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Model-free demand response Scheduling Strategy for Virtual Power Plants Considering Risk Attitude of Consumers

CSEE Journal of Power and Energy Systems 2023年第2期9卷 516-528页

作者： Yi Kuang Xiuli Wang Hongyang Zhao Tao Qian Nailiang Li Jianxue Wang Xifan Wang School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China Shaanxi Key Laboratory of Smart Grid Xi’an Jiaotong UniversityXi’an 710049China

Driven by modern advanced information and communication technologies,distributed energy resources have great potential for energy supply within the framework of the virtual power plant(VPP).Meanwhile,demand response(DR)is becoming increasingly important for enhancing the VPP operation and mitigating the risks associated with the fluctuation of renewable energy resources(RESs).In this paper,we propose an incentivebased DR program for the VPP to minimize the deviation penalty from participating in the power market.The Markov decision process(MDP)with unknown transition probability is constructed from the VPP’s prospective to formulate an incentivebased DR program,in which the randomness of consumer behavior and RES generation are taken into consideration.Furthermore,a value function of prospect theory(PT)is developed to characterize consumer’s risk attitude and describe the psychological factors.A model-free deep reinforcement learning(DRL)-based approach is proposed to deal with the randomness existing in the model and adaptively determine the optimal DR pricing strategy for the VPP,without requiring any system model information.Finally,the results of cases tested demonstrate the effectiveness of the proposed approach.

关键词： incentive-based demand response Markov decision process virtual power plant

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Prediction and assessment of demand response potential with coupon incentives in highly renewable power systems

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Protection and Control of Modern Power Systems 2020年第1期5卷 124-137页

作者： Hao Ming Bainan Xia Ki-Yeob Lee Adekunle Adepoju Srinivas Shakkottai Le Xie Electric Reliability Council of Texas 800 Airport RdTaylorTX 78664USA Intellectual Ventures BellevueWAUSA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUSA

demand response(DR)provides both operational and financial benefits to a variety of stakeholders in the power system.For example,in the deregulated market operated by the Electric Reliability Council of Texas(ERCOT),load serving entities(LSEs)usually purchase electricity from the wholesale market(either in day-ahead or real-time market)and sign fixed retail price contracts with their end-consumers.Therefore,incentivizing end-consumers’load shift from peak to off-peak hours could benefit the LSE in terms of reducing its purchase of electricity under high prices from the real-time market.As the first-of-its-kind implementation of Coupon incentive-based demand response(CIDR),the EnergyCoupon project provides end-consumers with dynamic time-of-use DR event announcements,individualized load reduction targets with EnergyCoupons as the incentive for meeting these targets,as well as periodic lotteries using these coupons as lottery tickets for winning dollar-value gifts.A number of methodologies are developed for this special type of DR program including price/baseline prediction,individualized target setting and a lottery mechanism.This paper summarizes the methodologies,design,critical findings,as well as the potential generalization of such an experiment.Comparison of the EnergyCoupon with a conventional Time-of-Use(TOU)price-based DR program is also conducted.Experimental results in the year 2017 show that by combining dynamic coupon offers with periodic lotteries,the effective cost for demand response providers in EnergyCoupon can be substantially reduced,while achieving a similar level of demand reduction as conventional DR programs.

关键词： EnergyCoupon incentive-based demand response Critical peak pricing Electricity market Prospect theory

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