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A SERVICE CURVE APPROACH TO DEMAND RESPONSE

Jean-Yves Le Boudec Dan- Cristian Tomozei. A SERVICE CURVE APPROACH TO DEMAND RESPONSE. 1. Agenda. Demand Response Service Curve Approach User Side Optimization Operator Side Optimization. Demand Response. Some Demand can be delayed !

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A SERVICE CURVE APPROACH TO DEMAND RESPONSE

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  1. Jean-Yves Le Boudec Dan-CristianTomozei A SERVICE CURVE APPROACH TO DEMAND RESPONSE 1 1

  2. Agenda • DemandResponse • Service CurveApproach • User SideOptimization • OperatorSideOptimization 2

  3. DemandResponse • SomeDemandcanbedelayed ! • DSO provides best effort service withstatisticalguarantees [Keshav and Rosenberg 2010] VoltalisBluepodswitches off thermal load for 30 mn Programmabledishwasher PeakSaver cycles AC for 15mn 3

  4. Price vs Quantity [Conejo et al, 2010] • Peaksaver, Bluepodact by quantity control • DSO/Aggregatorswitches off appliance • Price control oftenproposed as alternative • Userssavewhenpriceishigh • [Meyn 2010] : highvolatilityis an inherentfeature of electricitymarkets 4

  5. Centralized vs Distributed Control • Direct control by DSO/Aggregator for air conditioning, dryers • Not scalable, does not adapt to diversity and flexibility • Appliance control shouldbedone close to end-users 5

  6. Price BasedApproach QuantityBasedApproach + Predictablecosts -Centralized, inflexible, no user input + Distributed, flexible, user caninteract -Volatility, Reconciliation, Predictability Service CurveApproach + Distributed, flexible, user caninteract + Predictablecosts 6

  7. Definition of Service CurveApproach • Customer agrees to bethrottled, with a bound • Fixedprice per kWh • Total loadiscontrolled DSO Instant power Control by DSO Service curve contract Service curve 7

  8. Example 1:LoadSwitching • At most 30 mn of interruption total per day • Or reduction to for 60mn total per day 8

  9. Example 2:TwoLevel Control • Similar, but a minimum power isguaranteed • Bettersuited (than ex 1) whenapplied to an entire home /enterprise 9

  10. The Maths of Two-Level Control • The constraint on isequivalent to • i.e. the allowedenergy per window of time islowerbounded 10

  11. User SideOptimization • User can observe pastsignals and predictworst case future • Smart home controllercan manage loadaccordingly[LeBoudecTomozei 2011] 11

  12. Provider SideOptimization • Provider maysendsmoothsignals • E.g. to manycustomers, for long periods of time • Or burstysignals • E.g. to selectedcustomers, for shorterperiodsof time • Smoothsignals are optimal for stationary but randomloads, bursty signal are better for shaving peaks 12

  13. EPFL Testbed 13

  14. Conclusions • We propose a service curveapproach to demandresponse • DistributedApplies to total customerloadProvides large flxibility to providerProtects user frompriceuncertainty [Le BoudecTomezei 2011] Le Boudec J.Y. and Tomozei, D.C “Demand Response Using Service Curves”, EPFL-REPORT-168868, https://infoscience.epfl.ch/record/168868, 2011 14

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