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Distributed Mode Scheduling for Coordinated Power Balancing

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  1. SmartGridComm2013 ARCH1, Oct. 22nd Distributed Mode Scheduling for Coordinated Power Balancing Hiroaki Kawashima (Kyoto University) Takekazu Kato (Kyoto University) Takashi Matsuyama (Kyoto University)

  2. Motivation • Volatile power supply & demand in future • More operating reserves (power plants)?  increase electricity price • How can we coordinate end users to balance/flatten the total power? Total supply = Total demand Solar & wind power strongly depend on weather, etc. Electric Vehicle (EV) and Plugin Hybrid EV (PHEV) require several kW x several hours for everyday charging Sunny Frequency 50 Cloudy 49 51 Demand Rainy Hydro Oil

  3. End Users(household, office, etc.) Smart Meter (Grid) PV generator Battery • End user: a unit of decision making for energy management • Household, office, factory, etc. • Assume that Energy Management System (EMS) is installed • Smart meter, communication device, sensor (controller) of appliances • Prosumer: Producer + Consumer Distribution board MCB MCB SB Smart Outlet (sensor/controller) MCB MCB Home Server Eco house in Kyoto (Fromhttp://www.kyo-ecohouse.jp)) Private Adapter-type Smart Outlet Grid (utility) Smart Appliances

  4. End Users’ Consumptions • Examples of consumption patterns of several families • Apartment (1 bed room ) with EMS [Kato, et al. SmartGridComm11,12] • Affected by not only life styles but events (travel, party, etc.) End users have their own dailypreference and often difficult to predict from utilities

  5. Coordination of End Users in a Community • Demand Response • Coordination as a community Electricity markets, Utilities External interaction Operator Coordinator system Request Community Control/ Negotiation (M2M) Sensing Controlled by own EMS Sharing similar objectives (peak shaving, etc.) Automated DR Multi-dwelling Office building Demand-side management “from demand side”

  6. Community-based Coordination for Flattening • Distributed architecture • User has own controller (autonomous agent) • Users negotiate their plans via coordinator • Day-ahead coordination • Online coordination Time (10min x 144) • Coordinator Plan Community +2000W Plan EMS Plan Extra power EMS EMS ITC IPP Power grid -2000W Factory EMS -1000W Control inside the household Request power Office, condominium

  7. Outline • Motivation • Community-based architecture to coordinate users • Models and algorithms • Distributed optimization • End user’s model • Simulation examples • Conclusion

  8. Coordination of Households (End Users) • Flatten the peak power while preserving each household’s satisfaction: Dissatisfaction/difficultyof using power profile Penalty function for peak :One day profile of household  minimize over :One day profile of household Q1 :How should the households and coordinator interact with each other? Without disclosing internal information (objective functions) Power[W] Power[W] Power[W] time time time 1 1 1 T T T ? HEMS HEMS HEMS HEMS :Total demand of all the households

  9. Idea 1:Profile-based Distributed Coordination • Flatten the peak power while preserving each household’s satisfaction: • Coordination of distributed controllers (autonomous agents) • Each user does not disclose their objective functions  Can avoid privacy issues / integrate different types of EMS (allow heterogeneity) Difficulty/dissatisfaction of using Penalty function for peak Profile-based negotiation to find best plan Who can avoid morning? (broadcast) User: preferred p Coordinator Power Coordinator:Broadcast profile Want to use in the morning Morning Morning Morning Evening is OK Noon is OK HEMS HEMS HEMS HEMS Iterate several times T

  10. Distributed Optimization via ADMM Coordinator’s version of (expectation for) each user’s profile: Sharing Problem: Gap! User’s preferred profiles Dual decomposition with Augmented Lagrangian Subject to Alternating Direction Method of Multipliers (ADMM): End-users Coordinator

  11. Outline • Motivation • Community-based architecture to coordinate users • Models and algorithms • Distributed optimization • End user’s model • Simulation examples • Conclusion

  12. Control in a Household • Change of device usage:time-shift, reduction • We focus on time-shift (scheduling)of appliance usage in a household as it has a large effect in power flattening • (Ex.) EV charging, A/C, dryer, dish washer, rice cooker Pot A/C (precooling) Q2. How to model normal patterns/acceptable range of each household?

  13. Idea 2:Objective Function of Households • Objective function • Difficulty/dissatisfaction of realizing profileby household Difficult to realize =1000 Easy to realize =0.01 Impossible = Training data センサデータ Demand [W] Many profiles are infeasible, i.e., = Can we learn the functionfrom data? Time T 1 We can use a probabilistic model of time series used in speech/gesture recognition Smart tap (smart plug)

  14. Probabilistic Model of Time Series • Hidden Semi-Markov Model • Assume that each device has its “internal modes” (discrete states) • Power consumption is determined by the control of modes • All the model parameters can be learned from daily usage data (Ex.) Standby Charging Standby Charging After charging Mode1 Mode 3 Mode 2 Mode 2 Mode 1 Output probability Mode 1 Duration distribution Mode 2 Demand [W] Mode 3 Duration Time T 1 Replace user-side optimization over by ”mode scheduling” Take into account temporal constraints (duration, order) on power levels Duration Duration

  15. Distributed Mode Scheduling P • Flatten the peak power while preserving each household’s satisfaction • Household need to send only their profiles • The coordinator do not need to know each objective function Coordinator Mode 1 Coordinator:Broadcast profile Mode 3 Mode 2 Power Households:Preferred profile Mode 4 Mode 1 Each household optimizes its mode scheduling in each iteration via dynamic programming HEMS HEMS HEMS HEMS Mode 2 Mode 3 T

  16. Simulation (Day-ahead Scheduling) Group 1 Group 2 • PHEV charging • 1kW x 3hours in a day Flexibility of the start time of charging Power k (# of Iteration) Time Mode 3 (after) Mode1(before) Mode 2(charging) • Two groups with different flexibility (given manually) • Group 1 (20 households) • Large flexibility of changing the start time • Group 2 (20 households) • Small flexibility • Result • Almost converge with in 20 iterations • Realize group objective (peak shaving) while taking into account users’ flexibility

  17. Conclusion: Distributed Mode Scheduling • Coordination of user-side controllers (autonomous agents)  Profile-based negotiation (ex. different types of EMS can be integrated) Negotiation is done by the coordinator’s broadcast signal (simple) • Hidden-semi Markov model for users’ objective functions Model can be learned from daily consumption patterns User-side optimization becomes “mode scheduling” and solved efficiently • Future work • Economic design of objective functions • Generators and batteries (charging/discharging) • Online negotiation (Users do not always follow the schedule)