Hierarchically Correlated Equilibrium Q-learning for Multi-area Decentralized Collaborative Reactive Power Optimization  

作　　者：Min Tan Chuanjia Han Xiaoshun Zhang Lexin Guo Tao Yu 

机构地区：[1]Yangjiang Power Supply Bureau,China Southern Power Grid Company,Yangjiang 529500,China [2]College of Electric Power,South China University of Technology,Guangzhou 510640,China [3]Shenzhen Power Supply Co.,Ltd,Shenzhen 518000,China

出　　处：《CSEE Journal of Power and Energy Systems》2016年第3期65-72,共8页中国电机工程学会电力与能源系统学报（英文）

基　　金：supported in part by National Key Basic Research Program of China(973 Program:2013CB228205);National Natural Science Foundation of China(51177051,51477055).

摘　　要：A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.

关 键 词：Hierarchically correlated equilibrium multiarea decentralized collaborative reactive power optimization reinforcement learning 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]