Intelligent Voltage Control Method in Active Distribution Networks Based on Averaged Weighted Double Deep Q-network Algorithm  

作　　者：Yangyang Wang Meiqin Mao Liuchen Chang Nikos D.Hatziargyriou 

机构地区：[1]Research Center for Photovoltaic System Engineering,Ministry of Education,School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230009,China [2]University of New Brunswick,Fredericton,NB E3B 5A3,Canada [3]National Technical University of Athens,Athens 15780,Greece [4]IEEE

出　　处：《Journal of Modern Power Systems and Clean Energy》2023年第1期132-143,共12页现代电力系统与清洁能源学报（英文）

基　　金：supported in part by the Anhui Province Natural Science Foundation(No.2108085UD02);the National Natural Science Foundation of China(No.51577047);111 Project(No.BP0719039)。

摘　　要：High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control methods.Voltage control based on the deep Q-network(DQN)algorithm offers a potential solution to this problem because it possesses humanlevel control performance.However,the traditional DQN methods may produce overestimation of action reward values,resulting in degradation of obtained solutions.In this paper,an intelligent voltage control method based on averaged weighted double deep Q-network(AWDDQN)algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network(DDQN)algorithm.Using the proposed method,the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process(MDP)model which is solved by the AWDDQN algorithm.The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN.The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs,and compared with the DQN and DDQN algorithms based methods,and traditional mixed-integer nonlinear program based methods.The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones.

关 键 词：Averaged weighted double deep Q-network(AWDDQN) deep Q learning active distribution network(ADN) voltage control electrical vehicle(EV) 

分 类 号：TM714.2[电气工程—电力系统及自动化]