Distributed Mode Scheduling for Coordinated Power Balancing

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)