariosthatarenotpracticaltotestusingarealdistributionsystem.ThefindingsfromADMStestbedevaluationscanimproveeffectivenessofsubsequenttrialdeployments.TheseADMStestbedcapabilitieswillspeedindustryadoptionofADMSsystems,helpingtomeettheDOEGridModernizationMYPPgoalsby:increasinggridreliabilityandresiliencybyenablingthetestingofADMSresponsestoextremeeventsinalaboratorysetting;reducingtheeconomicimpactofoutagesthoughfaster,moreautomateddistributionrecovery;anddrivingdownthecostsofintegratingDERsbyincorporatingDERsintosystemoperations.ThisactivitywillsupporttheGridModernizationMajorTechnicalAchievementsbydevelopingatestbedtoevaluateADMScontribution to (1) advancedmodern grid planning on an analytics platform, (2) operating adistribution system reliably on lean reserve margins, and (3) controlling resilient distributionfeederswithhighpercentagesoflow-carbonDERs(50%).

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Theactivitywillestablishan integratedDMS/ADMStestbedwithintheGMLCTestingNetwork(GMLC-TN) that will allow utilities (including investor-owned utilities, municipalities, andcooperatives) and other stakeholders to evaluate technical, practical, and economicbenefits/costsofADMSfeaturesinahands-onenvironment.Thistestbedwillbevendorneutral,providinganopenandmodularframeworktointegratesoftwareandhardwarecomponentsfrommultiplevendors.Tosupporttheseaims,theactivitywillalsodevelopandvalidatetestcasesandscenariosbasedonspecificADMSfunctionality.As Figure 2 shows, the testbed will support multiple test cases by: (1) enhancing core DMScapabilities—suchasFLISRresponse,andVolt/VARoptimization(VVO)thatincludescoordinatedconservationvoltagereduction(CVR);and(2)evaluatingemergingADMScapabilities—suchascontrolling DERs along with regular distribution system operations, incorporating microgrids,distributed control schemes, and distribution automation.8 Furthermore, the testbed canfacilitateinteroperabilitytestingofemergingandlegacyutilityoperationssystemsfrommultiplevendors, and can simulate the control-room functions required to support forthcoming DERinterconnectionstandardstosupportgridservices.

3.2.2. TechnicalchallengesU.S.electricutilitiesareinvestingingridmodernizationtechnologiessuchasADMStomeettheircustomers’expectationsof“higherreliability,improvedpowerquality,renewableenergysources,securityoftheirdata,andresiliencytonaturaldisastersandotherthreatsthatdisrupttheflowof

8R.Das,V.Madani,etal.,“DistributionAutomationStrategies:EvolutionofTechnologiesandtheBusinessCase,”IEEETransSmartGrid,vol.6,no.4,pp.2166-2175,2015.

Figure2.ConceptualdiagramoftheproposedADMStestbed

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powerandtheirlifestyles.”9The“advanced”elementsofADMSspanmultipleutilitymanagementsystems and go beyond traditional DMS solutions by providing next-generation controlcapabilities. Thesecapabilities includemanagementofhighpenetrationsofDERs,closed-loopinteractions with BMSs, coordinated operation with microgrid controllers (MCs), and tighterintegrationwithanincreasinglydiversesuiteofutilitytoolsforMDMS,OMS,assetdata,billing,andmore.However,eachutilityelectricdistributionsystemisunique,thusmakingitdifficulttopredicttheutility-specifictechnicalandeconomicbenefitsofdifferentADMSfunctionssuchasFLISR,VVO,managing peak demand, and integrating BMSs, MCs, and DERMSs. It can be prohibitivelyexpensiveforindividualutilitiestoinvestinone-offorpilotdeploymentsofanADMSsimplytounderstandthetruebenefitsofthetechnology.Furthermore,itcanbedifficultorimpossibletotestthesystemresponsetofaultsandotherextremeeventsthatoccurinfrequentlybutwhereadvancedfunctionscanmakeaconsiderabledifference.Fromavendorperspective,emergingADMScapabilitiesinteractwithanincreasinglycomplexsystemofresources,makingitdifficulttodevelopandtestnewcapabilitiesinisolation.Toovercomethesechallenges,thisprojectwilldevelopanADMStestbedtoprovideutilitiesandvendors alike a neutral “sandbox” to try out, refine, and stress-test ADMS capabilities whileinteractingwithrealistic,butsimulated,powersystemsthatmayalsoincludeactualhardware.

3.2.3. TechnicalscopeThis activity will build a comprehensive ADMS testbed at the NREL’s ESIF to testADMS functionalities frommultiple vendors that spanmultiple utility management and datacollection systems. This testbed will address the challenges faced by utilities in adoptingADMStechnologybydevelopingarealisticADMStestbed—includingmegawatt-scalehardwaretestingandarbitrarysystemsimulation—thatwillenableutilitiestoevaluatethefullbenefitsofadvanced functionality and test the integration of DMS systems with multiple legacy andadvancedsystems. Thistestbedanditsusecaseswill leverageexistingandnewcapabilities—some proposed through other Grid Modernization projects—at NREL and complementarylocations at other national laboratories, including advanced large-scale grid simulation andextensivereal-timehardwareintegrationthroughpowerhardwareintheloop(PHIL).The ADMS testbed will house commercial DMS systems and complementary managementsystemsfrommultiplevendorstoenableutilitiestoworkwithknownpartnersandpotentiallytouseactual systemdatadirectly in the simulations. TheADMSunder testwill interfacewithahybridhardware/softwaresimulationthatrepresentsthedistributionnetworkbeingcontrolledandalsoothersystemswithwhichtheADMSmustinteract,includingtransmission-levelEMSandBMS.ThecoreADMStestbedwillconsistofagrowingmulti-vendorsuiteofoff-the-shelfutilitymanagement systems, complete with simulated control room functions at the ESIF. Thisenterprise-class systemwill control a simulated distribution system through a combination ofreal-time software simulation coupled with PHIL. This approach allows users to accuratelysimulate the distribution system at both steady-state and dynamic timescales, and to assessperformance and benefits of advancedADMS functions. Later in the project, other real-timesoftwaremodelsthatrepresentthemarketandtransmissionsystemswillbemodeledalongsidethePHILsimulation, thusallowing testingofADMS interoperabilityandsystem-level functions

9Ditto,Ref.2.

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such asmarket participation of DERs and providing ancillary services. Finally, a system-levelvisualizationwillclearlyportraytheADMSperformanceontimescalesfromsub-secondtoyears,andfromasingledevicetotheentireutilityregion.Thiscapabilitywillgreatlyhelpinassessingthebenefitsoftheusecasesundertest.Figure3showsafunctionalblockdiagramoftheproposedADMStestbed.TheADMSundertestconsistsofaDMS,SCADAinterface,andothersystems,whichmayincludebutnotbelimitedtoa DERMS, OMS, Demand Response Management System (DRMS), MDMS, MC, and BMS. ItreceivessignalsrepresentingdistributionsystemmeasurementsthroughSCADAandthroughtheADMStoSimulatorLink.ThesesignalsmayeithercomefromtheDMSinternalpower-flowsolver(existing,testedinYear1)orfromamulti-timescalesoftwaremodelof(mostof)thedistributionsystem(developedinYear1,testedinYear2).Themulti-timescalemodelsimulatesthestateofthenetworkinrealtimebasedontheADMScontrolsandproducesvirtualmeasurementsofthesystemstate.Elements of the distribution system of particular interest that are difficult to model arerepresentedbyactualpowerhardwareinNREL’sESIFlabs.Thismayinclude,butnotbelimitedto, distribution system equipment such as capacitor banks, protection equipment, and smartinverters.ThepowerhardwareactuatescontrolsfromtheADMSandinteractswiththesimulatedpartof thenetworkviaa real-timesimulator (RTS) thatcontrols theACpoweramplifiervoltages.Physicalmeasurementsfromthepowerhardware,includingvoltagesandcurrents,arefedbacktothesoftwaresimulationviatheRTStoclosethePHILloopandalsobacktotheDMSviaSCADAtoclosetheloop.

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Figure4illustratesthePHILarchitecturemoreexplicitly.Thisapproachallowsthereal-timesimulationofcompletedistributionsystemmodelswhilerepresentingpartsofthenetworkofparticularinterestinmoredetailedtransientsimulationorinhardware.InadditiontothePHILsimulationofthedistributionsystemnetworkitself,theADMStestbedalsoincludesascientificdatacapturesystem,real-timemodelingofbuildingsandBMSs,andanEMSthatmayberunatPNNL’sEIOCandbeconnectedremotelytotheADMStestbedatESIFasoneofthecapabilities.Interoperabilitybetweenutilitysystemswillbeanimportantaspectofthetestbedtomaintainproperoperationasnewfunctionality isaddedtodistributionsystemoperations. TheElectricPowerResearchInstitute(EPRI)hasrecentlydevelopedweb-based“testharnesses”toevaluatetheconformanceofDMScomponentstostandardizedmessagingprotocolsthatshouldsimplifyplatform-levelintegrationthroughtheenterprisemessagingbus. Underthisactivity,NRELwill

Figure3.ProposedADMStestbedfunctionalblockdiagram

18

workwithEPRItoprovidefollow-ontestingofinteractionswiththeADMS,itscomponents,andconnecteddistributionsystemhardware.ThisinteroperabilitywilleasetheabilitytoswapandtestmultipleADMScomponentsfromdifferentvendors.

3.2.4. StatusofcurrentdevelopmentNRELworkedwithDukeEnergyandGEGridSolutionstotestanewVVOapplicationthatcontrolsphotovoltaic(PV)invertersaswellaslegacycapacitorbankandvoltageregulatordevices.ThisprojectinvolvedbuildingalimitedprototypeDMStestbedthatlinkedtheGEGridSolutionsDMSandtrainingsimulationenvironmenttopowerhardwarethroughPHILtesting. Thissetupwasusedandisbeingfurtherdevelopedtoevaluatesmart-inverteroperationsandDMSintegrationofamockutilitydistributionfeeder,andtocomparethevalueofalternativeVolt/VARschemestotheutility.Thisprojectfocusedonthepotentialopportunitiestousesmartinverterstosupportvoltageregulationforpowerdistributionsystems,andhowtoincorporatesuchcapabilitiesinthesoftware tools used by the utility to support grid operations. Manufacturers do not usuallydisclose the detailed control strategy for advanced inverters; therefore, the ESIF’s ability toconnectactualinverterstodistributionsystemsoftwaremodelsthroughPHILtestingiscriticaltothis project. The capability developed in this project will serve as a foundation for theADMStestbeddevelopment,anditwillbefurthervalidatedusingausecaseidentifiedbytheISGinYear1.NRELisworkingwithEPRIandSchneiderElectricaspartoftheIntegratedNetworkTestbedforEnergy Grid Research and Technology Experimentation (INTEGRATE) program to advanceintelligent control of connected devices. This work involves demonstrating an end-to-endframework of communication and control technologies, integrating operation of differentdomainswithindistributionsystems(includingDMSs,demandresponseservices,andresidentialappliancescheduling)throughopen-sourcesoftwaretools.Theframeworkincludesanenterpriseintegration test environment, commercial ADMS from Schneider Electric, open-softwareplatforms,openHomeEnergyManagementSystem(HEMS)platform,communicationmodules,andapplications. Thisproject incorporatesopenstandards inamixed-standardenvironment,wheremultiplecommunicationprotocolswillco-existatESIF—muchastheymightatanelectricutility in the near future. Since this project will install Schneider Electric’s ADMS at NREL, itprovidesasecondsystemfromwhichtoevolvetheintegratedADMStestbed.AspartoftheDOEOESmartGridR&DProgram,ArgonneNationalLaboratory(ANL),EPRI,andNREL areworking on the “Structuring DMS andMC Interaction” project. The objective is todevelop integrated control and management systems for distribution systems with high

Figure4:DetailedillustrationofPHIL

19

penetrationsofinterconnectedgenerationfromrenewableenergysources(RES)aspartofthegridmodernizationprogram.ThisprojectiscloselyrelatedtotheDMSstudiescompletedinFY15byANLandEPRI.10 The“StructuringDMSandMCInteraction”projectwill leaddirectlytousecases related to integrating existing DMS platforms with ADMS, especially oriented towardDERMSandmicrogrids.ThePHIL,modeling,andsimulationcapabilitiesofESIFareuniquelysuitedtobeingatestbedfortheseADMSusecasesandtosupportevolvingrequirementsofpartnerutilitiesandvendors.

3.2.5. FederalroleGovernmentand thenational laboratoriesareuniquely suited toprovideaneutral testbed toevaluateexistingandfuturetechnologiesonacommonrealisticutilityframework.DoingsoalsoleveragesuniquegovernmentfacilitiessuchastheESIFtoincorporateactualhardwaredevicesatpower. Through the testbed, utilities and other stakeholders will obtain a comprehensive,crediblecost/benefitanalysisofADMSapplications,whichwillgreatlyhelputilitiestoplanADMSimplementationatsignificantlylowercostandwithoutdisruptingcustomers.RemovingsomeoftheuncertaintyandbarriersthroughindependenttestingatthetestbedwillhelpstimulatethemarketforADMSandhelputilitiesavoidthecostlydelaysincurredtoday.

3.2.6. TechnicalactivitydescriptionsToachieve thegoalsset forth, theactivity isdivided intosixmajor interacting tasks thatspanthreeyears.Figure5showsthetimingofthetasks,withadditionaldetailprovidedbelow.Task1involvesmanagingtheADMStestbedISGactivities,includingassemblinganADMStestbedISGcomprisingutilitypowerdistributionpersonnelresponsibleforDMS/ADMSdeploymentsandoperationswithintheirorganizations.TheISG’smainobjectiveistoidentifyanddeveloptheusecasesincollaborationwiththeprojectteam.TheISGwillalsoassistindevelopingthetestplans.

Task2istoidentifythefunctionalityandtestingrequirementsoftheADMStestbed.Inthistask,spanningthefirsttwoyears,usecasesandtestplanswillbedevelopedforYear2andYear3,includingtestingofadvancedfunctionsthatcovermultipleutilitysystems. Theusecasesmayinclude:(1)demonstrationofintegratedDMS,MC,andDERMSbenefitsatmultipletimescales;NRELwillworkcloselywithANLandEPRIonthisusecase;and(2)demonstrationofintegratedDMS,BMS,DRMS,andEMSbenefits,incollaborationwithPNNL(whereEMSmaybelocatedatPNNL)andasillustratedinFigure6.

10ArgonneNationalLaboratory,“Guidelinesforimplementingadvanceddistributionmanagementsystems:RequirementsforDMSintegrationwithDERMSandmicrogrids,”August2015.

Figure5.Timelineofthetasks

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Task3willdesignandbuildthesoftwareandhardwareinfrastructureforthetestbed.Circuitsfrompartnerutilities(distinctfromISGparticipants)willbepartlysimulatedandpartlyemulatedinthePHIL/controllerhardwareintheloop(CHIL)environment.TheactivitywillinvolveworkingwiththeutilitiesandvendorstoconnecttheproperhardwareequipmentintheESIFMedium-VoltageOutdoor Test Area (MVOTA) and Power System Integration Lab (PSIL) to represent aselectionofdistributionequipment inaserviceterritory. TheworkwiththeutilityandADMSvendorincludesinstallingtheADMSandenablingcommunicationswiththehybridhardwareandsoftwaresimulation.Thistaskwilloccurmainlyinthefirsttwoyears.InYear1,amulti-timescaledistributionsystemsimulationwillbedeveloped,consistingofaquasi-statictime-series(QSTS)simulationforthedistributionsystemandbuildings,inconjunctionwithaphasorsimulation(inOpal-RT’sePHASORsim)onthereal-timesimulator.NREL,EPRI,Opal-RT,andPNNLexpertiseinpower-systemmodelingwillbe leveragedinthistask. Themulti-timescaledistributionsystemsimulationwillbecoupledwithpowerhardware representing distributionsystemcomponentsofinterest.InYear2, the transmission power systemmodel and bulk market model alongwith the BMS and EMS will beintegrated to allow testing againststatic and dynamic system-scalephenomena.InadditiontosimulatingADMS operation in routine andanomalous conditions, this task willalso test interoperability of ADMScomponentsthroughacombinationofone-at-a-timesemantictestharnessesinpartnershipwithEPRI,andfullmulti-component interoperability as part oflarger tests. This task alsoaccomplishes the core vulnerabilitytestingneededforthesystem.Task4includesbaselinevalidationandbenchmarkingof theADMSuse casesidentified by ISG, executing the testplans, and performing economicevaluations. To ensure that thetestbed accurately models the utilityfeeders,abaselineof the testbedusecases on the modeled utility feederswill be performed by validating thebase results against real-world datareadily available from the utility. Atleast twoADMSuse cases,developedjointly with the ISG in Task 2, will betested using the testbed, and theresultswillbeanalyzed.Further,economiccostandbenefitimplicationsofthetestcasestoallthestakeholderswillbedevelopedusingthedatageneratedfromthetestbed.

Figure6.Depictionofexampleusecases

21

Task5istodevelopthevisualizationcapability.Multiple2Dand3Dvisualizationtechniqueswillbedevelopedtoprovideahuman-machineinterface(HMI)forthetestbed.Thiscapabilitywillsupportthereal-timemonitoringofthetestbed,aswellasthein-depthanalysisoftheADMSinpostsimulation.ThevisualizationcapabilitywillprovidetheframeworkinwhichthebenefitsoftheADMScanbeunderstoodandillustrated.Task6involvesdisseminatingtheresultsofteststoutilitycustomersandconductingoutreachtoall the stakeholders. A two-day-longworkshopwillbeconductedatappropriate locations todisseminatethefinalresults.SpecificstudiesconductedusingtheproposedADMStestedwillbedocumentedandsharedwiththeelectricpowerindustrythroughtechnicalreports,factsheets,journalarticles,conferenceparticipation,andothercommunications.

3.2.7. Milestones

Year1Task1–AssembleADMStestbedISGTask2–TestplanforYear2Task3–ADMStestbedmulti-timescalesoftwareandhardwareinfrastructuredevelopmentfor

Year2usecaseTask4–TestbedforADMSusingintrinsicDMSpowerflow:Performbaselinevalidationand

benchmarkingforthechosenusecase#0

Year2Task2–DeveloptestplanspecifyingteststobeconductedinYear3Task4–CompleteexecutionoftheYear2testplanTask6–One-dayworkshopdisseminatingthelessonslearnedinADMSusecase#1

Year3Task4–CompleteexecutionoftheYear3testplanTask6–One-dayworkshopdisseminatingthelessonslearnedinADMSusecase#2

3.3. ADMSApplications

3.3.1. TechnicalgoalandobjectiveTheoverarchinggoalofthistechnicalareaistodevelopanintegratedsuiteofADMSapplicationsthat:facilitateintegrationofhighlevelsofdistributedrenewables,improveoperationalvisibilityandsituationalawareness,increasereliabilityandresiliency,reduceinfrastructureupgradecosts,facilitatetransmissionsupportservices,enhancepowerquality,enablemicrogridcoordination,and reduce energy costs, allwhilemaintaining personnel and customer safety. ExistingDMSapplications provide a number of these services; however, these are rarely delivered in anintegrated solution with cross-application coordination that uses applications in common forinformationsuchasstateestimationandsystem identification. Applicationsareoftenvendordependentandlackaninteroperableinterface.

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3.3.2. TechnicalchallengesDMSapplicationsareoftenconstructedinasiloedmanner,buttheycananddointeract.

• Transmission support functions and VVO could impact state variables in oppositedirections (for example, if a transmission level request for added real power injectionfrom storage coincides with high voltage magnitudes at the point of injection ondistribution).

• Networkflowimpactsassetlife;therefore,assetmanagementandnetworkoptimizationobjectivesoverlap.

• DERMScanbeconfiguredforseveraldifferentapplicationswithrelatedbutnotidenticalobjectives, including applications that provide network optimization, applications thatreduce infrastructureupgradecosts,orapplicationsthatmaximizerenewableshostingcapacity.

• Demand response (DR) actions that reduce energy costs or avoid network capacityupgrade investments may degrade power quality and safety by creating phaseimbalances,andinvolveCISinadditiontonetworkcontrols.

• DRandDERMScanbeusedtoperturbnetworkflowconditionstofacilitateparameteridentificationsuchastopologyidentification.

ThechallengeofdevelopinginteroperableADMSapplicationsisnotonlytoavoidconflict,buttointeractinsynergisticwaysthatultimatelyenableoperatorstocoordinatenetworkmanagementand operation decisions. This requires platform development that facilitates applicationintegration (as described in Section 3.1); it also requires developments that ensure thatapplications are not only compatible, but resolve conflicting objectives in ways that aretransparentandcustomizablebyoperators.Ataminimum,thiswillrequireinformationsharingandrecognitionofsituationswhereapplicationobjectiveseithercompeteorcomplementeachother.However,insomecases,thesolutionmaybetocollapsemultipleapplicationsintosinglesubsumingapplications. Amajorchallengewillbetomakecomplexapplicationdecisionsandoperatorrecommendationstransparentandeasilyactedupon.TherearetechnicalchallengestotheproblemofmanaginginformationacrossanADMS.Oneofthese is the challenge of developing software functions that leverage advanced sensormeasurements todeliver real-timeand forecasted informationabout systemconditions. Thisinformationneedstobemadeavailable to relevantapplications ina timelymanner toensureseamlessoperation;itmustalsobeusefullyinterpretedandvisibletooperators.ThereisaneedtodeveloptoolstosupportsensorplacementwithmanyADMSapplicationsinmind.Itwillalsobe important to assemble and integrate state estimation algorithms that are both fast (forreliability and outagemanagement purposes) and accurate (to ensuremodel-driven networkdecisionsareasneartooptimalaspossible).Othersupportingfunctionsthatareofusetomanyapplications—includingpowerflowanalysis, loadandDERforecasting,andoptimizationunderuncertainty—mustalsobedevelopedascommonfunctionsavailabletoallDMSapplicationstoensureconsistentdecision-makingandtoavoidambiguities.Another challenge is that of managing applications with interacting objectives and controloutputs.DERandelectricvehicles(EVs)canandwillputunprecedentedstrainondistributionandtransmissionsystemcomponents.Potentialimpactsincludephaseimbalance,voltageexcursions,andcomponentoverload.WhiletheseimpactscanbemanagedwithDERMSapplications,DERsandEVsarealsovaluabletonetworkoptimizationroutinessuchasVVOandCVR.Synthesisofalloftheseconstraintsandobjectivesintoasingledecisionmakingplatformisdesirable;however,therearesignificantchallengeswithrespecttomanagingmodelfidelity,stateestimationerrors,

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andforecastuncertaintyaswellasdealingwiththecomputationalcostofsolvingaproblemthatmanages all of these issues simultaneously. Additionally, cybersecurity, communications, andinterfacesforapplicationsneedtobestandardizedtofacilitatesecure,interoperableapplicationsaswellasdatainputandoutput.Finally, significant challenges arise from the potentially uncertain availability of the DER forcontrol, including increasinghosting capacity, avoidingnetworkupgradecosts, andpreservingpowerqualitywith“smartgrid”strategiessuchasDERactuationorDR. Forexample,storagedevicesmayreachstateofchargecapacityconstraints,customersmayoptoutofDRprograms,orsolarPVoutputmightbereducedbycloudsordisconnectedfromcustomerrooftops.Whilethesetypesofresourcesmaybeabletodelivernetworkservicesforlessexpensethantraditionalmeasures (that involve, for example, conductor and capacity bank upgrades, capacitor bankinstallation,andvoltageregulators),theuncertainnatureoftheseemergingoptionsrelativetotheirtraditionalcounterpartsmustbefactoredintonetworkoptimizationandplanningdecisions.

3.3.3 TechnicalscopeADMS applications will be developed in three general domains: (1) Non-emergency networkoperations,(2)Reliabilityandoutagemanagement,and(3)Dataanalyticsandvisualization.Thetechnical scopewill focus on, at aminimum, integration of applicationswithin each domain.Theseareasandhowexistingapplicationsfitaredescribedbelow.Indescribingtheseareas,theaimistoexplorethescopeofapplicationsthatcanfallundertheumbrellaofanintegratedADMSandthegeneralitiesandinteractionsbetweenapplications.Non-emergency network optimization. Applications in this domain will support or subsumetraditional applications, including VVO, DERMS, DR, and transactive solutions for energymanagement. Emerging applications including transmission support services are also in thisdomain.Supportingfunctionsincludenetworkmodelidentificationandpowerflowanalysis.Reliabilityandoutagemanagement.ADMSapplicationscanavoidoutagesusingavailableDER.In addition, they must also respond efficiently during unplanned outages, avoid serviceinterruptions,andfacilitateequipmentrepairs.Acombinationofapplicationsassistsinreliabilitybyefficientcommunicationofrelevantfaultlocationsinadditiontoadvanceddataanalyticsandoperatorperformancevisualization.DERcanassistinmanagementofoutagesbycoordinatingresponsesorprovidingmicrogridsupport.Applicationswithinthisinclude,

• FLISR• OMS&Workforcemanagement• Riskbasedanalysisforfailure• RiskbasedanalysistoolswhenconsideringhighpenetrationofDERinthesolution• Predictivemaintenancescheduling• Reliabilityasaserviceinfuture

Bydevelopingappropriateanalyticsforoperatorsanddataintegration,thebestpathforoutageavoidanceandmanagementcanbedevelopedviaacommonuserinterface.Dataanalyticsandvisualization.Keyfactorsindataanalyticsandvisualizationincludeensuringoperatorsreceivetimely,usefulinformation.Integrationofforecastingandrisk-basedanalysiswith fast and accurate analysis and determination of the best choice for reconfiguration andcontrolselectionarekeyfeaturesforanADMS.Userinterfacesandapplicationintegrationareessential,alongwithappropriatesensorplacementandplanning.Traditionally,SCADAhasbeen

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consideredthebackboneofanalysis,formanagementofpeakandnon-minimumloadconditions,which often do not coincide with peak and minimum DER generation conditions. Withdevelopmentofdatadrivenapplications,andintegrationofcustomermeteringtotheinterfaces,theselectionofappropriatedataforimprovedsituationalawarenesswillbeessential.Supportingfunctionsfordataanalyticsandvisualizationinclude:

• stateestimationandtopology/parameteridentificationalgorithms;• loadanddistributedgeneration(DG)forecastingalgorithms;• switchplanmanagementalgorithms(discreteoptimization);and• analysistoolsbasedondataprovidedfromadvancedsensors,includingsynchrophasors

forpowerdistributionsystems,linesensors,smartmeters,andfaultindicators.

3.3.4. StatusofcurrentdevelopmentCurrentdevelopmentwithin theADMSapplicationenvironment is fundamentally limitedbyanumber of issues including application interoperability, data availability, communicationsinfrastructure,andinstitutionalissues.

• TherearesignificantADMSofferingsfromanumberoftheprevalentvendors,includingABB,GE(PowerOn),Schneider,andSiemens(Spectrum),withapplicationsincludingDMS,SCADA,OMS,andEMSsolutions.

• State-of-the-artanalyticsintegrationisbeingdevelopedonparallelpaths,withminimalefforttointegrateintootherADMSapplications.

• CommunicationsbackbonesaregenerallylimitedtoSCADAand(separately)smartmeterstatus; it is usually difficult to access or integrate full information from low-voltagenetworks.

• GIS updates tend to be disconnected from the operations environment; planning andoperationsarerarelyintegrated.

• Smartmeters location and phasing is often unknown, thus suggesting the need for aconsistentmodelvalidationframework.

• ADMSapplicationsoftenutilizeaggregatedorlumpedcharacteristicsfromthepointofinterconnectionatthesubstationduetothelackoffullcircuitmodels.

• Utilizing behind themeter DER as an ADMS application, or integratedwith DERMS islimited by the existing standards for integration including IEEE 1547. However, newstandardsreleasedin2017(estimated)mayallowgreateruseofthesecontrolcapabilitiesandpresentalargeopportunityforADMSapplications.

Somestate-of-the-artexamplesofdeploymentofapplicationsinclude:

• Outage location and restoration with smart meters at the Pacific Gas and ElectricCompany(PG&E).

• ForecastingintegrationforhighpenetrationofDERattheHawaiianElectricCompanies(HECO).

• EnhancedcommunicationandsituationalawarenessthroughafiberopticbackboneatChattanoogaPublicUtility.

3.3.5. FederalroleThefederalroleinthiseffortis,broadly,toenvisionanddeveloptheapplicationsandfunctionsthatwillhavemarketdemand/energy system impacton timescalesoutsideof shortbusinessdevelopmentcycles.AnintegratedADMSapplicationsuitewouldprovidesignificantbenefittodistributionsystemoperationandbuildout;however,thereisriskindevelopingthesetoolsdue

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to the technical challenges—including the difficulty of operating each class of applications.Specificactivitiesfortheuniquepositionoffederallaboratoryresearcherstopursueinclude:

• Advancing and applying fundamental knowledge for optimal decision making indistributionnetworks.

• ApplyingNationalLabadvancedHILtestingexpertiseandcapabilitiestodevelopnewanddeeperunderstandingofADMSapplications.

• Developingandapplyingacommonapproachacrosslabtestbedsforeffectivevalidationofemergingapplications.

• Solicitingstakeholderfeedbackandguidancethroughtechnicaladvisorygroups.

3.3.6. TechnicalactivitydescriptionsThetechnicaltasksareorganizedintothefollowingpriorityareas:Non-emergencynetworkoperations:

• Developand translatemodel identification and distribution state estimation functionsthatdelivercross-applicationinformationfornetworkplanningandoperationsdecisions.

• Develop,andtranslateintocross-applicationfunctionality,distributionnetworkmodelingstrategiesthatbalancecomputationalspeedwithapproximationerrors. (Forexample,linearmodelsaresolvedefficientlybut introduceerrors;fullnonlinearrepresentationsareaccurate,butimpracticallyslowinmostoptimizationapplications)

• Develop forecasting algorithms rooted in injection/extraction forecasts derived fromSCADA,customerAMI,solarproductiondata,andadvancedsensorsincludingPMUsfordistributionsystems.Investigatethetradeoffbetweenlocationalnetworkresolutionandaggregatedforecastaccuracy.

• Developoptimizationtoolsthatsupportarangeofapplicationsdependingonhowtheyare configured (including coordinating power flow for transmission-level integration,DERMSformaximizinghostingcapacity,peakdemandmanagementtodefercapacitorbankandconductorupgradeprojects,CVR,VVO,andphasebalancing).

• Develop optimization tools that facilitate network-level decision-making undermodeluncertainty,forecasterror,andfaultystateestimates.Thiscouldincludestochasticandrobustoptimizationalgorithmsforpowerflowcoordinationandoptimization.

• Developdecentralized“communicationsfailuresafe”algorithmsthatmaximizenetworkperformance. These could be applications emerging in the near term such as droop-controlled power electronic inverter solutions or longer term solutions such asdecentralizedcontrolalgorithms.

• Develop framework and implement methods that facilitate local transactions amongdistributedresourcesasanalternativetocommandandcontrolarchitectures.

• DevelopstrategiesfortuningandsendingDERcontrolsettings(thatis,invertervoltageregulation and ride-through functions) and legacy voltage regulation equipment tosupporthighpenetrationofrenewablegeneration.

• Platformcommunicationsstandardization:developtheminimumdatarequirementsforalloperationsapplicationssoastoallowforinteroperableapplicationdesignforADMSenvironments.

Reliabilityandoutagemanagement:

• Developanddemonstrate—inbothvendorspecificandopenplatforms—controllawsforautomated and manual contingency switching plans based on off-line power flow

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scenariocalculations.Modelsandpowerflowsolverscanbesharedwiththoseemployedinthenon-emergencyoperationsmoduleabove.

• Develop“rules”(orcontrollaws)forswitchingofflinethatcanthenbeimplementedinrealtimeusingonlyimmediatelyavailableinformation.Theserulescould,forexample,coordinate switching and restoration actions tomaintain power quality andminimizecustomerinterruptions.Off-linecontrollawscanbedeterminedusingcross-applicationpowerflowsolvers,models,andforecasts.

• Developnetworkconnectivityandvalidationanalysismodulesthatoperateas“reducedform” model identification routines (see Network Operations activities above) andresolvenetworkconnectivitymaps.

• BuildnextgenerationFLISRintoanintegratedADMSapplication,introducinglocationoffault/predictionofevent/automatedreconfigurationandalsoutilizationofDERcontrolsinsystemrestorationplan.

• Pursuepredictiveanalytics—basedonintegratedsensormeasurements,stateestimates,and power flow solutions—to determinemean-time-to-failure of equipment, plannedmaintenance,andprobabilityofoutages.

• Platformcommunicationsstandardization:developtheminimumdatarequirementsforall reliability and outage management applications so as to allow for interoperableapplicationdesignforADMSenvironments.

Dataanalytics&communications:

• Integratecustomeroutagecalls,complaints,andpictureswithplatformforfullrelationalsituationalawareness.

• Sensorandcommunicationstrafficstudy, linkedwithgridmodernizationobjectives,todetermine both optimal placement and communication requirements for a suite ofapplications.

• Developtoolstosupportverificationofcustomergenerationandloadperformance.

3.3.7. Milestones

Nearterm(1-2years)• Applicationinteroperabilityandcommunicationsstandardsreview.• Develop and implement network optimizationmethods that plan network operations

underuncertainty.• Develop tools to characterize systembehavior from large sets of heterogeneous data

sources.Theseeffortsaretoincludehistoricalandreal-timeanalysis.• DemonstratethatADMSsoftwarecanprovidetransmissionsupportbydeliveringprecise

andcertaincontrolloadbusstates(powerandvoltage)withhighprecisionandaccuracy.• IntegrationofsensorsanddatatoADMSapplicationsinterface.

Midterm(3-4years)• Integrate centralized and decentralized ADMS applications into existing DMS vendor

platforms.• DemonstrateasubsetofADMSfunctionsinHILtestbeds.• Extendnetworkoptimizationmethodsbeyondtransmissionsupportactionstocapture

hostofothernetworkmanagementobjectivesincludingmaximizingDERpenetration.• Enablevisualizationoffeedercharacterizationfromdataanalyticsefforts.

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• Translate power flow and state estimation tools to emergency networkmanagementapplications.

• IntegrateADMSalgorithmsintocommercialproducts.

Longterm(5+years)• DevelopcommercialADMSapplicationsthatdemonstrablyreduceratepayercostsand

improvereliability.• Integrate load bus control into transmission system operator market and

EMSapplications.

3.4. ADMSFoundational:AdvancedControl

3.4.1. TechnicalgoalandobjectiveIncreasing penetration of stochastic and variable renewable generation (both centralized anddistributed)intransmissionanddistributiongridsisdecreasingtheavailabilityoftraditionalformsofgenerationused tocontrol realpower forbalancing loadand reactivepower for regulatingvoltagemagnitude.Viewedinthisway,eventheexpansionofflexiblenaturalgasgenerationinmanypartsoftheU.S.mayultimatelybelimitedinitsabilitytoprovideadequatecontrollableresources.ThesechangesaredrivinganemergingtransitiontoleveragealargelatentcapabilityinthegridbycontrollingDERstosupplementandreplacethecontroloftraditionalgeneration.ThegoalofthisprogramelementistodeveloptheoreticalfoundationsfornewcontrolsolutionsfortheU.S.powergrid.ThefocusisondistributionsystemstosupportADMSprogramobjectivesfortransitioningthepowergridtoastatewherealargenumberofDERsareparticipatingingridcontrol, to enable the grid to operate with lean reserve margins and to enable resilientdistributionfeederswithahighpercentageoflowcarbonDERs.Inadditiontoitsemphasisondistribution,thisprogramelementwillalsoaddressintegrationofa large number of DERs into transmission and market operations (for example, automaticgeneration control [AGC], security constrained economic dispatch) using hierarchical controltheory and solutions, including the coordination between bulk system controls and the newcontrolsandoptimizationdevelopedinthiswork.ThelargeamountofR&Dalreadyperformedonthesetransmission-levelapplicationswillbeleveraged.Themajoroutcomeofthistechnicalareawillbetodevelopnovelcontrolsolutionsthataccessthe currently latent control capability in many DERs (generation, storage, and load), therebycreatingasystemthatismoreflexiblethanthelegacysystemwhileenablingthefollowing:

• Controlsystemsthataremoreeffectiveatkeepingoperatingconditionswithinprescribedsafetymargins.

• Contributiontoa33%decreaseincostofreservemarginswhilemaintainingreliabilityby2025.

• Enablingoftheinterconnectionofintermittentpowergenerationwithlessstrictmarginsandwithreducedneedforelectricalstorage—contributingtoa50%decreaseinthenetintegrationcostsofcleanenergytechnologiesby2025.

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3.4.2. TechnicalchallengesThese expected and ongoing changes in the nature of the electrical power system are beinghinderedbythelackofeffectivecontrolstomanagethesenewDERs.Wherecontrolsolutionsdoexist,theyarestressedorlimitedbecausetheyoperatetooslowly,donotprovidetherequiredcontrolaccuracy,donotenabletheactuationandcontrolofdiversesetsoflargenumbersofnewintelligentDERs,donoteffectivelymanagetheuncertaintyofDERresponseonmultipletimeandgrid scales,donot integrate thesenewDERswithexisting systemcontrols, andarenoteasilytransferrabletomultiplecontrolsettings.AdvancedcontrolsolutionsthatareabletoachievetheADMSvisionneedtohavethefollowingproperties:

• Easytointegratewithlegacysystemstoenabletheadoptionofadvancedsolutionsinasmoothmanner.

• AbletosimultaneouslycoordinatetheresponseofalargenumberofDERsthatmustalsobe compatible with scalable communications, information, and computationalarchitectures.

• AbletocontroldiversesetsofDERsthathavedistinctactuationinputs,deviceparametersand responses (continuous versus discrete), for example, PV inverters, battery energystorage (including electric vehicles), thermostatically controlled loads, distributedgeneration,fuelcells,etc.

• Mustmatchtheavailability,timeresolution,spatialgranularity,anduncertaintyofthesensordataandactuationsignals.

• Mustmanagevoltage,powerflow,andothernetworkconstraintswhileoptimizingthespatiotemporallycoordinatedactuationofalargenumberofdevices.

• Mustbeabletoaddressthefullrangeoftimescalesbyprovidingactuationoftheoptimalresourceatthecurrenttimewhileanticipatingtheeffectsonfutureflexibility.

• Mustmanageall sourcesofoperationaluncertainty, including intermittentdistributedgenerationandtheuncertaintyoftheresponseofalargenumberofDERs,tolimittherisktopowersystemsatminimumcost.

• Shouldsupportmarket-basedandnon-market-basedmodelsforDERengagementingridcontrolandtheintegrationwithbulkpowersystemmarkets.

3.4.3. TechnicalscopeAchieving the ADMS and Grid Modernization MYPP goals requires significant advancementsbeyond the current state of the technology and innovations in several interrelated areas:(1)Co-DevelopmentofControlTheoryandSystemArchitecturetocreateconcretedirectionsoftheoreticaldevelopment,whichwillbemeasuredagainstmetricsalsodevelopedinthisactivity;(2)TheoryforHierarchical,Decentralized,Distributed,andRisk-AwareControlsthatenablethedesignandanalysisofcontrolalgorithmsforatleast10,000DERsembeddedindistributionandtransmissionnetworks;and(3)At-ScaleTestingviaSimulationtovalidatetheperformanceofthedeveloped control solutions and foster development of transition plans for industrydemonstrations.Co-developmentofcontroltheoryandsystemarchitecture.Toachievetransferable,resilient,anddeployablecontrolsolutions,theexpertiseandexperienceoftheindustrypartners(OncorElectric Delivery, PJM Interconnection, United Technologies Research Center [UTRC]) will beleveragedtoco-developcontrolapproachesandtheoryalongwithcontrol,communications,andinformationsystemarchitectures. Althoughinformedby industry,co-developmentwillensure

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thesearchitecturesarealsoinformedbycontroltheoryandnotsimplypredefinedbywhatexiststoday.Thesearchitectureswillbeeconomicallydeployable,representindustryapproaches,anddemonstrateacontinuousevolutionfromtoday’slegacysystemstotheADMSfuturevision.Thearchitectureswillbedesignedtoberesilienttoimpactsofnaturalormaliciousevents.Thistaskwill collaborate with GMLC Task 1.2.1 by instantiating several of the architectures from thatprojecttodevelopcontrolsandbyperformingearlytestingandevaluationofthesearchitecturesagainstasetofcontrolsystemmetricsalsodevelopedwithinthistask.Bydirectly incorporatinggridsystems, informationandcommunicationssystems,andmarketsandindustrystructureintoasinglearchitecturalrepresentation,thespatiotemporalavailabilityand resolution of sensor data and actuation signals and their respective aggregation anddisaggregationareknownandclearlydefined.Thisupfrontarchitecturalintegrationenablesthedevelopmentofcontroltheoryandassociatedalgorithmsthatprovidehighperformanceoneachoftheproposedarchitectures.Italsoenablestheevaluationoftheperformanceofarchitectureastheinformationandactuationavailabilityiscompromisedbyanaturaleventorcyber-attacks.Atthesametime,thecontroltheorieswillinformarchitectureinGMLCTask1.2.1sothatusefulstructuralchangesthatsupportadvancedcontrolcanbesuggestedtootherpartsoftheindustry,thuseasingtheadoptionofnewcontrols.Theoryforhierarchical,decentralized,distributed,andrisk-awarecontrols. ToaccommodatemassivenumbersofDERs,architecturaloptionsthatemergeareexpectedtobehierarchicalanddistributedinnature.Atthedistributionnetworklevel,objectivesincludeestimatingtheboundsofDERflexibilityandcontrollingwithinthoseboundstomeet“external”demands(forexample,areferencesignalprovidedbytheISOBulkMarket)whilerespecting“internal”constraints(forexample, voltage magnitude and power flow limits on the host distribution circuits andsubstations).Thedevice-levelcontrolobjectivesdrilldeeperintothedevicephysicstodeveloptheory for aggregating large numbers of diverse DERs (generation, storage, and loads) intoensemblemodelsthatensurecomputationaltractabilitywhilesystematicallyincorporatingend-usedynamicsandphysicalconstraints.Theensemblemodelsshouldprovidepredictionsoftheaverageresponse,thefluctuationsanduncertaintyaboutthataverageresponse,andpredictionsoftheimpactsofbotheffectsonthepowersystemviathephysicsofpowerflow.Boththedevice-level and distribution network-level control systems should integrate seamlesslywith the ISOBulk/WholesaleMarketcontrolsystemsasdefinedinthecandidatearchitectures.Theproposedcontrolstrategiesareexpectedtobeopen,flexibleandinteroperable,exhibiting“plug-and-play”features,supportingdynamicsystemconfiguration,andrespectingtheexistingmechanismsandengineeringrealitieswithineachsubsystem.

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At-scaletestingviasimulation.Thecomplexityandnatureoftherequiredtestingisdeterminedbymanyfactorssuchasthesizeofthecontrolnetwork, locationandtypeofconstraints,timescale and time step, resource and load characteristics, aggregation of the loads and DERs,temporalandspatialdiversityoftheresources,andmarketsandregulations.Initialtestingwillincludesimplificationsofthesefactorstoprototypeandimprovethecontrol implementations.Thesesimplificationswillbereplacedwithmorerealisticassumptionsasthecontroldesignsreachadesired levelofperformanceandmovetowardamore integrated,at-scaletesting. Throughsimulationandmodeling,theperformanceofthecontroldesignswillbeassessedanditerativelyimprovedtoguaranteestabilityandreliabilityunderawidevarietyoftestscenarios. Thetestmethods,scenarios,andprotocolswillbedevelopedincollaborationwiththeindustrypartners(Oncor,PJM,UTRC)toensurerelevancetotheiroperatingconditionsandrequirements.Viaanindustryworkshop,thetestingresultswillbesharedandvettedwiththeindustrypartnersandawider set of industry stakeholders (utilities, regional reliability organizations, control systemdevelopers,andsoftwarevendors)toensuretheyunderstandthetestprotocolsandresultsandtheavailabilityofcontrolsolutionsforfuturedemonstrationandtransitiontopractice.Modelingandanalysisofadvancedgridcontrolapplicationsrequireafundamentalunderstandingofthebehaviorofthephysicalsystemsandanintegratedmodelingapproachthatportraysthecontrol system performance in the setting of the surrounding bulk generation, transmissioninfrastructure,marketsystems,reliabilitycoordination,andotheraspectsofutilityoperations.The control systems developed in this project will be tested for stability and performance inuniquenumericalsimulationenvironments.Developmentofthesimulationenvironments,andthe testing itself, will be an iterative process in close coordination with control theorydevelopmentsandwiththeADMSPlatformandADMSTestbedtechnicalareasandGMLCproject1.4.15,Co-SimulationofTransmission,Distribution,andCommunications(TDC).

3.4.4. StatusofcurrentdevelopmentThecurrentstateoftherelevantcontroltechnologyisperhapsbestdescribedbysummarizingafewcurrentDERcontrolapplicationsinuse,andafewbeingstudiedtheoretically,inthecontextofthedesiredcontrolandoptimizationsystempropertiesdescribedabove.Spinning reserve and critical peakmanagement fromdemand response. These applicationsgenerallyonlytapintoarelativelysmallnumberoflargeindustrialandcommercialloads.Thecontrol is typically open loop and not continuous. The time scales for actuation are slow(~10minutes), andaccuracy requirements arenot strict. These relatively loose requirementshave spawned mostly manually driven actuation that is not scalable, includes limited use ofoptimization,doesnotconsiderpowerfloweffects,andincludeslittleifanyconsiderationoftheuncertaintyoftheresponse.Batterystoragecontrol.Inmanyapplications,batterystorageprovidesasingle“anchor”enduseservice,11 forexample,peakshavingat substation transformers todeferupgrade investments.Control algorithms are typically focused on this single anchor servicewithout coordination atshorterorlongertimescalestoextractadditionalvalue.ThecontroltypicallydoesnotcoordinatewithotherDERsonthedistributiongrid,andpowerflowandvoltageeffectsaremostlyignored.

11[DOE/EPRI2013]“DOE/EPRI2013ElectricityStorageHandbookinCollaborationwithNRECA,”TechnicalReportSAND2013-5131.SandiaNationalLaboratories,July2013.

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PVinvertercontrol.Althoughsmartinvertersarenotwidelydeployed,thisareahasseenveryactivetheoreticalresearch.Approacheshaveincludedpolicy-basedopenloopcontrol12withoff-line consideration of power flow, advanced distributed algorithms that integrate power flowapproximations,13,14centralizedmethodsthat integratepowerflowrelaxations,15andmethodsthatutilizecontinuousODErepresentationsofpowerflow.16 Thesemethodshavemadegoodtheoreticalprogressonthespecificproblemofusingadvancedinverterstocontrolpowerflowand voltage along distribution circuits; however, they have not generally addressed broadercontrolandoptimizationissuesrelatedtointegratingtheseinvertercontrolswithcontrolofotherDERs or themanagement of uncertainty from intermittent generation or the control systemresponse.Ensemblecontrolof loads. Severaleffortshaveaddressed limitedaspectsofcontrolling largeensembleofloads,primarilythermostaticallycontrolledloads(TCL).Approacheshaveincludeddata-drivenstatisticalmodels,17 statistical stateestimation,18andFokker-Planck likemodelsofphysicalTCLdynamics.19,20However,theseeffortshavenotexplicitlymodeledorincorporatedfluctuationsoftheensembleloadandthepropagationofthatuncertaintytohigherlevelsofahierarchicalcontrolsystem.Also,theseapproachestypicallydonotconsiderthephysicsofpowerflowandtheeffectofensemblecontrolonthevoltagesattheloadsbeingcontrolled.

3.4.5. FederalroleEstablishinganoveloverallcontrolsolutionfortheU.S.powergridisataskthatismuchbeyondanysingleprivatestakeholderorresearchorganizationandrequirescoordinationacrossteamsand disciplines beyond the scope of typical university-led research efforts. It calls forcollaboration by several national laboratories that jointly have the required capabilities andindividualcredibilityintheirareasofexpertiseincooperationwithseveralkeyindustrypartners.

12[Turitsyn2011]K.Turitsyn,P.Sulc,S.Backhaus,andM.Chertkov.“OptionsforControlofReactivePowerbyDistributedPhotovoltaicGenerators.”ProceedingsoftheIEEE,99(6):1063–1073,June2011.13[Sulc2014]Sulc,P.;Backhaus,S.;Chertkov,M.“OptimalDistributedControlofReactivePowerviatheAlternatingDirectionMethodofMultipliers.”InEnergyConversion,IEEETransactions,vol.29,no.4,pp.968-977,Dec.2014.14[DallAnese2014]DallAnese,E.;Dhople,S.V.;Johnson,B.B.;Giannakis,G.B.“DecentralizedOptimalDispatchofPhotovoltaicInvertersinResidentialDistributionSystems.”InEnergyConversion,IEEETransactions,vol.29,no.4,pp.957-967,Dec.2014.15[Lingwen2015]LingwenGan;NaLi;Topcu,U.;Low,S.H.“ExactConvexRelaxationofOptimalPowerFlowinRadialNetworks.”InAutomaticControl,IEEETransactions,vol.60,no.1,pp.72-87,Jan.2015.16[Wang2012]DanhuaWang;Turitsyn,K.;Chertkov,M.,“DistFlowODE:Modeling,analyzingandcontrol-linglongdistributionfeeder.”InDecisionandControl(CDC),2012IEEE51stAnnualConference,pp.5613-5618,10-13Dec.2012.17[Callaway2009]DuncanS.Callaway.“Tappingtheenergystoragepotentialinelectricloadstodeliverloadfollowingandregulation,withapplicationtowindenergy,”EnergyConversionandManagement,Vol.50,Issue5,May2009.18[Mathieu2013]Mathieu,J.L.;Koch,S.;Callaway,D.S.“StateEstimationandControlofElectricLoadstoManageReal-TimeEnergyImbalance.”InPowerSystems,IEEETransactions,vol.28,no.1,pp.430-440,Feb.2013.19[Kundu2011]S.Kundu,N.Sinitsyn,S.Backhaus,andI.Hiskens.“Modelingandcontrolofthermostati-callycontrolledloads.”InPowerSystemComputationConference(PSCC),2011.Stockholm,Sweden.20[Sinitsyn2013]NikolaiA.Sinitsyn,SoumyaKundu,ScottBackhaus.“Safeprotocolsforgeneratingpowerpulseswithheterogeneouspopulationsofthermostaticallycontrolledloads.”EnergyConversionandManagement,vol.67,March2013,pp.297-308.

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An effortwith this breadth in expertise and supported by theU.S. governmentwill have therequiredcredibilitytoconvinceindividualstakeholderstoengageinitsdeployment.

3.4.6. TechnicalactivitydescriptionsThethreemainareasof technicalactivityare: (1)ArchitectureandMetrics; (2)ModelingandControls;and(3)TestingandValidation.Eachoftheseactivitiesisdescribedindetailnext.Task1:ArchitectureandMetrics. Inthistask,severaloptionswillbeformulatedforadaptiveand agile architectures. These architectureswill focus on structure of the control systems inconjunctionwithotherrelatedgridstructures,namelyelectricinfrastructure,industrystructure(includingmarkets),informationandcommunicationtechnology(ICT)superstructure(includingcommunication networks for sensing and control), and overall coordination framework. Thecontrol architecture options will set the structural bounds within which optimization can beperformed for stability and efficiencies of control operation in an all-hazards environmentspanningT&Dincludingthephysicalpowersystem,communicationsnetworks,dataaggregation,andcontrolsignaldisaggregation.Thearchitecturetaskwilldeliverrelevantemergingtrendsandsystemic issues lists (partofrequirements),referencemodels(problemdomaindepictions)forcontrol, models for market/control interactions in high DER distribution systems, and multi-structure architectures for control, communications, and data management in joint T&Denvironments.A set of metrics for relevant control methodologies (reliability, resiliency, scalability, clean,affordability,security,etc.)willalsobedeveloped.Ascalableapproachwillbepresentedthatisapplicabletosystemintegrityandextensibletothebusinesscasefortheperformancethroughcomparisonwithamodeledphysicalsystem. Insteadof focusingon individualcontributorsofperformance separately, a unifying methodology will consider impact to the resultantenvironmentandtheindividual,controlledfeatureofthisenvironment.Finally,theperformanceofeachdevelopedcontrolmethodologyinTask2willbeassessedusingthedevelopedmetrics. Technicalperformancemetricswillbeevaluatedusing simulation, forexample, using convex relaxation theory to calculate bounds on the control performance.Non-technicalmetricswillbeevaluatedusingmethodsdevelopedinGMLCProject1.1.Task2:ModelingandControls.Inthistask,modelsforarangeofindividualandaggregationsoflargenumbersof continuousanddiscrete-controlDERswill bedeveloped. Aprimary focus isthermostaticallycontrolledloadsandinverter-basedDER(forexample,photovoltaicsystemsandbatterystorage).OtherloadsandDERswillbeconsidered,butthesetwogeneralcategoriesofdevicesdefineawideswathoftheflexibleloadsandDERs.ThemodelsdevelopedwilldependonthearchitecturaloptionsfromTask1.However,twomaindirectionsareexpected—device-level models and ensemble-level models. Devices may be modeled via switched stochasticdifferentialequations,withthestochastictermsrepresentingexogenousfluctuationsaswellasuncertaintyinthetransitionsbetweenthestates. Thismodelwillbeexploredanalyticallyandnumerically.Aggregationsofthesedeviceswillbemodeledusingmethodsfromstatisticalphysicsthatdescribetheprobabilitydistributionsofloadsthatareintheon/offstate.Acomputationalschemewillbedevelopedtodescribethespectralpropertiesoftheaggregatesystemaswellascrossvalidateanalyticalandcomputationalresults.

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ThistaskwillalsoexplorepowerflowrelaxationsandapproximationsthatareinformedbythearchitecturaloptionsinTask1andtheload/DERmodelsdevelopedinTask2.Theexplorationwilltake two complementary paths. The first path will catalog the wide variety of power flowrelaxationsandapproximationsthathavebeendevelopedrecentlywithdifferentlevelsoffidelityandcomputationaltractability.Thecomputationalrequirementsandcontrolarchitecturechoicesmustdriveselectionofappropriatepowerflowmodelsfordifferentapplicationsinthecontroltheory(forexample,real-timeapplicationsmusthaveverytractablemodelsthatmaycomeattheexpenseoffidelity).Tobalancethesetrade-offs,thefirstgoalofthistaskistocatalogexistingpowerflowrelaxationsandapproximations,identifyhowtheirstrengthsandshortcomingsalignwiththeneedsofthecontroltheoryandarchitecturechoices,createaroadmapdescribingaplantomakethempracticallyusefulincontextofothercontroltheoryefforts,andimprovequalityofrelaxationsandapproximations.Thesecondpathwilldevelopnew,non-traditionalmethodsthatutilize concepts of monotonicity applied to radial distribution circuits to compute regions ofvoltagefeasibilityinthespaceofload/DERactuation.ThistaskwillfurtherfocusontopologiesfororganizingcontrolsrelatedtoDERsandalgorithmsfordeployingthecontrols.AdistributedcontroltheoreticframeworkforhandlinglargenumbersofDERswillbedeveloped.Aspartofthis,aggregatemodelsofload/DER(asdevelopedinthetasksdescribedabove)willbeincorporated.ThistaskwillinvestigatehowtofitthedevelopedcontrolsolutionsintoanintegratedelectricitysystemthatincludesintegratedT&Dsystemsandwholesale and retail markets (real-time and day-ahead). The communication requirementsneededtorealizetheintegratedsystemwillalsobeconsidered.AsamoresubstantialintegrationofDERswillalsoaffectwholesalemarkets,thedevelopedapproachwilltakethedynamiccouplingintroducedintoaccount.Furthermore,tosupportemergingDSOmodels,thistaskwillprovideatheoretical control framework for supporting apotential two-market system,whereDERswillhave different value in different markets and on different time-scales. In particular, theframework will target integrating ancillary and real-time energy services to ensure DERs areexploitedtofullpotential.Task 3: Testing and Validation. The initial focus of this task is targeted at specifying anddeveloping a numerical simulation test bed in collaboration with the ADMS Platform,ADMS Testbed technical areas, and GMLC project 1.4.15, Co-Simulation of Transmission,Distribution,andCommunications.Thetestbedwillbedesignedspecificallytoallowtestingofvariouscontrolapproaches.Thecontrolmethodswillthenbetestedonthenumericalsimulationtest bed under a range of load, generation, grid, and communications conditions expected innormal grid operations and during severe operational upsets such as faults, transmissioncontingencies, and other compromises. Further, transition plans for industry demonstrationprojectswillbedevelopedforthemostpromisingcontrolmethods.

3.4.7. MilestonesProjectYears1-3.ThemilestonesforthefirstthreeyearsoftheprojecthavebeendevelopedinclosecollaborationwiththeDOEandGMLC,andarelistedinthetablethatfollows:

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Description EndDate Type

TASK1

Documentarchitecturalreferencemodelsforcontrolthatincludethreekeyscenarios:legacysystems,communications-heavysystems,andcommunications-lightsystems.

10/1/16Y1Q2

Annual

Documentarchitecturalreferencemodelswithextensionstoincludemarket/controlinteractions,multi-structurearchitecturediagrams,anddetaileddata.

4/1/17Y1Q4

Midterm

Completeevaluationofarchitectureforscalabilityandcost,providingguidanceongranularityofdeploymentanddistributionofcontrolsystems.

10/1/17Y2Q2

Annual

Completeevaluationofarchitectureforsecurityandresilience,providingguidanceonkeyarchitecturalnodesthatrequirespecificcyberand/orphysicalprotection.

4/1/18Y2Q4

Midterm

Completeevaluationoftestbeddataforcontrolsystemcomputationalscalabilityandresilience.

4/1/19Y3Q4

End/EndofProject

TASK2

Documentcatalogofrequiredindividualandaggregateload/DERmodelsandroadmapoftheoreticaldevelopmentstepstoachievetractableload/DERmodels.

10/1/16Y1Q2

Annual

Documentcatalogofexistingandalternativepowerflowrelaxationsandapproximationsfordistributionsystemswithdiscussionofapplicabilitytooptimizationandcontrolofdistributionnetworks,anddownselectforfurthernumericaltesting.

10/1/16Y1Q2

Annual

Documentpreliminaryformulationanddevelopmentroadmapforrisk-awarecontrolofmultipledistributioncircuitswith>10,000DERincludingpowerflowphysics,legacyequipment,andnetworkconstraints.

10/1/16Y1Q2

Annual

Documentinitialdesignofcontrolmethodologiesforaggregatedandindividualload/DERmodels.

10/1/16Y1Q2

Annual

Documentanalyticalsolutionsforstatic(unactuated)load/DERmodels.

4/1/17Y1Q4

Midterm

Documentfinalspecificationsforthehierarchicalcontrolframeworkforeachofthearchitecturalreferencemodelsincludingtopologies,communications,dataexchange,andtimescales.

4/1/17Y1Q4

Midterm

35

Documentpreliminaryanalyticalsolutionsfordynamic(actuated)load/DERmodels.

10/1/17Y2Q2

Annual

Completeanddocumentassessmentofexistingpowerflowrelaxationsandapproximationsfordistributionsystemswithcomparisonsoferrorsandcomputationalperformance.

10/1/17Y2Q2

Annual

Risk-awarecontrol/optimizationof~10distributionfeedersincludingstochastic(uncontrolled)DER,legacy/utilityequipment,andpowerflowphysics.

10/1/17Y2Q2

Annual

Documentpreliminarytheoreticalframeworkforwholesaleretailmarketintegration.

10/1/17Y2Q2

Annual

Documentanalyticalsolutionsfordynamic(actuated)load/DERwithnumericalsimulationvalidation.

4/1/18Y2Q4

Midterm

Fullydevelopanddocumentpowerflowrelaxationandapproximationapproachesincludingrecommendationsforintegrationintocontrol/optimizationmethodologies.

4/1/18Y2Q4

Midterm

Documentfinaltheoreticalframeworkforwholesaleretailmarketintegration.

4/1/18Y2Q4

Midterm

Risk-awarecontrol/optimizationof~10distributionfeedersincluding~10,000controlledDER,legacy/utilityequipment,andpowerflowphysics.

10/1/18Y2Q2

EndofProject

Additionofco-optimizationofenergyandancillaryservicestorisk-awarecontrol/optimizationof~10distributionfeedersincluding~10,000controlledDER,legacy/utilityequipment,andpowerflowphysics.

4/1/19Y3Q4

End/EndofProject

Documentfinaltheoreticalframeworkforco-optimizationofenergyandancillaryservices.

4/1/19Y3Q4

End/EndofProject

TASK3

Documentnumericalsimulationtestbedrequirementsanddownselect(adaptexistingversusdevelopnew).

10/1/17Y2Q2

Annual

Documentcontrolsystemtestingplanandimplementationplanfortestbeddevelopment/adaptation.

4/1/18Y2Q4

Midterm

Testbedimplementationcomplete,andtestinginitiated. 10/1/18Y3/Q2

EndofProject

Numericalsimulationtestingofcontrolsystemcomputationalscalabilityandresiliencecomplete.

4/1/19Y3Q4

End/EndofProject

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ProjectYears4-5—Theseprojectyearshavenotbeenpartof thedetailedplanning;however,additionalworkbeyondtheendofprojectyear3willenableachievingseveraladditionalgoalsandrelatedmilestones.Thesehigh-levelmilestonesinclude:

• Y4/Q2:Moredetailedintegrationofthecontrolsdevelopedinprojectyears1-3withbulkelectric system markets to include the effects of stochasticity and control responseuncertaintyonthecommitmentanddispatchoflargecentralgeneration.

• Y4/Q4: Deeper integration of the controls developed in project years 1-3 with

distribution-level markets accounting for the quality of control service achieved bydifferentDERswithinthenetwork.

• Y5/Q4: Development of control system algorithms for distributed, peer-to-peer

communications systems that manage DER and utility control equipment with muchreducedburdenonutilitysupervisorycontrolsystemsandcommunicationsnetworks.

3.5. ADMSIntegrationwithEMSandBEMSThissectiondescribestheactivityof integratingADMSintothenextgenerationgridoperatingsystemunderanewsystemcontrolparadigm.Withinthisactivity,RD&Dwillbeperformedtodevelopanddemonstrateadvancedgridcontrol technologies thatwill allow thesystemtobeoperatedwithlessreservemargin,dramaticallyenhancingtheefficiencyoftheoverallsystem.Toenablethisvision,itwillbenecessarytoachievegreaterend-to-endintegrationacrossalllevelsofthesupplyanddeliveryinfrastructure.ThisactivitywillaccomplishgreaterintegrationbetweenADMS,grid-levelEMS,andBMS. Linking thesesystemswillenableseamlessoptimizationandcontrolacrosstheentirepowersystem.Openstandardsandmiddlewaresoftwareapproacheswillbecriticaltothesuccessofthisobjective.

3.5.1. TechnicalgoalandobjectivesThecurrentapproachtoelectricpowersystemoperationsandcontrolswasdevelopedoverthelastthreetofourdecadesusingapiecemealapproach,withinnarrowfunctionalsilosforDMSandEMSsoftwaretools thatofferminimalability tocoordinateor interact. Additionally,end-usetoolssuchasBMS,DERMS,andMCaretotallyindependentfromgridcontrolsoftwaresystems.Therapidgrowthofrenewablepowergeneration,theincreaseduseofelectricvehicles,andtheincreased need to integrate customers with the power system are rendering the currentgenerationofgridoperatingsystemsobsolete.Thegoalofthisactivityistocreateanintegratedgridmanagementframeworkthatwillbeakinto having an autopilot system for the grid’s interconnected components—from central anddistributed energy resources at bulk power systems anddistribution systems, to local controlsystems for energy networks, including BMS. By the end of five years, this activitywillsuccessfully:

• Develop anopen framework to coordinate EMS,DMS, andBMSoperations, and thendemonstratethenewframeworkonausecaseatGMLCnationallabfacilities.Thetest

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system will have more than 15,000 transmission substations and will involve highpenetrationofDERsormicrogrids(morethan50percent).

• Deploy and demonstrate new operations applications—probabilistic risk-based

operations,forecastingdataintegrationanddecisionsupport,andheterogeneoussensordataintegration—thattransformorextendexistingEMSandDMSapplications.

• Demonstrate the value of integrating ADMS into this new generation grid operating

system.

3.5.2. TechnicalchallengesCurrent grid operations and control systems have relied on decoupled approaches totransmission, distribution, and local energy networks. This approach is not capable of fullyaddressingtheholisticapproachofferedbycomplexsmartgridsystems,wheremonitoringeffectsof renewablesandelectricenergystorage in thedistributionnetworkand forendusershasasignificantimpactthroughouttheentirenetwork.Thefuturegridoperationsandcontrolsystemsmust be able to monitor, protect, and automatically optimize the operation of the grid’sinterconnectedelements–fromthecentralanddistributedgenerator throughthehigh-voltagenetwork anddistribution system, tobuilding automation systems, to controllers forDERs andmicrogrids,andtoend-useconsumers,includingtheirthermostats,electricvehicles,appliances,andotherhouseholddevices.Additionally,futureelectricalgridswillbecharacterizedbyhigherlevelsofstochasticgeneration,by larger load fluctuations that impact systemcontrols, andbyoperating closer togrid limits.Continueduseofdeterministicmethodsandalgorithmsthatdonotaccountforthestochasticnature of renewable and/or distributed generation, or load fluctuations, will effectively blindelectric system operators to the increased risk caused by those fluctuations. As a result,transmission and generation limits will be exceeded, and voltage and transient stabilityboundarieswillbeviolated.Grid operations presently handle large amounts of multi-source and multi-scale data fromdisparate sources, suchas theSCADA system,assetmanagement system,OMS,weatherdatasystem,andGISsystem.Nationalgridmodernizationeffortstofacilitateadoptionofsmartgridpracticesinrecentyearshaveincreasedadoptionofsmartgridtechnologies,suchasPMUs,AMI,andsmartmetering.Thesedeploymentsareexpectedtogenerateatsunamiofadditionaldata.This large influx of data offers multiple advantages; however, without proper structure andanalysis,itcanoverwhelmthesystems,causingchaosindataacquisition,datastorage,anddataanalysis.

3.5.3. TechnicalscopeThisactivitywilldevelopanopenframeworktoexchangeinformationbetweenADMS,electricgrid’sEMS,andBMScontrolsystems.Tocreatethisframework,interfacestoADMS,EMS,andBMSthataresuitedforthehierarchicalstructureoforganizations’utilities,asshowninFigure7,willbebuilt.TheinterfaceswillalignwithGMLC1.2.1(gridarchitecturedevelopment)andfollowtheguidelinesdevelopedinthatproject.Thediverseandlargenumberofbuildingsconnectedtotheadvanceddistributionsystemrequiresaframeworkthatenablesscalablemessaging,whichwillbeinstalledatseverallocationsandregisteredwitheachlocation.Inaddition,theintegration

38

requiresbuildingprofiles for the informationmodelsanddataexchange. TheactivitywilluseinteroperabilitystandardsthataredevelopedfromGMLC1.2.2 (interoperability)andcommoninformationmodels(whereavailable)toensureacommoninterpretationofthedataexchangedacrosstheinterface.

Figure7.CollaborativeefforttodevelopaMulti-ScaleIntegrationofControlSystem

Toachievetheend-to-endcontrolsystemcoordination,thisactivitywillalsodevelopapplicationsthatquantifyandincorporateuncertaintyandalsointegratewithsensordatatoprovidedecisionsupportforcontrolroomapplications.Thisactivitywillinvestigatemethodsanddevelopsoftwaretoolsthatutilizedatafromsensornetworksatdifferentspatialandtemporalscales,forecastingapplications formonitoringgridhealth, detecting incipient failuresor faults and locating theirsource,managingtheresponsetoagridevent,anddevelopingstrategiesformitigatingfutureevents. Somemachine learningtoolsdevelopedthroughtheconceptpaper“MultiScaleDataAnalytics”willbe leveragedon thisactivity. Probabilisticmethodsandalgorithmswillalsobedeveloped to incorporate the effects of stochastic fluctuations. With amove to probabilisticmodels, decisions regarding risk levels become an explicit decision rather than an implicitassumption. Riskmetricswill also bedeveloped to estimate theprobabilities of violations ofsystem security; compute commitment, dispatch, and control actions to constrain theseprobabilities within acceptable limits; and yield defendable prices for these actions and forinjectionsthatincreasesystemsrisks.

3.5.4. StatusofcurrentdevelopmentThissectionidentifiesthecurrentdevelopmentandcapabilitiesofthenationallabcomplexthatarerelevanttotheintegrationofADMS,EMS,andBMS.

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• EIOCatPNNL:TheEIOCisusedbyresearcherstoexplorehowchangesinthewaythenation's electrical grid is operated can improve the grid’s reliability, lower costs, andlessen environmental impacts. The EIOC’s focus is on developing real-time tools andsupportingtheirintegrationintooperatingsystems.Thus,theEIOCsupportsthenation’sneedtodevelopnewtoolsthatprovidenotonlyabetterviewofthecurrentpowergrid,butalsofasterandmoreaccuratepredictionsofwhatmightbehappeningsooperatorscan quickly respond. The EIOC allows researchers to work with real data—runningscenarios to determine how to increase capacity and improve reliability models, andtestingnewtechnologywithoutthecostandriskofdisruptingthesystem.BecausetheEIOCisasafesetting,researcherscanworkthroughtheiterativeprocessofdevelopingandrefiningtechnologymorequickly.TheEIOChasbeenusedforseveraluserstudiesevaluating next generation operation tools, including a ramping uncertainty tool, animprovedgraphicalpresentationofcontingencyanalysisdata,andasharedperspectivetooltoenhancesharedunderstandingbetweencontrolcenters.

• VOLTTRON at PNNL: VOLTTRON is an open-source platform for secure distributed

sensing and control integrating deviceswith applications for controlling those devicesthat were developed by PNNL. VOLTTRON can be used as the basis for a BMS bydevelopingasetofservicestoprovidethisfunctionality.Forexample,VirginiaTechhasbuiltBeMOSS(http://www.bemoss.org/),anapplicationontopofVOLTTRON,whichactsas a BMS for small residential buildings. Other examples of services implemented toachievethisfunctionalityincludeautomateddiscoveryofcertaindevicesonanetwork,acommondataformatforalldevicesandagents,andauserinterfaceforcontrollingthedeployment.

• IntegratedDistributionManagementSystem(IDMS)atNREL: NRELcollaboratedwith

Duke Energy and Alstom Grid to implement comprehensive modeling, analysis,visualization,andhardwarethatisrepresentativeofDukeEnergy’sutilityfeederattheNRELESIF.ThisprojecthelpedDukeEnergyunderstandtheimpactofsmartinvertersbyimplementingtheminGIS,DMS,andSCADA.TheAlstomGridIDMSwasimplementedinESIFsystems.

• PHILPlatformwithremotedistributioncircuitco-simulationbetweenPNNLandNREL:

NREL built the capability of integrating PHIL with a large distribution simulation andsuccessfullydemonstratedremoteco-simulationwithPNNL.Inthedemonstration,theadvancedPVinvertersaretestedatpowerusingNREL’sPVandgridsimulatorsinteractingwithGridLAB-DdistributionfeedersoftwaremodelsatPNNL.Thisgeographicalflexibilityallows researchers to interconnect hardware and/or software acrossmultiple sites toleverageuniqueequipment,devices,measurements,orothercapabilities.

• IntegratedTransmission,Distribution,andCommunicationModelingandSimulationat

Lawrence Livermore National Laboratory (LLNL): LLNL has developed the ParGridsimulator forcoupled transmission,distribution,andcommunicationanalysis. ParGridintegrates LLNL’s GridDyn transmission simulator; PNNL’s GridLAB-D distributionsimulator; andNS-3, an open-source communication network simulator. ParGridwasdesigned to federate multiple parallel simulators running across high performancecomputing nodes for large-scale power grid analysis. These distributed simulatorscommunicatethroughasynchronousmessageswhilemaintainingaglobaltimeacrossallsimulatorcomponents.Thisapproachensuresefficientandcorrectexecution.ParGrid

40

hasthepotentialtoconductcross-domainanalysisfora large-scalegrid likeCalifornia,which includes 6,000 transmission substations connecting about 10,000 distributionfeederstoserve15millioncustomers.

• StochasticoptimizationresearchatSandiaNationalLaboratories(SNL)andLosAlamos

NationalLaboratory(LANL):SNLdevelopedsolversforstochasticunitcommitment(UC)to achieve tractable solution times when solving realistic ISO-scale problems. Thesolutionmethodusesadecompositiontechniqueknownas“progressivehedging”andwasdemonstratedusingaproblemsimilartothesizeofISONewEngland(forexample,approximately350generators)withhundredsof scenarios representing thestochasticnatureof loadandwindatveryhighpenetration levels. Solutiontimeswerereducedfromintractabletotensofminutesusingcommodityclusters.ThisworkwasfundedaspartofARPA-E’sGreenElectricityNetworkIntegration(GENI)portfolioofprojects.ThedemonstratedcostsavingsassociatedwithdeploymentofstochasticUCinsystemswithhighpenetrationofrenewableswere3-4%ofthetotalproductioncosts,whileincreasingthe reliability of the system. SNL also designed stochastic process models that usehistorical load, wind, and solar forecast power generation data with correspondingactuals to produce well calibrated probabilistic forecasts in the form of probabilityweightedscenariosforthosethreesourcesofuncertainty.UndertheDOE/OEAdvancedGridModeling(AGM)Program,LANLdevelopedprobabilisticrisksawareED,calledthe“Chance-constrainedOptimalPowerFlow,”thatallowsforcontrollingtheprobabilityofviolationofsafetylimitswithinanoptimizationproblem.Ascalablealgorithmforsolvingthe problem was developed exploiting methods from convex optimization, and theefficiency/performance of the algorithm was demonstrated on the Bonneville PowerAdministration(BPA)testcaseusingverylimitedcomputationalresources.

• DynamicLineRating(DLR)atANL:ANLhasinvestigatedriskassessmentandcontrolof

congestionmanagementusingreal-timeDLRs.Insteadofexaminingtheoverloadingriskcausedbywrongforecastingonasingleline,asdonetypically,ANLexpandedthestudyto evaluate the probability of overloading onmultiple lines to address a system-widecongestion problem. To this end, ANL developed a new distributionally robustoptimization-basedcongestionmanagementmodelthatselectivelyusesDLRtoalleviatesystemcongestionwhilekeepingtheoverloadriskatthesystemlevelwithinasaferange.As the formulated model is computationally challenging to solve, ANL exploited thestructureoftheproblemandutilizedthelatestdevelopmentinstochasticprogrammingto convert the original nonlinear problem to a readily solvable mixed-integer linearprogramming (MILP) problem. The computational results show that ANL’s proposedmethod can effectively utilize the value of DLR while keeping the simultaneousoverloadingriskundercontrol.

3.5.5. FederalroleThe technical riskofconductingmulti-scalecontrol system integration ishighsince it requirescomprehensivetechnicalexpertisefromtransmissionoperations,todistributionoperations,tovariouslocalenergynetworkoperatingsystems.Thus,theinvestmentbytheDOEisnecessary,asthisactivityleveragesthebroadcapabilityofexperiencedDOEandnationallabpersonnelwhocan execute this work without further training. The budget required to performmulti-scalecontrolsystemintegrationislarge,andwithoutDOEfunding,thelevelofworktobecompletedistooextensivefortheprivatesectortocover.TheDOEcansupporttheU.S.powerindustrywith

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thisdevelopment,whichusestheexistinginvestmentinnationallabresources,builtovertime.UtilizingexistingDOEfacilitiesdeployedatnationallabs(suchasEIOCatPNNLandIDMSatNREL)willbenefittheU.S.powerindustryasitcontinuestointegrateadvancedtechnology.

3.5.6. TechnicalactivitydescriptionsTask 1: Use case development. Develop a use case onwhich EMS, DMS, and BMS operateinteractivelyforatestsystemthathasmorethan15,000transmissionsubstationsandinvolveshighpenetrationofDERsormicrogrids(morethan50percent);identifyandselecttheprototypeEMS,DMS,andBMSsystemsavailableatnationallabsorcollaborativeentitiesthatwillbeusedforTask3.TimeFrame:1-2Years

Task 2: Open framework development for EMS/DMS/BMS integration. Develop openframeworkapproaches for thisusecasetocoordinateEMS,DMS,andBMSoperationsamongthemselvesandwithotherlocalenergynetworkcontrollers,suchasmicrogridcontrollers.TheintegratedplatformwillbedemonstratedontheusecasedevelopedinTask1.ThiswillrequireextensiveexternalstakeholderengagementandcoordinationwithGMLC1.2.1(gridarchitecture)and1.2.2(interoperability).TimeFrame:3-5YearsTask 3: New application development on probabilistic risk-based operations for EMS.Investigate and incorporate probabilistic risk-based approaches into next generationEMS/DMS/BMS,shiftingfromatraditionalcontingencyanalysismodeltoastochasticmodel.Itisanticipatedtherisk-baseddistributedcontrolsystemwillreducemitigationcostsandminimizesecurityviolationriskstypicallyfoundindeterministicapproachesinlarge-scaleusecases.TimeFrame:3-5YearsTask 4: New application development on uncertainty modeling and forecasting methodfor EMS. Develop applications that quantify and incorporate uncertainty and integrate withsensorandforecastingdatatoprovidedecisionsupportforadvancedcontrolroomapplications.Examplesincludemakingbetteroperationaldecisionswithreal-timeinformation(suchasDLR,dynamic reserve allocation, or real-time assessment of stability limits) and more preciselymanagingriskassociatedwithpotentialfutureconditions(suchasprobabilisticforecastingandfaultsonsystemsthatprovideconfidenceinformation).TimeFrame:3-5YearsTask5: Newapplicationdevelopmentonheterogeneoussensordata integration forADMS.Develop and demonstrate a tool for acquiring heterogeneous sensor data and populating anextendedgridstatethatisinformedbycontinualrediscoveryofgridtopology.TimeFrame:3-5YearsTask 6: Cost/benefit analysis of integrating ADMS into this new generation grid operatingsystem. Define key assumptions for the ADMS—system/market conditions, DER adoptionscenarios,andnewADMSapplications(notusedintheexistingDMS)—andthenconductthecost-benefit analysis tomonetize the impacts and add them to the accumulated costs to producesummarycost-benefitmetrics. Inaddition toconsolidating results, theeconomicanalysiswilltracehowcostsandbenefitsariseamongvariousentities,includingcustomersandsociety.

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TimeFrame:2-3Years

3.5.7. Milestones

MilestoneName/Description EndDate Type

• Completereportdocumentingusecaseanditsdataset.• Completereportdocumentingdataexchangerequirements

andprotocols.• CompleteintegrationofLANLEDwithSNLUCengine.• CompletereportdocumentinginvestigationofDLRuse.

09/30/2016 AnnualMilestone

• DemonstrateintegrationofEMSandDMSinformationinusecase.

• CompleteinitialdataintegrationplatformatNREL’sESIFwithfeederdata.


• SuccessfullydemonstrateintegratedEMS/DMS/BMSplatformatalaboratoryscalesystem.


• SuccessfullydemonstrateintegratedEMS/DMS/BMSplatformatindustryscalesystem.


• SuccessfullydemonstratethevalueofintegratingADMSintoEMS/DMS/BMSplatform.


4.ProgramManagementandStakeholderEngagement

4.1 ProgramPortfolioManagementApproachTheADMSProgramfollowsamulti-stepplanningandmanagementprocessdesignedtoensurethat all funded technical R&D projects are chosen based on qualifications in meeting clearlydefinedcriteria.Thisprocessentailsthefollowing:

• Competitive solicitations for financial assistance awards to industry, universities, andnationallabsonRD&Dactivities.

• Peerreviewsofproposalsinmeetingthesolicitationgoals,objectives,andperformancerequirements.

• Peerreviewsofin-progressprojectsonthescientificmerit,thelikelihoodoftechnicalandmarketsuccess,theactualoranticipatedresults,andthecosteffectivenessofresearchmanagement. The ADMS Program and its in-progress R&D projects will be reviewedthrough this external review process once every two years, with evaluation resultsfeedingback toprogramplanningandportfoliomanagement. The inauguralprogrampeerreviewmeetingisplannedfor2017.

• Stagegatereviewstodeterminereadinessofatechnologyoractivitytoadvancetoitsnextphaseofdevelopment,pursuealternativepaths,orbeterminated.Thesereadinessreviewswill be conducted on an as-needed schedule based on project progression inmeetingtheestablishedstagegatecriteria.

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• OEinternalreviewoftheADMSProgramannuallytoensurecontinuousimprovementsandproperalignmentwithR&Dprioritiesandindustryneeds.

Themerit of R&D projects, individually and collectively, in achieving the ADMS Program goaltargetswillbemadetransparentbyapplyingthismanagementprocessconsistentlythroughouttheProgram.Moreover,thismerit,tobesupportedbyrigorousanalysisandevaluation,willbetransparentinProgramcommunicationstotheindustry,thepublic,andotherADMSstakeholderorganizations.This MYPP will be used to guide ongoing projects and development of the ADMS Programportfolio of projects through2020. It is anticipated that annual updates of theMYPPwill benecessary to reflect current state of advances, priority needs, and resources availability.Implementation schedules for the activities and tasks described in theMYPP will depend onannualappropriationsoftheProgrambudget.

4.2 StakeholderEngagementKey industry stakeholder groups for the ADMS Program include electric utilities that employADMSandvendorsthatofferADMSproductstoutilitycustomers.TheProgramwillengagethesekeystakeholdergroups,andtheirassociatedorganizationssuchastheAmericanPublicPowerAssociation(APPA),EdisonElectricInstitute(EEI),NationalRuralElectricCooperativeAssociation(NRECA),andEPRI,tojointlyplanandimplementitsresearch,development,anddemonstrationefforts.ThisengagementwillbethroughtheIABsetupforeachoftheProgram’stechnicalareasand through meetings/workshops with expanded participation of stakeholder organizations.Each IABwillbe representedbyexpertsand thought leaders fromutilitiesandvendors in therespectiveprogramtechnicalareas.IABactivitiesincludeannualmeetingswheretheIABreviewsandevaluatestheprogressofprojectsinatechnicalarea,alongwithquarterlywebmeetingstoprovidetimelyinputtotheprojects.AttheProgramlevel,theADMSSteeringCommittee(seeSection1.5)will guide theoverallProgramdirections,portfoliodevelopment,andstakeholderengagementstrategies.TheADMSProgramwill alsoplan toholdmeetingsand/orworkshops to seek input fromandcommunicateresultstomuchbroaderrepresentationsofstakeholdergrouporganizationswhenwarranted.Examplesofsuchmeetingsandworkshopsincludethebiennialprogrampeerreviewmeeting,theindustrystakeholderworkshopforinputtofinalizethisMYPP,andotherstodeep-diveonspecifictopicsofgreatimpact.Having an effective stakeholder engagement strategy and practice is critical to reaching theProgramtargetsof20%,50%,and100%ofvendorshavingADMScapabilitiesin2020,2025,and2030,respectively.TheProgramwillcontinuetorefinethestrategytobroadenengagementofstakeholdergrouporganizationsnecessaryforthesuccessoftheADMPProgram.

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