AutonomicMicrogrids?

ProfessorPhilTaylorSiemensProfessorofEnergySystems

DirectoroftheEPSRCNationalCentreforEnergySystemsIntegration

Niagara2016SymposiumonMicrogrids20– 21st October2016

NiagaraontheLake,Ontario

Overview• AutonomicMicrogridsandSelf*Operation• TechnicalZoning• AlgorithmSelection• MarketZoning• SelfOrganisingArchitecturesandCyberSecurity

• NationalCentreforEnergySystemsIntegration

Drivers

OverarchingResearchQuestion

Can a fully distributed intelligence and controlphilosophy deliver the future flexible gridsrequired to facilitate the low carbon transition,allow for the adoption of emerging game-changing network technologies and cope withthe accompanying increase in uncertainty andcomplexity?

Self*NetworkOperationandControlSchematic

II.Howtozone?–(a)

§ Proximitymetric:definitionofdistanceamongbuses

§ Definitionof mergingcriteria

§ Clusteringvalidationcriteria

§ Zonalcentroididentification(pilotnode)

Zoningmethodology

Zoningmethodologies(examplesofexistingandownones):

§ Hierarchicalclustering–singledistance(HCSD)§ Hierarchicalclustering–MVArcontrolspace(HCVS)

§ SpectralClustering(SKC)§ FuzzyClustering(FCM)

II.Howtozone?–(b)

Barbara AlimisisSeptember 2014

accurate data error 2% error 4% error 6% error 8% error 10%

82

84

86

88

90

92O

PI(%

)

HCVSSKCFCMHCSDRobustnessofazoningdecision

-testingtheeffectofuncertaintyonthemeasurements(e.g.imperfectprediction,noisyorcorrupteddata)

Performanceofazoningdecision

-agreaterperformancesignifiesreducedlosses&enhancedsecurity

HCSD HCVS SKC FCM80

85

90

95

100

OPI

(%)

III.Staticvs.adaptivezoning..

Barbara AlimisisSeptember 2014

Questioningthefeasibility.

§ Fastenough(<1min)forlargescalenetwork(e.g.2383busestestnetwork)?

§ Availabilityofmeasurementandtelecommunicationinfrastructure?

Questioningthevalue.

§ Performanceenhancementvs.reconfigurationthreshold

100 101 102 103 104

FCM

SKC

HCVS

HCSD

Execution time (sec)

Zo

nin

g m

eth

od

olo

gie

s

HCSD HCVS SKC FMC-15

-10

-5

0

5

D(O

PI)

static PIthres=20 PIthres=10 PIthres=1

AlgorithmSelection:WhyDoIt?

Potentialtoprovidebetterperformancebyselectingalgorithmsforeachstate,insteadofusingonealgorithmforallstates

Algorithm A best

Algorithm A best

Algorithm B best

Network states

Per

form

ance

A

B

BuildingAlgorithmSelectors• Createanalgorithmselectortoexploitlinkbetweennetwork

stateandalgorithmperformance• Usemachinelearningtocreatetheselector

PerformanceData

Network State Measurements

Algorithm Selections

States Algorithms

Machine Learning

Algorithm Selector

OFFLINE (TRAINING) ONLINE (USE)

Different machine learning algorithms can beused, such as artificial neural networks (ANN),decision tree learners and random forests

• Creationofalgorithmselectorsalreadyestablishedincomputerscienceapplications

• Twomaintypes:– Direct

– EPM-based(EmpiricalPerformanceModel)

BuildingAlgorithmSelectors

Network State Measurements

Algorithm Selections

Algorithm Selector

EPM for Algorithm A

EPM for Algorithm B

Network State Measurements

Predicts performances

Algorithm Selections

Selection based on predictions

Predicts which algorithm will

be best

Application:PowerFlowManagement

• AdditionalDistributedGenerators(DGs)cancauseoverloadednetworkbranches

• PowerFlowManagement:– Activeapproach(ActiveNetworkManagement)– ControlDGoutputstomitigateoverloads

• Ideally:minimiseoverloadswhileminimisingDGcurtailment

Application:PowerFlowManagement

5

Power flow management

algorithms

implemented & tested

4

Case study power

systems

used for testing

40k+

Varying system states

simulated per system

20k+

Algorithm selector designs

developed & evaluated

Application:PowerFlowManagement• Example:IEEE57-bussystem,10,000states

Algorithm / SelectorNo. of

overloads (count)

Curtailed energy (MWh)

Best Individual Algorithm (OPF) 1367 749,900Best Direct Selector 771 900,734Best EPM-based Selector 772 926,646Optimal Selections 768 821,087

• Algorithm selection reduces the number of overloads

EconomicalandTechnicalLayers

Set points

Economical Layer

Technical Layer

Technical constraints:power voltage

InteractionbetweenthetwoLayers

Technical)zoning)

Technical))constraints)violated)

Y

N Lowest cost

AuctionRuns

Economical)zoning)

Solution)

Economical)zoning)Economical zoning

Lowest possible cost

Anexample– initialzoning• 11biddersparticipatingin

theauction– sixdemands(D1-D6)andfivesuppliers(S1-S5).

• OveralldemandinControlzoneAis12.5MWand10MWinControlzoneB.

OffersandbidsinzoneAInitialzoningDemand MW Cost (pence/MW)

Demand 1 5 120

Demand 2 2.5 100

Demand 3 5 90

Suppliers MW Cost (pence/MW)

Supplier 1 2.5 140

Supplier 2 5 130

Supplier 3 7.5 130

Supplier 4 12.5 90

Supplier 4 2.5 80

Usingatwo-sideduniform-priceauction,Supplier4delivers12.5MWtoDemands1,2and3atapriceof90pence.Beforeusingtheflexiblezoningstructure,thecostinEconomiczoneAalonewouldbe1125pence(12.5MW*90pence).

OffersandbidsinzoneBInitialzoningDemand MW Cost (pence/MW)

Demand 4 5 80

Demand 5 2.5 50

Demand 6 2.5 40

Suppliers MW Cost (pence/MW)

Supplier 5 12.5 50

Supplier 5 10 40

Supplier 5 10 40

Again,usingatwo-sideduniform-priceauctionSupplier5delivers10MWtoDemands4,5and6atapriceof40pence.Atthisprice,theenergycostinEconomiczoneBalonewouldbe400pence(10MW*40pence).TogetherwithEconomiczoneA,totalenergycostwouldbe1525pence.

Suggestion1– lowestoverallcostByreallocatingsomeDandSbetweenzonesZones MW Cost (pence/MW)

Economic Zone A (new zone)

Demand 1 5 120

Demand 3 5 90

Demand 5 2.5 50

Supplier 5 12.5 50

Economic Zone B (new zone)

Demand 2 2.5 100

Demand 4 5 80

Demand 6 2.5 40

Supplier 5 10 40

Total cost 1025

12.5*50+10*40=625+400=1025pence.

Suggestion2– second-lowestoverallcost(IfSuggestion1isnottechnicallyfeasible)

Zones MW Cost (pence/MW)

Economic Zone A (new zone)

Demand 1 5 120

Demand 2 2.5 100

Demand 4 5 80

Supplier 4 2.5 80

Supplier 5 10 40

Economic Zone B (new zone)

Demand 3 5 90

Demand 5 2.5 50

Demand 6 2.5 40

Supplier 5 10 40

Total cost 1400

12.5*80+10*40=1000+400=1400pence.

Decision• TheTechnicalLayerrejectsEconomicLayerSuggestion1.• EconomicLayerSuggestion2isfeasible.Suggestion2isacceptedbyTechnicalLayerbutis

animprovementoninitialsuggestion.

Self-OrganisingArchitectures• AnAgentbasedarchitecture• Self-Organisingpropertiesto

respondtoattacks• Operatesinthreestages

– Initialisation– PerformanceMonitoring– DecisionMakingandreconfiguration

• FuzzybasedDecisionmakingengine

• InterfaceswithMatpowerforloadflowcalculation

SmartMeterCustomerGeneratorEntity

LocalController/DataCollectionCentralServer

NetworkConfiguration

• 340Customerswithprofiles• 4PVGeneratorswithprofiles

• 4ActiveAggregates(4Dormant)• 4CentralCoreAgents

Observer Architect Gateway ErrorGenerator

AttackStrategies• Allattacksarebasedonlow-rate

DenialofServiceattacks• Selectedcustomeragentsactas

theattackers• AggregateAgentsascontrollers

arethetargets• Twolevelsofattacker

sophistication– Static:Lowlevelofsophistication,

attackerselectsafixedtarget– Adaptive:Anescalatedstate,attack

trafficredirectsafteranarchitecturetransition

29CombinationsofAttackStrategy,IntensityandSophistication

• BurstAttack:Attacktraffictransmittedfor250seconds

• ContinuousAttack:Attacktraffictransmittedoncetriggereduntiltheendofthesimulation

• SequentialAttack:TwoBurstinstancesatcriticalstagesofthecontrolprocess

Attackstimedtocoincidewithvoltagecontrolsignals

Respondingtoanattack• Thearchitectisinformedtheimpact

onperformancemetrics.• Allmetricsarecombinedtoforma

valueforComputationalBurden.• Afuzzybaseddecisionmakingengine

monitorstheburdenanditsrateofchange.

• Ifnecessaryarchitecturaltransitionsareinitiatedtoredistributeconnections,replaceagentsorincreaseaggregatecapacity

• Aimingtoimprovecontrolperformancethrougheasingloadonthecommunicationnetwork

Mathematics

Anthropology

ComputerScience

Geology

Economics

Physics MechanicalEngineering

ElectricalEngineering

Website : research.ncl.ac.uk/cesiTwitter : @cesienergyEmail : cesi@ncl.ac.uk

TheUrbanMicrogridinNewcastle:DemonstratorsonScienceCentral

EVFillingstation

CHPsystemThermalStorageHeatandCoolNetwork

Conclusions

• AutonomicMicrogridsandSelf*promising– DynamicZones,AlgorithmSelection

• MultipleMicrogrids?• DecentralisedMarkets• CyberSecurityneedsmorework

– Modeltheattackers• MultiVectorMicrogrids