Stochastic Optimal CPS Relaxed Control Methodology for Interconnected Power Systems Using Q-Learning Method
Stochastic Optimal CPS Relaxed Control Methodology for Interconnected Power Systems Using Q-Learning Method
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使用 Q-Learning 方法的互联电力系统随机最优 CPS 松弛控制方法
DOI：
10.1061/(asce)ey.1943-7897.0000017
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发表时间：
2011-09
期刊：
影响因子：
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中科院分区：
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This paper presents the application and design of a novel stochastic optimal control methodology based on the Q-learning method for solving the automatic generation control (AGC) under the new control performance standards (CPS) for the North American Electric Reliability Council (NERC). The aims of CPS are to relax the control constraint requirements of AGC plant regulation and enhance the frequency dispatch support effect from interconnected control areas. The NERC’s CPS-based AGC problem is a dynamic stochastic decision problem that can be modeled as a reinforcement learning (RL) problem based on the Markov decision process theory. In this paper, the Q-learning method is adopted as the RL core algorithm with CPS values regarded as the rewards from the interconnected power systems; the CPS control and relaxed control objectives are formulated as immediate reward functions by means of a linear weighted aggregative approach. By regulating a closed-loop CPS control rule to maximize the long-term discounted...
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影响因子：
6.6
作者：
N. Hoonchareon;C. Ong;R. Kramer
通讯作者：
N. Hoonchareon;C. Ong;R. Kramer
DOI：
10.1007/978-3-030-78731-8_5
发表时间：
2021-11
期刊：
Advanced Textbooks in Control and Signal Processing
影响因子：
--
作者：
Hai Lin;P. Antsaklis
通讯作者：
Hai Lin;P. Antsaklis
DOI：
10.1109/tpwrs.2002.1007921
发表时间：
2002
期刊：
IEEE Power Engineering Review
影响因子：
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作者：
T. Sasaki;K. Enomoto
通讯作者：
T. Sasaki;K. Enomoto
影响因子：
6.6
作者：
Mao Xiao-ming;Z. Yao;Guan Lin;Wu Xiaochen
通讯作者：
Mao Xiao-ming;Z. Yao;Guan Lin;Wu Xiaochen
DOI：
10.2307/3008248
发表时间：
1970
期刊：
--
影响因子：
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作者：
A. S. Harding
通讯作者：
A. S. Harding