towards autonomous power management: extending the … · 2017. 10. 16. · • a. trias, j. l....

Gridquant Technologies LLC & Elequant, Inc. 1

TowardsAutonomousPowerManagement:ExtendingtheHolomorphic

EmbeddingLoadflowMethodforNASADCsystems

EnergyTechConferenceNovember28,2016

BobStuart,PIGridquantTechnologiesLLC

AntonioTrias,Ph.D.JoseLuisMarin,Ph.D.

ElequantInc.


Outline

•  TheneedforautonomouscontrolofspacecraPpower

•  TheroleandrelevanceofpowerflowinNASA’sIntelligentAutonomousControlArchitecture

•  HELMTM:quickoverview

•  SBIRresults:–  Devicemodeling(ISSsinglechannel)

–  LatestbreakthroughsinHELMtheory

•  VisionforfutureapplicaXons


TheneedforAutonomousControlofSpacecra7PowerSystems

Ø SpacecraPPowerSystemsareDCmicrogridsthatmustbeextremelyrobustØ InternaXonalSpaceStaXon(ISS)andmannedspacemissionsinnearearthorbithaveconstantgroundsupportfromHoustonMissionControlCenterØ DeepSpaceTraveltoMARSwillrequireautonomouscontrolduetocommunicaXonlatencyØ CommunicaXonlatencyforMARSmissionwouldbeanywherefrom15to25minutesdependingonofproximityofMARSorbitinrelaXonshiptoEarth


TheroleofpowerflowinDC&ACpowersystems

•  LargeTerrestrialACNetworksrepresentadynamicsystemthatconstantlychanges24x7-ThousandsofcontrolacXonstakendailytocontrolvoltageandfrequency-GeneratorexcitaXoncontrol,loadtapchangers,phaseshiPers,reacXvedevices-AGCatamoremacrolevel,andoperatoracXonintermsofschedulechanges

•  DCMicrogridsonboardspacecraPandontheISSalsorepresentverydynamicsystems

-SSUinPVarraypanelsandvoltagecontrolbytheDDCU-DCcomponentssuchasPVarraysandba`eriesareinherentlynon-linear

•  TerrestrialACnetworksandDCmicrogridshavetheluxuryofoperatorintervenXon

•  PowerflowsinterrestrialACnetworksplayasignificantroleindeterminingphysicalstate:-ACpowerflowscriXcaltodeterminingwhetherinastablestateornearcollapse-DuringXmesofextremestressorlargestepchanges,iteraXvemethodshavefailed

•  AutonomouscontrolofspacecraPrequiresrobustpowerflowsthatcansolveatthelimit


TheroleandrelevanceofpowerflowinNASA’sIntelligentAutonomousControlArchitecture

FromJ.Soederetal.,”OverviewofIntelligentPowerControllerDevelopmentforHumanDeepSpaceExplora;on”,IECEC2014

•  InuXliXes,powerflowisnormallyassociatedtoanalysisandplanningtasks

•  HerewehaveaverydifferenttypeofapplicaXoninmind:analy<caltoolsfordecision-supportinnetworkoperaXons;andulXmately,forcompleteautonomousmanagement.

PowerSystemModelfuncXons:1.  ModelofpowergeneraXon2.  Modelenergystorage3.  Modelpowernetwork(powerflowmodelof

thedistribuXonsystem)4.  PowerSystemStateEsXmator

Thismoduleprovideshighfidelitymodels&simula<onofthepowersystem,whichothercontrolblocksneedtomakedecisionsregardingcontrolac<ons.


HELM™Overview

Currentmethods:lackofconvergence;neediniBalseedsoluBon

•  HolomorphicEmbeddingLoadFlowMethod

–  Direct,construcXvesoluXontopowerflowequaXons

–  Non-iteraXveanddeterminisXc,unliketradiXonalmethods

–  UsesafundamentallynewmathemaXcalapproach

•  BasedonComplexAnalysis:analyXcconXnuaXon,notnumericalconXnuaXonorhomotopy

•  Newmeasuresofdistancetocollapse(Sigmaindicators)

•  ThisnewPFengineisthekeyenablerofanewclassofsoPwareapplicaXonsfordecisionsupportingridoperaXons

–  ApplicaXonscannowreliablyperformmassivesearchonthestate-spaceofthesystem.AnalogoustoGPSsat-nav.

–  TheyruninparalleltoexisXngtools–actasexpertoperatorsupportinonlinemode

–  TheyworkintermsoftheactualSCADAacXons,notidealizedorsimplifiedmodels


HELM™OverviewIfpocketcalculatorsbehavedlike(iteraXve)powerflow…


SBIRresults:extensiontoNASADCsystems

•  DemonstratedthefeasibilityofadapXngHELMtofullyDCnetworks.

•  A.Trias,J.L.Marin,“TheHolomorphicEmbeddingLoadflowMethodforDCPowerSystemsandNonlinearDCCircuits”,IEEETransacXonsonCircuitsandSystemsI:RegularPapers,63(2),pp.322—333,2016.

CPL

R12

1 2

•  DevelopedHELMABAC,aMATLAB-basedimplementaXon.Openandauditablecode.

•  NowdevelopingHELMLABDC,forfullyDCsystems.TargetmodelsaretheISSPMAD.


SBIRresults:devicemodeling

•  ISSmodelsfromPCKrause(Simulink,averaged)•  Derivedtheircorrespondingsteady-statemodelsforpowerflow

V (volts)

I (am

ps)

50

60

Vengage105 V

Von Vmin

Voff

Op. point

80 100 160

155 160 165

-40

-30

-20

-10

0

10

20

30

40

50

60

Input Voltage (V)

Inpu

t Cur

rent

(A)

Voltage Command

deadbanddischargeSlope

chargeSlope

imin

imax

Primary side(input)

Secondary side(output)

1 : M(D) vp vs rs

ip is

Control input D Power conversion efficiency η

v's

PVsolarPanels(+SSU)

Ba`eries(BCDU) DC-DCconverters

(DDCU)

ConstantPowerLoads(+converter)


Fullsystemexample:ISS,1-channel

CPL

PV panel

Constant Power Load

Rline

Rin

Cin

Vload

Vtr_bus

τ=RC

Vd_busSSU

R

LC

Vbatt

Rbatt

BCDU




SteadyStatevs.TimeDomain•  Steady-statemodelingeasiertofittoreality;fewerparameters•  Inreal-Xmescenarios,(StaXc)StateEsXmaXonneededtogetthose

parameters

•  Atdesign-Xmeandanalysisscenarios:HELMcomplementsXme-domainsimulaXon,byprovidingthe(stable)DCoperaXngpoints.

•  MinorissueswhentryingtomatchSimulink:•  Fimngthesteady-stateerrorfromfeedbackcontrolloops.•  Modeldynamiceffects(e.g.effecXveresistancesinaDC-DCconverter)àinreal-

Xme,be`ertojustesXmatethem

•  Butremember:therealaimisnotmatchingdetailedXme-domainmodels,butobtaininggoodsteady-statemodelsfortheiruseinreal-<me,powerflow-basedalgorithms.


Verifyingthestabilityofsolu<onswithdynamicsimula<ons

SBIRresults:HELMLABDC

Lowload,onlyonesoluXon•  Itisstable(a`ractor)


Highload,threesoluXons:•  Twoarestable(a`ractors)•  Oneisunstable(repeller)

SBIRresults:HELMLABDC

Verifyingthestabilityofsolu<onswithdynamicsimula<ons


NewmethodtofindallsoluBonsinachaintopology(reparameterizaXontechnique)

All16solu;onbranchesofa5-busDCradialchainnetwork

whitebranch

non-whitebranches(physical)

non-whitebranches(non-physical)

SignificantadvancesinHELMtheory


VisionforfutureapplicaXons

•  ThisSBIRisprototypingapowerflowengineusingtheISSmicrogridasamodel;thiswillbeextendedtootherfuturespacecraP(Orionsystems)andfuturemicro-griddesigns(e.g.otherdeepspacevehicles,orlunarbases).

•  AutonomousgridoperaXonisachievableviamodel-basedalgorithmsbasedon(intelligent)exploraXonofthesteadystatesofthenetwork.Closelyrelatedtotheparadigmofmodel-predicXve-control;onlythisisattheXmescalesofhumanoperators.


VisionforfutureapplicaXonstoNASAandterrestrialsystems

•  OurPhaseIISBIRdemonstratedthatHELMsolvesreliablythepowerflowinDCmicrogrids.ItprovidesthestableoperaXngpoints,incaseseveralarepossible.

•  HELM,alongwithadvancedintelligentapplicaXons(inthespiritofthoseusedinAGORAforterrestrialgrids)canbeintegratedinthefutureforautonomouscontrolofspacecraP

•  HELMcanbekeyforthemanagementoffuturetechnologiessuchasSolarElectricPropulsion(SEP),wherethesystemundergoesabruptstatechanges.

•  EmergingterrestrialmicrogridswillneedHELMtechnologyinthefuture

Figure1.Schema<cofmicrogrid SourceofPicture:J.Soederetal.,“ApplicaBonofAutonomousSpacecraPPowerControlTechnologytoTerrestrialMicrogrids”,IECEC2014


Elequant Inc. (Grupo AIA)48 Terra Vista Ave. #DSan Francisco, CA 94115Tel +1 415 978-9800

HQ Barcelona:Av. de la Torre Blanca, 5708172 Sant Cugat del VallèsBarcelonaTel. +34 93 504 49 00

ThankyouGridquant Technologies LLC2750 Peachtree Industrial Blvd, Suite EDuluth, GA 30097Tel +1 404 386-3120


BACK-UPSLIDES


TheroleandrelevanceofpowerflowinNASA’sIntelligentAutonomousControlArchitecture


HELM:quickoverview• A new method for solving the powerflow equations.

• Based on Complex Analysis (holomorphicity, algebraic curves, power series, analytic continuation)

• Constructive, direct, non-equivocal, and complete:

•  Provides the operational solution when it exists

•  Correctly detects powerflow infeasibility when no solution exists

Yi(sh)

Si

Bus i

Bus j


RelevancetoNASA

Iterative methods: •  dependent on the choice of a “suitable” initial

seed •  intrinsic fractality makes their results erratic. •  need human supervision •  cannot be fully trusted for automation purposes.

FractalbasinsofaQracBoninIEEE-300

By contrast, HELM: •  always provides correct results, completely

unattended •  enables new applications for the autonomous

operation of power systems. •  already proven in AC transmission grids; used

for real time decision-support for operators. Our approach to autonomous operation is algorithmic and model-based. Think “GPS satnav for network operators”.


HELM™powerflowfornetworkapplica<onsinu<li<es

AIALoadFlow

ConXngencyAnalysis

StateEsXmator

RTSimulator

PV/QVCurves

OPF

RestoraXonSolver

Lim.Viol.Solver

SCADA


SpacecraPPowerSystemArchitecture

Source:J.Soederetal."OverviewofIntelligentPowerControllerDevelopmentforHumanDeepSpaceExploraBon”,2014.


G(tr)=a / rs

Gp(sh) = a(a-1)/rs Gs

(sh) = (1-a)/rs

p s


Example:DC-DCconverters•  Reducedtojustthreeparameters:rs, a,η.•  CanbemadetofullyresembletheirAC

counterparts


BasicDCpowersystem


NonlinearBehaviorofComponents

•  ThenonlinearbehaviorofcomponentsresultsinmulXplesoluXonpoints.TheactualoperaXngstateisdeterminedbythestabilityofthesoluXonpoints.


•  RealisXcmodelofcomplexACpowergrid

•  Severalnodesnearvoltagecollapse

•  HELMTMtechnologycanaccuratelydeterminedistancetocollapse

•  Aroadmapcanbeprovidedbacktostablesystemevenifsystemhascollapsed

HELM-basedtools:HELM-FlowIEEE300-busmodel

HELMTM–FlowSigmaCurve

towards autonomous power management: extending the … · 2017. 10. 16. · • a. trias, j. l....

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