Genetic Based Fuzzy Q-Learning Energy Management for Smart Grid
For the energy management problems for demand response in electricity grid, a genetic based fuzzy Q-learning consumer energy management controller (CEMC) is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for the consumer behavior in electricity grid. In this case, the Q-learning, which is independent of mathematic model has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. Simulation results show that the proposed controller can learn to take the best action to regulate consumer behavior with the features of low average end-user financial costs and high consumer satisfaction.
Demand response Q-learning fuzzy inference system genetic operator
LI Xin ZANG Chuanzhi ZENG Peng YU Haibin
Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University, Shenyang 1100 Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, 110016, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
6924-6927
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）