Demand Response in Data Centers

Grid Integration for Data CentersSVLG Data Center SummitOctober 24, 2012Girish Ghatikar, Vish Ganti, Nance Matson, and Mary Ann PietteDemand Response Research Center, Lawrence Berkeley National Laboratory (LBNL)http://drrc.lbl.gov/publications/DR-opportunities-and-enabling-technologies-data-centers-field-study

1Outline1. Problem Statement, Goals and Objectives2. Participating Data Centers3. Field Tests and Results4. Key Conclusions and Future Developments

2Problem Statement• According to a U.S. Environmental Protection Agency (EPA)forecast for 2011, 20% of U.S. data center energy use is inthe Pacific region alone. In the Pacific Gas and ElectricCompany utility territory, data centers are estimated toconsume 500 megawatts of annual peak electricity.• Recent 2011 data show that the increase in energy use isslightly lower than the EPA forecast.• The two key driving factors this study were:1. increasing data center energy use and cost, and2. impact of data center energy use during peak periods and high prices

3Project Goals and ObjectivesImprove understanding of demand response opportunitiesin data centers using field tests.Study is intended to accelerate the applicability andadoption of Demand Response in data centers andrelate the findings to similar data centers.DRis Demand Responsenot Disaster Recovery

4Data Centers and Demand Response StrategiesDemand Response Strategy*1 Server and CRAC units’ shutdown.2 Load Shifting or Queuing IT jobs – Server idling.3 Temperature set point adjustment.4 Shutdown and idling of IT storage clusters.5 Cooling relative to IT equipment load reduction.6 Load migration between heterogeneous systems.7 Load migration between homogeneous systems.LBNLB-50NetAppSDSC, UCB,LBNL B-50IT = IT equipment; CRAC = Computer Room Air Conditioners*Note: Detailed sequence of operations for each of the strategies is described in the report.

5Demand Savings at Data Center Level (kW & %)IT &SITELEGEND(a) LBNL 50B(b) NetApp(c) SDSC and UCB(d) SDSC and LBNL 50B

6Field Test Results | Correlation of CPU utilization and PowerUC Berkeley (Mako) – SDSC (Thresher) Load Migration StrategyDR Event: April 25, 2012 (12.30 pm to 5.10 pm)Migration

7Key Conclusions and Future Developments1. Similar data centers can provide DR with no impact to operations orservice-level agreements, set by the data center operators.2. A larger DR opportunity is within the IT equipment, with larger savingswhen combined with DR automation and integration with the cooling.3. Load migration strategies are unique of data centers and can provideFast-DR (e.g., ancillary services, intermittency with renewables).Future Developments:Study represents a small data-set. Future developments must:1. Consider Semi- or Full- automation using enabling technologies toexecute and administer resource-intensive DR strategies.2. Evaluate end-uses and its feasibility and economics for DR programssuch as dynamic pricing, ancillary services, renewable integration, etc.

8AcknowledgementsLBNL would like to thank the following for supporting the study:1. The subcontractors, Megawatt Consulting and SCG.2. LBNL and UC Berkeley data center team.3. Data Centers: NetApp, Inc., LBNL, UC Berkeley, and SDSC4. Enabling Technology providers: Power Assure, and SynapSense.Contact:Girish GhatikarGGhatikar@lbl.govhttp://drrc.lbl.gov/projects/dc

Back-Up Slides9

10Methodology & Data Center CharacterizationSite andTechnologySelectionFrameworkAssessment,Outreachand SiteRecruitmentDevelopCustomField TestPlansConductField Tests*Results andAnalysis**Note: Test plans and DR strategies were revised based on the results and findings from field.Characterization is used todevelop right set of DRstrategies for field tests,and applications to similardata centers.1. 2. 3. 4. Data Enabling End Load use Center Profile loads Technologies Analysis FunctionsSDSC= San Diego Supercomputer Center; UCB= University of California, Berkeley

11Summary of the Data Center SitesFloor area (and %relative to wholebuilding)LBNL Bldg. 50 NetApp SDSC UC Berkeley5000 ft 2 (100%) 1100 ft 2 (37%) 19000 ft 2 (54%) 10000 ft 2 (22%)Function Storage & HPC Storage SystemsStorage Systems &HPCStorage Systems &HPCUtility territory WAPA Power PG&E SDG&E PG&EDemand 550 kW 816 kW 2.3 MW 1 MWIT equipmentaverage loadEnablingtechnologies(IT and site)350 kW 145 kW 1.6 MW 550 kWSynapSensePower Assure,Automated LogicControlPower Assure,Opto22, JohnsonControlsPulse Energy,Emerson ControlsSDSC= San Diego Supercomputer Center; UC= University of California; HPC= High Performance Computing;WAPA= Western Area Power Administration; PG&E= Pacific Gas and Electric Company; SDG&E= San DiegoGas and Electric

12Summary of DR test resultsDataCenterNetAppLBNLB-50SDSC,UCB,andLBNLB-50DR StrategyEventDateActive EventPeriodResponse Period (min)5% shed 10% shedRecoveryPeriod(min)Test 1 (Shift/Queue IT jobs – Storage) 19-Dec-11 2.30pm to 5pm 10 22 25Test 2 (Temperature set pointadjustment)21-Dec-11 12.00pm to 1.00pm 5 15 15Test3 (Shift/Queue IT jobs – Storage w/manual temperature adjustment)13-Jan-12 2.00pm to 4.00pm 7 15 17Test 4 (Shift/Queue IT jobs – Storage) 11-Jan-12 1.00pm to 3.00pm 7 15 30Test 1 (Server and CRAC units shutdown) 28-Oct-11 8:00am to 5:00 pm 0 60 90Test 2 (Shift/Queue IT jobs – ServerMidnight to 6:001-Nov-11idling)amnot reached not reached n/aTest 3 (Temperature set point) 16-Nov-11 12:35 to Midnight not reached not reached n/aTest 4 (Data Center Shutdown)2-Dec-11 to 3:40 pm3-Dec-11 12:00 pm5 15 180Test 1 (Load migration - Homogenous –Idling)Test 2 (Load migration - Homogenous –Shutdown)Test 3 (Load migration - Heterogeneous– Decay)25-Apr-12 12.30pm to 2.45pm 2 6 225-Apr-12 2.46pm to 5.10pm 3 7 103-Jul-12to5-Jul-1210.45 am to11 am147 175 15

13Demand Savings Analysis (An Example)NetApp Test-1: IT load shifting with automatic response of site infrastructureDR Event: December 19, 2011 (2.30 pm to 5 pm)*Select IT equipment = 12%whole building loadNote: Standalone data center loads are not weather sensitive and the results must be similar in Summer.

14Field Test Results | example of load shed analysisNetApp Test 3 Analysis ( Queue IT jobs and manually adjust temperature set-pointsDemandSavings(kW and %)ITCoolingUnitsUPSTotal24(14.7%)23(14%)5( 3.1%)52.4(32%)Note: % at data centerlevelThe 23 kW demand savings from cooling equipment are dominated by chiller plant savingsDR Event: Jan 11, 2012 ( 2 pm to 4 pm)