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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 : [email protected]
TheUrbanMicrogridinNewcastle:DemonstratorsonScienceCentral
EVFillingstation
CHPsystemThermalStorageHeatandCoolNetwork
Conclusions
• AutonomicMicrogridsandSelf*promising– DynamicZones,AlgorithmSelection
• MultipleMicrogrids?• DecentralisedMarkets• CyberSecurityneedsmorework
– Modeltheattackers• MultiVectorMicrogrids