文献类型：期刊文献

中文题名：A home energy management approach using decoupling value and policy in reinforcement learning

作者：Luolin XIONG[1];Yang TANG[1];Chensheng LIU[1];Shuai MAO[2];Ke MENG[3];Zhaoyang DONG[4];Feng QIAN[1]

机构：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China;[2]Department of Electrical Engineering,Nantong University,Nantong 226019,China;[3]School of Electrical Engineering and Telecommunications,University of New South Wales,Sydney NSW 2052,Australia;[4]School of Electrical and Electronics Engineering,Nanyang Technological University,Singapore 639798,Singapore

年份：2023

卷号：24

期号：9

起止页码：1261

中文期刊名：Frontiers of Information Technology & Electronic Engineering

外文期刊名：信息与电子工程前沿（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2023_2024】；

基金：Project supported by the National Natural Science Foundation of China(Nos.62293502,62293500,62293504,62073138,and 62173147);the Fundamental Research Funds for the Central Universities,China(No.222202317006);the Nanyang Technological University Startup Grant and MOE Tier 1(No.RG59/22)。

语种：英文

中文关键词：Home energy system;Electric vehicle;Reinforcement learning;Generalization

摘要：Considering the popularity of electric vehicles and the flexibility of household appliances,it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents.In this paper,a novel home energy management(HEM)approach is proposed based on a data-driven deep reinforcement learning method.First,to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles(EVs),an improved mathematical model integrating driver’s experience,unexpected events,and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems.Second,a decoupled advantage actor-critic(DA2C)algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks.Furthermore,separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios.Finally,comprehensive experiments are carried out to compare the proposed approach with existing methods,and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.