Q-Learning Approach for Hierarchical AGC Scheme of Interconnected Power Grids
This paper formulates automatic generation control (AGC) for the power dispatch center as a twolayer hierarchical control framework, which can be divided into two discrete-time Markov decision process (DTMDP) sub-problems.The first one focuses on the solution of optimum AGC regulating commands under control performance standards, while the second works on the dynamic optimization allocation of the commands to various types of AGC units.The modelfree Q-learning and multicriteria reward function are proposed and designed specifically for two DTMDP subproblems, respectively.The proposed methodology can enhance the overall performance of the hierarchical AGC scheme from the viewpoint of long-term optimal objective.The effectiveness and efficiency of the AGC scheme are fully studied via simulation tests on a two-area interconnected hydro-thermal power system model, and test results are benchmarked against another heuristic algorithm and practical engineering approaches.
AGC CPS Q-learning Hierarchical control Dynamic generation allocation DTMDP Stochastic optimization
B. Zhou K. W. Chan T. Yu
Department of Electrical Engineering,The Hong Kong Polytechnic University,Hong Kong SAR,China College of Electric Power,South China University of Technology,Guangzhou,510640,China
国际会议
成都
英文
27-32
2011-09-27（万方平台首次上网日期，不代表论文的发表时间）