Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management  

作　　者：Chao Huang Hongcai Zhang Long Wang Xiong Luo Yonghua Song 

机构地区：[1]IEEE [2]State Key Laboratory of Internet of Things for Smart City,University of Macao,Macao S.A.R.,China [3]School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China [4]Shunde Graduate School,University of Science and Technology Beijing,Foshan 528399,China [5]State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering,University of Macao,Macao S.A.R.,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2022年第3期743-754,共12页现代电力系统与清洁能源学报（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62002016);the Science and Technology Development Fund,Macao S.A.R.(No.0137/2019/A3);the Beijing Natural Science Foundation(No.9204028);the Guangdong Basic and Applied Basic Research Foundation(No.2019A1515111165)。

摘　　要：This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation.However,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions.Thus,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)algorithm.The DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous actions.The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment.However,unsafe actions,which violate system constraints,can give rise to great cost.To handle such problem,a safe-MDRL algorithm is further proposed.Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management.The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset.Moreover,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.

关 键 词：Demand response deep reinforcement learning discrete-continuous action space home energy management safe reinforcement learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TU855[自动化与计算机技术—控制科学与工程]