Greening Geographic Load Balancing

Data Center Demand Response: Coordinating IT and the Smart GridZhenhua Liuzhenhua@caltech.eduCalifornia Institute of TechnologyDecember 18, 2013Acknowledgements:Adam Wierman1, Steven Low1, Yuan Chen2, Minghong Lin1, Lachlan Andrew3, , Cullen Bash2, Niangjun Chen1, Ben Razon1, Iris Liu1 1California Institute of Technology, 2HP Labs, 3Swinburne University of Technology

job market12Sustainable IT IT for sustainabilityEnergy efficiency of IT systemIT as a demand response providerbroader impacts2Renewables are coming3Cumulative capacity has grown by 72% from 20002011

Wind and solar grow fastest (13x and 51x)

Source: Gelman, R. (2012). 2011 Renewable Energy Data Book (Book). Energy Efficiency & Renewable Energy (EERE)Worldwide Renewable Electricity Capacity increase in renewable3Challenges with renewables4

Generation

TimePower12 AM12 AMGeneration = Demandat all timesat all locations

DemandKey constraint: predictablecontrollablelow uncertaintyGeneration follows Demandtraditional approach: generation follows supply4Challenges with renewables5

GenerationGeneration = Demandat all timesat all locations

DemandKey constraint: responsiveless controllablehigh uncertaintyDemand follows Generation(to some extent)

expensivechallenges with renewable integration5Need huge growth in demand response6Data centers are a promising option

Wind and Solar capacities are growing 15~40% per year large loads: 500kW~50MW each

increasing fast: 10~15% per year

significant flexibilitiesOthers vs DC6Data center flexibilitiescooling, lighting, 5% of consumption can be shed in 2 min [LBNL2012] 10% of consumption can be shed in 20 min [LBNL2012] workload managementTemporal demand shaping [Sigmetrics12][3 patents]HP Net-Zero data center, 2013 Computerworld Honors LaureateGeographical load balancing [Sigmetrics11][GreenMetrics11][IGCC12]Best student paper award at ACM GreenMetrics 2011Best paper award at IEEE Green Computing 2012Pick of the Month in the IEEE STC on Sustainable Computing

onsite backup generators & storage7previous work: design & implementation, distributed and online algorithms with theoretical guarantee7

Geographical load balancingpower and coolingspatial flexibility

future: robustness when adding new data center or failure8Data center flexibilitiescooling, lighting, 5% of consumption can be shed in 2 min [LBNL2012] 10% of consumption can be shed in 20 min [LBNL2012] workload managementTemporal demand shaping [Sigmetrics12][3 patents]HP Net-Zero data center, 2013 Computerworld Honors LaureateGeographical load balancing [Sigmetrics11][GreenMetrics11][IGCC12]Best student paper award at ACM GreenMetrics 2011Best paper award at IEEE Green Computing 2012Pick of the Month in the IEEE STC on Sustainable Computing

onsite backup generators & storage9great opportunitiesData center demand response today10coincident peak pricing (CPP)timecustomer power usagesystem peak hour(decided by utility)coincident peak demandcustomers peak demandMany programsTime of use (ToU) pricingWholesale marketAncillary service market

Monthly bill = fixed charge + usage charge + peak charge + coincident peak charge CPP is popular

how CPP works10CPP in practiceRates at Fort-Collins Utilities, Colorado, USA11CP is very important!fixed charge: $101.92/monthusage charge rate: $0.0245/kWhpeak charge rate: $4.75/kWcoincident peak (CP) charge rate: $12.61/kWExample: average demand 10MW, peak demand 15MW, CP demand 14MWMonthly bill = fixed charge + usage charge + peak charge + coincident peak charge $101.92$176,400$71,250$176,540

CPP is very important11DC management is challenging12Uncertainties in CPonly known at the end of the monthParticipating CPP program is risky!algorithm designchallenge1213mind f(d; t)expected cost optimizationdata mining for patternsless accurate with renewablesrobust optimizationmind Et[f(d; t)]mind maxt [f(d; t)]online algorithmoptimal competitive ratioExtensions warning signals backup generator & local renewables workload & renewable prediction errorswhat is f

two approaches

performance guarantee

extension1314mind f(d; t)expected cost optimizationrobust optimizationTimePower12 AM12 AMperiods with high probability to be CPTimePower12 AM12 AMmake the demand flatLimited demand responsemarket designlimited demand response14Potential of data center demand response15

Goal: minimize voltage violation with large PV generation20MW DC 3MWh storage=voltage violation ratewith 20% flexibility optimal location & fast charge rate

great potential from the societys perspective15Pricing data center demand response16

supply function si(p)Pricing data center demand responseefficiency loss due to user strategic behavior [XLL2013]17

market-clearing price p

supply function bidding

but when we have data centers works well when no user has large market powerPricing data center demand response18

price p

prediction-based pricing supply function

Pricing data center demand response19

prediction-based pricing supply si(p)efficiency loss is independent of market powerbut depends on prediction accuracy

parameter in supply functionfor quadratic cost function

20supply function biddingprediction-based pricing vsefficiency loss depends on market powerefficiency loss depends on prediction accuracy

supply function biddingprediction-based pricing

supply function biddingprediction-based pricing21supply function biddingprediction-based pricing vsincorporating power networkvalue of locationoptimal power flowlearning from user responseexploitation vs explorationtheory of quantization[BSXY2012]Pick of prices during learning stageDesign demand response menufuture work

connection2122

demand response

flexibilitiescloud platform23Thank you!References[LBNL2012] Ghatikar, Girish, et al. "Demand response opportunities and enabling technologies for data centers: Findings from field studies." LBNL-5763E. 2012.

[XLL2013] Yunjian Xu, Lina Li, Steven Low. On the Eciency of Parameterized Supply Function Bidding with Capacity Constraints. 2013.

[BSXY2012] Bergemann, Dirk, et al. "Multi-dimensional mechanism design with limited information." Proceedings of the 13th ACM Conference on Electronic Commerce. ACM, 2012.24Model for prediction based pricing25

user

for each realizationcost function

supply

utility

penalty

social objective

offline optimal

Model for prediction based pricing26

utility

penalty

social objective

offline optimalperformance evaluation

competitive ratio

Theorem