moving bits not watts: geographically coordinated ... · united states data center energy usage...
TRANSCRIPT
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Moving Bits Not Watts: Geographically Coordinated Frequency Control
ZhenhuaLiu
JointworkwithJoshuaComden,TanN.Le,YueZhao,BongJunChoiStonyBrookUniversity,SUNYKorea
LBNLApril2018
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Short bio
• AssistantProfessoratStonyBrookUniversitysince2014• Operationsresearch,computerscience,SmartEnergyTechnologiesCluster• Onleaveduring2014.6-2015.8(ITRI-RosenfeldPostdoctoralFellowwithMaryAnn)
• PhDinComputerScience2014,CaliforniaInstituteofTechnology• MS&BEinComputerScienceandControlfromTsinghuaUniversity• DoubledegreeBSinEconomicsfromPekingUniversity
• Research• Sustainablecomputing,Demandresponse,Onlineanddistributedoptimization,Schedulingandresourceallocation,BigDataSystems
• 4NSFgrantsduringthepast3years• ~30publicationsincludingSIGMETRICS,NSDI,~1,600citations• 4USpatentsfiled,2ofwhichhavebeenawarded
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My Wishlist
• Comments/suggestionsonmyresearch• Exploringopportunities
• Research:realdata,systems,domainknowledge,etc• Funding:NYSERDA,DoE,otherfederalagency
• Wecanleadorsub
• Long-termcollaborations
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Research examples
• DistributedOptimization• GeographicalLoadBalancing+DistributedFrequencyControl• DemandResponseProgramDesign
• OnlineOptimization• SmoothedOnlineConvexOptimization• CoincidentPeakPricing,Multi-scaleelectricitymarkets
• BigDataSystems• Multi-resourceallocation• BoundedPriorityFairness,InterchangeableResourceAllocation
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DataCenterabilityforFrequencyControlUSDataCenterElectricityConsumption
UnitedStatesDataCenterEnergyUsageReport,LawrenceBerkeleyNationalLaboratory,2016.
2000 20142005 20100
20
40
60
80
Year
2%TotalUSConsumption
DataCenter
Fast&PreciseEnergyControlSystems
E.g.DynamicFrequencyScalingofCPUsEnergyStoragewithinUPSs
LargepotentialforFrequencyControl
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CloudComputingisInterdependent
PowerNetwork
SharedComputationWorkload
DelayedorUnprocessedWorkload
SharedCost
PowerConsumptionDecisionsareDependent
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Requiresfrequencystabilizationinseconds
Communication SpeedPrivacyInfeasibleforlarge
networksAgentsdon’twanttoshareprivate
objectives
IssueswithCentralizedControl PrimaryFrequencyControl
DistributedControlforPFCOptimalDecentralizedPrimaryFrequencyControlinPowerNetworks
C.ZhaoandS.Low-CDC2014
AssumesIndependentCostsàSeparableObjectiveFunction(validforsomeapplications)
CloudComputingCostsareInterdependentàInseparableObjectiveFunction
TheneedforDistributioninPrimaryFC
+
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Goal:DesignPrimaryFCthatusesaNetworkofDataCenters
Approach:IncorporateInterdependentCosts
DistributedControlLaws SimulationResults
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CloudComputingModel
WorkloadIncomingRate
𝑊
DataCenterProcessingRates
𝑟↓1
ExcessProcessingRateoftheNetwork𝑠≔∑𝑗↑▒𝑟↓𝑗 −𝑊
𝑠<0
Workloadunprocessedordelayed
𝑟↓2
𝑔(𝑠)
Network-wide(Interdependent)Cost
SingleDataCenterPowerConsumption𝑑↓𝑗 ≔ 𝑑 ↓𝑗 + 1/𝑎↓𝑗 𝑟↓𝑗
Linearpowerusageprofile: 𝑑 ↓𝑗 constantoverhead 𝑎↓𝑗 conversioncoefficient
Costtoconvertelectricalàcomputing
power
𝑐↓𝑗 ( 𝑑↓𝑗 )
LoadBalancer
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PowerNetworkModel
Bus𝒋RealPowerInjection
𝑃↓𝑗 ≔ 𝑝↓𝑗 − 𝐷↓𝑗 𝜔↓𝑗 − 𝑑↓𝑗
Non-controllableloadsControllableloads(DataCenter)
Frequency-sensitive
FrequencyDeviation
𝜔↓𝑗 = 𝑑𝜃↓𝑗 /𝑑𝑡
Voltagephaseangle(w.r.t.nominal 𝜃↓0 )
RealPowerFlows
𝐹↓𝑗 (𝜽)≔∑𝑘:(𝑗,𝑘)𝜖𝐸↑▒𝑌↓𝑗𝑘 sin�(𝜃↓𝑗 − 𝜃↓𝑘 ) −∑𝑖:(𝑖,𝑗)𝜖𝐸↑▒𝑌↓𝑖𝑗 sin�(𝜃↓𝑖 − 𝜃↓𝑗 ) Maximumpowerflowacrossline(𝑗,𝑘)𝜖𝐸
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SystemDynamicsModel
SwingEquation𝑀↓𝑗 𝑑𝜔↓𝑗 /𝑑𝑡 = 𝑝↓𝑗 − 𝐷↓𝑗 𝜔↓𝑗 − 𝑑↓𝑗 − 𝐹↓𝑗 (𝜽)
PhysicalInertia
Equilibrium(𝜽↑∗ , 𝝎↑∗ , 𝑷↑∗ , 𝒅↑∗ ,𝑠)
𝑑𝜔↓𝑗↑∗ /𝑑𝑡 =0
𝑑𝑃↓𝑗↑∗ /𝑑𝑡 =0
𝜔↓𝑗↑∗ =𝜔
∀𝑗: (nochangeinfrequency)
(nochangeinpowerinjection)
(allbusesatsamefrequency)
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GeographicFrequencyControlProblem
Minimize CostofPFCtoDataCenters CostofFrequencyDeviations+𝑠,𝒅,𝝎 𝑔(𝑠)+∑𝑗↑▒𝑐↓𝑗 (𝑑↓𝑗 ) ∑𝑗↑▒𝐷↓𝑗 /2 𝜔↓𝑗↑2
ComputationalProcessingPowerBalances.t.
ElectricalPowerNetworkBalance
𝑠=∑𝑗↑▒𝑎↓𝑗 𝑑↓𝑗 −𝑊−∑𝑗↑▒𝑎↓𝑗 𝑑 ↓𝑗
0=∑𝑗↑▒(𝑝↓𝑗 − 𝐷↓𝑗 𝜔↓𝑗 − 𝑑↓𝑗 )
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13
Goal:DesignPrimaryFCthatusesaNetworkofDataCenters
Approach:IncorporateInterdependentCosts
DistributedControlLaws SimulationResults
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DistributedControlLaws
𝜇(𝑡)= 𝜇(0)+ 𝛽∫0↑𝑡▒(𝑠(𝜏)− (𝑔′)↑−1 (𝜇(𝜏)))𝑑𝜏
𝑑↓𝑗 (𝑡)= [(𝑐↓𝑗 ′)↑−1 (𝜔↓𝑗 (𝑡)− 𝑎↓𝑗 𝜇(𝑡))]↓𝑑 ↓𝑗 ↑¯𝑑 ↓𝑗 PowerNetwork
FrequencyDeviation 𝜔↓𝑗
LoadControlleratDataCenter𝒋
Load 𝑑↓𝑗
WorkloadBalancer
ProcessingRate𝑟↓𝑗
Workload𝑊
Supply/DemandDisturbance
ExcessRate𝑠WorkloadBalancerSignalController
ControlSignal𝜇
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ControlLawsConvergetoanOptimalPoint
Theorem:Thetrajectoryof(𝝎,𝑷,𝒅,𝑠,𝜇)asymptoticallyconvergestoanequilibriumpoint(𝝎↑∗ , 𝑷↑∗ , 𝒅↑∗ , 𝑠↑∗ , 𝜇↑∗ ).
Theorem:Anequilibriumpoint(𝝎↑∗ , 𝑷↑∗ , 𝒅↑∗ , 𝑠↑∗ , 𝜇↑∗ )isoptimaltotheGeographicFrequencyControlProblem.
Stability
Optimality
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Goal:DesignPrimaryFCthatusesaNetworkofDataCenters
Approach:IncorporateInterdependentCosts
DistributedControlLaws SimulationResults
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SimulationSetupNewEnglandIEEE39-bus
=25MWDataCenter
=Disturbanceof50MWDropinPower
10DataCenters=1.8%TotalDemand
PowerSystemToolbox(Matlab)
EachDataCentersharesafractionofa100MWequivalentcomputationalworkload.
Interdependentcost:𝑔(𝑠)=𝛾𝑠↑2
Eachwithdifferentefficiency
http://icseg.iti.illinois.edu/ieee-39-bus-system/
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ProposedControlLawsStabilizetheSystem
60
59.95
59.90 10 20 30 40 50
Time(sec)
Freq
uency(Hz)
5
0
-100 10 20 30 40 50
Time(sec)
Power(M
W)
-5
OLC
Proposed
NoControl
OLC(NoInterdependentCosts)OptimalDecentralizedPrimaryFrequencyControlinPowerNetworks
C.ZhaoandS.Low-CDC2014
Disturbanceof50MWDropinPowerDataCenterDemandChange
Convergestoequilibriumwithin30seconds
MostefficientDataCenter
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CostofProposedControlLawsisnearOptimal
50
0
100
OLC Proposed Optimal
Totalcost($/sec)
Costs=Interdependent=Independent
ProposedincorporatesInterdependentcosts,whereasOLCdoesnot.
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20
Goal:DesignPrimaryFCthatusesaNetworkofDataCenters
Approach:IncorporateInterdependentCosts
DistributedControlLaws SimulationResults
AsymptoticallyConvergestoward
OptimalCost
ConvergestoanEquilibriumthatisnearOptimalCost
Bonus:CommunicationandimplementationDelay
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DistributedControlLaws
𝜇(𝑡)= 𝜇(0)+ 𝛽∫0↑𝑡▒(𝑠(𝜏)− (𝑔′)↑−1 (𝜇(𝜏)))𝑑𝜏
𝑑↓𝑗 (𝑡)= [(𝑐↓𝑗 ′)↑−1 (𝜔↓𝑗 (𝑡)− 𝑎↓𝑗 𝜇(𝑡))]↓𝑑 ↓𝑗 ↑¯𝑑 ↓𝑗 PowerNetwork
FrequencyDeviation 𝜔↓𝑗
LoadControlleratDataCenter𝒋
Load 𝑑↓𝑗
WorkloadBalancer
ProcessingRate𝑟↓𝑗
Workload𝑊
Supply/DemandDisturbance
ExcessRate𝑠WorkloadBalancerSignalController
ControlSignal𝜇
Outdated𝜇(𝑡-𝚫))
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Systemwithdelay
Equilibrium(𝜽↑∗ , 𝝎↑∗ , 𝑷↑∗ , 𝒅↑∗ ,𝑠)
𝑑𝜔↓𝑗↑∗ /𝑑𝑡 =0
𝑑𝑃↓𝑗↑∗ /𝑑𝑡 =0
𝜔↓𝑗↑∗ =𝜔
∀𝑗: (nochangeinfrequency)
(nochangeinpowerinjection)
(allbusesatsamefrequency)
Andholdforatleast𝚫oftimeoftime
Stability:if𝛽issmallenough,thetrajectoryasymptoticallyapproachesanequilibriumpoint.
Corollary:Anequilibriumpoint(𝝎↑∗ , 𝑷↑∗ , 𝒅↑∗ , 𝑠↑∗ , 𝜇↑∗ )isoptimaltotheGeographicFrequencyControlProblem.
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0 10 20 30 40 50
Time (sec)
59.9
59.95
60
Fre
qu
en
cy (
Hz)
OLC
proposed
none
(a) Frequency
0 10 20 30 40 50Time (sec)
-10
-5
0
5
Po
we
r (M
W)
(b) Power demand change, �j
0 10 20 30 40 50Time (sec)
0
50
100
tota
l co
st (
US
D/s
ec)
OLC
proposed
(c) Cost change
Fig. 4. Trajectories of the system state variables: (a) Bus frequencies for each of the ten buses containing a datacenter under the three different controlschemes; (b) changes in load for each of the ten datacenters under the proposed control; (c) Total cost changes under OLC and the proposed control.
OLC proposed Lower bound0
20
40
60
80
100
tota
l co
st (
US
D/s
ec)
independent interdependent
(a) Total cost
1 2 3 4 5 6 7 8 9 100
0.5
1
1.5
2
tota
l co
st (
US
D/s
ec)
OLC proposed
(b) Independent costs
1 2 3 4 5 6 7 8 9 10
-10
-5
0
5
Po
we
r (M
W)
OLC proposed
(c) Load deviations
Fig. 5. (a) Total equilibrium cost separated by independent and interdependent costs of OLC, the proposed control, and the lower bound. For each datacenter(b) and (c) give the independent costs and equilibrium load deviations respectively under OLC and the proposed control. Note: The datacenters are numberedin decreasing order of efficiency aj .
0.05 0.1 0.15 0.2 0.25 0.3
γ
0
10
20
30
40
cost
savi
ngs
(%)
(a) Impact of � (interdependent cost)
40 60 80 100
flexibility (%)
0
10
20
30
40
cost
savi
ngs
(%)
(b) Impact of demand flexibility
0 50 100
Time (sec)
59.96
59.97
59.98
59.99
60
Fre
quency
(H
z)
No delay
∆=0.5 second
∆=1 second
(c) Impact of communication delay
Fig. 6. Sensitivity analysis in terms of cost savings by using the proposed control instead of OLC for: (a) Interdependent cost coefficient; (b) Demandflexibility. Stability analysis with the presence of (c) Communication delay and PFC updating every 0.1 second.
The results presented in this paper open up four distinctfuture research directions. The first is to explore how otherinterdependent systems (e.g. electric mass transit, thermalgrids) can be used to help increase the reliability of the grid.The second is to investigate how a network of datacentersthat are located in multiple disjoint power grids can utilizetheir interconnectedness to enhance the reliability in thosegrids. The third is to separate the computational workloadinto different resource demands, each with a different inter-dependent cost and computational efficiency. The fourth is toapply the distributed control laws to a system with a higher-order transient stability model. For our future work, we plan toextend the proposed control laws to take into account furthernetwork effects such as power flow constraints across linesand network losses.
X. ACKNOWLEDGMENTSWe would like to thank Dr. Changhong Zhao for his valuable
and insightful comments on this paper and the reviewers fortheir suggestions. This research is supported by NSF grantsCNS-1464388, CNS-1617698, CNS-1730128, CNS-1717558,and was partially funded by MSIP, Korea, grants IITP-2017-R0346-16-1007 and NRF-2015R1C1A1A01053788.
REFERENCES
[1] H. Shayeghi, H. Shayanfar, and A. Jalili, “Load frequency control strate-gies: A state-of-the-art survey for the researcher,” Energy Conversionand management, vol. 50, no. 2, pp. 344–353, 2009.
[2] A. Molina-Garcia, F. Bouffard, and D. S. Kirschen, “Decentralizeddemand-side contribution to primary frequency control,” IEEE Trans-actions on Power Systems, vol. 26, no. 1, pp. 411–419, 2011.
[3] N. Jaleeli, L. S. VanSlyck, D. N. Ewart, L. H. Fink, and A. G. Hoffmann,“Understanding automatic generation control,” IEEE transactions onpower systems, vol. 7, no. 3, pp. 1106–1122, 1992.
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Futurework
Includeothergeographicinterdependentsystems,e.g.thermalgrids,electricmasstransit,naturalgas.
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Research examples
• DistributedOptimization• GeographicalLoadBalancing+DistributedFrequencyControl• DemandResponseProgramDesign
• OnlineOptimization• SmoothedOnlineConvexOptimization• CoincidentPeakPricing,Multi-scaleelectricitymarkets
• BigDataSystems• Multi-resourceallocation• BoundedPriorityFairness,InterchangeableResourceAllocation
25
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Solar+WindenergyoutpacesDRadoption
0
2
4
6
8
10
12
14
2003 2005 2007 2009 2011 2013
Percen
tofG
enerationCapacity
California
Solar+Wind CAISODR
CaliforniaStateLegislaturesolution(2013):1,325MWofgridstorageby2020
http://energyalmanac.ca.gov/electricity/electric_generation_capacity.htmlFERCAssessmentofDemandResponseandAdvancedMeteringStaffReports:2010-2015.CAISODemandResponseBarriersStudy2009.
26
Expensive
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CustomerLoadReductions
Mandatory Voluntary
BecauseDRProgramshavevariousLevelsofCommitment
BaseInterruptibleProgram(BIP)EmergencyResponseService(ERS)
SpecialCaseResources(SCR)
DemandBiddingProgram(DBP)VoluntaryLoadReduction(VLR)EmergencyDRProgram(EDRP)
CAISOERCOTNYISO
27
DRprogramsallocatecustomeruncertainties
Customerisresponsible
LSEisresponsible
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Customeruncertainties
Baseline
MandatoryDRtobaselinemaybecostly
UMassTraceRepository:Smart*DataSet 28
VoluntaryDRisnotdispatchableforLSE.
vs.
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Challengesinhandlingcustomeruncertainties
LSEdoesnotknoweachofthecustomer’suncertainties.
DRprogramsthathavecustomerstakesomeresponsibilityaremandatory.
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Goal:IncreasereliableDRadoption
Approach:IncorporateCustomerUncertainties
DistributedAlgorithm LeverageRandomness
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Linearcontract
𝑥↓𝑖 (𝐷, 𝛿↓𝑖 )
Within10%OfflineOptimalsolution
31
LoadReduction
= ++Aggregatemismatch
Individualmismatch
Constantreduction
𝛼↓𝑖 𝐷 𝛽↓𝑖 𝛿↓𝑖 𝛾↓𝑖 = + +
Real-timeOptimal:Quadraticcostfunctions
Simpleandeasytoimplement
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Customercostfunctionsraisechallenges
Accuracy:LSEdoesnotknowtheircostfunctions
Privacy:Customersdonotwanttogiveuptheirinformation
àSeparatetheDRdecisionproblem
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DistributedAlgorithm
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Customer1LSE
Customer2
Customer𝑖( 𝛼↓𝑖 , 𝛽↓𝑖 , 𝛾↓𝑖 )
( 𝛼↓2 , 𝛽↓2 , 𝛾↓2 )
( 𝛼↓1 , 𝛽↓1 , 𝛾↓1 )
(𝜋↓𝑖 , 𝜆↓𝑖 , 𝜇↓𝑖 )
(𝜋↓2 , 𝜆↓2 , 𝜇↓2 )
(𝜋↓1 , 𝜆↓1 , 𝜇↓1 )(𝝅,𝝀,𝝁)
SubgradientMethod
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DistributedAlgorithmconvergestoCentralizedTheorem:Thedistributedalgorithm’strajectoryofpricesconvergestotheoptimalcentralizedprices.
2 4 6 8 100
1000
2000
3000
4000
5000
iteration,k
DistCent
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2 4 6 8 100
0.2
0.4
0.6
0.8
iteration,k
CUST
Cent
1
∑𝑖↑▒𝛼↓𝑖
LSE
iteration,k
2 4 6 8 100
0.002
0.004𝛼↓1
𝛼↓2
𝛼↓3 2 4 6 8 10
0
2 4 6 8 100
0.002
0.004
0.002
0.004
Customer1
Customer2
Customer3
𝛽↓𝑖 , 𝛾↓𝑖 (remainat0forcentralizedanddistributedsolutions)
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Customercostuncertainty0.1 0.2 0.3 0.4
0.2
0.4
0.6
0.8
1
1.2
0.1 0.2 0.3 0.4
0.5
1
1.5
2
Linearcontractdrawback:Itismandatory.
RequiredcomplianceduringhighcustomercostperiodsCustomercostuncertainty
Customercost
time
Ex.Importanttasksthatneedtobecompleted
𝐶↓𝑖 (𝑥↓𝑖 )
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LIN+()
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Aimtoavoidhighcustomercostperiods
0 1
LevelofCommitment
LIN
𝜌
Customercommitsto𝜌fractionoftimeslotstofollowLIN
Commitmentdecisionbasedonlyonrealizedcostfunction 𝐶↓𝑖 (𝑥↓𝑖 )= 𝑎↓𝑖 𝑥↓𝑖↑2
Ex.Quadratic
Customeronlyknows 𝑎↓𝑖 beforedecidingtocommittoDR
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LIN+(𝝆)reducescostfurtherthanLINonly
𝜌 LIN10.80.60.40.20
2300
2400
2500
2600
2700
2800
2900
RSD(a)=0.05RSD(a)=0.56
𝐶↓𝑖 (𝑥↓𝑖 (𝑡);𝑡)= 𝑎↓𝑖 (𝑡) (𝑥↓𝑖 (𝑡))↑2
LargercustomercostuncertaintyàlargersavingsfromLIN+(𝝆)
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Decreasefrom𝜌=1=1àavoidshighcustomercosts
Furtherdecreaseof𝜌àlargermismatchforLSE
LevelofCommitment
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Goal:IncreasereliableDRadoption
Approach:IncorporateCustomerUncertainties
Convergestocentralizedsolution
LowerSocialCostclosertoOffline
Optimal
DistributedAlgorithm LeverageRandomness
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Futurework:Incorporatepowernetworkconstraints
CongestedlinesàNecessarytocontrollocalmismatches 𝛿↓𝑖 locally(with 𝛽↓𝑖 )
Ex.Lineconstraints
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Research examples
• DistributedOptimization• GeographicalLoadBalancing+DistributedFrequencyControl• DemandResponseProgramDesign
• OnlineOptimization• SmoothedOnlineConvexOptimization• CoincidentPeakPricing,Multi-scaleelectricitymarkets
• BigDataSystems• Multi-resourceallocation• BoundedPriorityFairness,InterchangeableResourceAllocation
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Artificial Intelligence
Robotics
AutonomousVehicles
SmartGrid
1. Real-timedecisions2. Changingenvironment3. SwitchingCosts4. Utilizepastinformation
ContinualLearningStoica,etal.(2017)
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Example: Dynamic Capacity Provisioning
Demand𝑦↓𝑡
DataCenter
𝑥↓𝑡 =#serverson
OperatingCostℎ(𝑥↓𝑡 , 𝑦↓𝑡 )
SwitchingCost𝛽‖𝑥↓𝑡 − 𝑥↓𝑡−1 ‖
+
Futuredemandpredictedat𝑡𝑦↓𝑡+1|𝑡 , 𝑦↓𝑡+2|𝑡 ,…, 𝑦↓𝑡+𝑤−1|𝑡
Howmanyserversshouldbeturnedon/offrightnow?
Goal:DesignanOnlineAlgorithmwithPerformanceGuarantees.
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1-Dimensional Multi-Dimensional
sup┬{𝑦↓𝑡 , 𝑦 ↓𝑡|𝑡 ,…, 𝑦 ↓𝑇|𝑡 }↓𝑡=1↑𝑇 � cost(ALG)/cost(OPT) CompetitiveRatio:
Lin,etal.(2011):3-competitiveLazyCapacityProvisioning
Bansal,etal.(2015):3-competitiveMemoryless
Bansal,etal.(2015):2-competitive
IGCC12:1+𝑶(𝟏⁄𝒘 )-competitiveRecedingHorizonControl
1-StepPrediction
IGCC12:1+𝛀(𝟏)-competitiveRecedingHorizonControl
IGCC12:1+𝑶(𝟏⁄𝒘 )-competitiveAveragingFixedHorizonControl
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RHC vs AFHC
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FHC with limited commitment 𝑣
1. Every𝒗≤𝑤rounds,receivethepredictions𝑦 ↓𝑡|𝑡 ,…, 𝑦 ↓𝑡|𝑡+𝑤−1 2. Solvemin┬𝑥↓𝑡 ,…, 𝑥↓𝑡+𝑤−1 �∑𝜏=𝑡↑𝑡+𝑤−1▒[𝑐(𝑥↓𝜏 , 𝑦↓𝜏|𝑡 )
+‖𝑥↓𝜏 − 𝑥↓𝜏−1 ‖] 3. Implement𝑥↓𝑡 ,…, 𝑥↓𝑡+𝑣−1 forthenext𝒗rounds
Use𝑣ofthecalculatedactionsbeforeusingnewpredictions.
LevelofCommitment,𝑣
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Committed Horizon Control
1. Run𝑣FHCalgorithmswithlimitedcommitment𝑣,eachstartingatadifferentround.2. UseFHC(k)todetermine𝑥↓𝑡↑(𝑘) ,…, 𝑥↓𝑡+𝑣−1↑(𝑘) 3. Implement𝑥↓𝑡 = 1/𝑣 ∑𝑘=0↑𝑣−1▒𝑥↓𝑡↑(𝑘)
Averagethedecisionsbetweenasetofdifferent𝑣FHCalgorithms.
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CHC generalizes RHC and AFHC (Sigmetrics16)
AFHC(𝑣=𝑤)RHC(𝑣=1)
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Average-case Analysis Theorem:Let‖𝑓↓𝑘 ‖beameasureofthepredictionerrorfor𝑘stepsintothefuture,then𝐸[cost(𝐶𝐻𝐶)−cost(𝑂𝑃𝑇)]≤ 2𝑇/𝑣 (𝐷+𝐺∑𝑘=0↑𝑣−1▒‖𝑓↓𝑘 ‖↑𝛼 )Optimal𝒗dependsonhow‖𝒇↓𝒌 ‖growswith𝒌dependsonhow‖𝒇↓𝒌 ‖growswith𝒌
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Different properties give different optimal 𝑣
𝒗↑∗ =𝟏00(AFHC)00(AFHC)𝒗↑∗ =𝟏(RHC)
𝒗↑∗ ≈𝟕
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𝛼-Höldercontinuity-Höldercontinuity|𝑐(𝑥, 𝑦↓1 )−𝑐(𝑥, 𝑦↓2 )|≤𝐺‖𝑦↓1 − 𝑦↓2 ‖↓2↑𝛼
||𝑓(𝑠)||↓𝐹 =𝑐,𝐿≥𝑠>0
Rangelimitingcorrelationerror
||𝑓(𝑠)||↓𝐹 =0,𝑠>𝐿
Whitenoisevariancetrace(Cov(𝑒))= 𝜎↑2
IllustrationofTheorem