Abstract：This paper proposes a multi-agent (MA) smart generation control scheme for the coordination of automatic generation control (AGC) in the power grid with system uncertainties.A novel MA new algorithm,i.e.DWoLF-PHC(λ) with a multi-step backtracking and a variable learning rate,is developed,which can effectively identify the optimal average policies under various operating conditions by the control performance standard (CPS).Based on the mixed strategy and the average policy,the algorithm is highly adaptive in stochastic Non-Markov environments and large time-delay systems and can also achieve AGC coordination in interconnected complex power systems in the presence of increasing penetration of renewable energies.Simulation studies on both a two-area load-frequency control (LFC) power system and the China Southern Power Grid model have been done respectively.The results show that the algorithm can achieve the optimal average policies,the closed-loop system has excellent properties,and the algorithm has a fast convergence rate and a higher learning ability compared with other existing intelligent methods.
席磊, 余涛, 张孝顺, 张泽宇, 谭敏. 基于狼爬山快速多智能体学习策略的电力系统智能发电控制方法[J]. 电工技术学报, 2015, 30(23): 93-101.
Xi Lei, Yu Tao, Zhang Xiaoshun,Zhang Zeyu, Tan Min. A Fast Multi-agent Learning Strategy Base on DWoLF-PHC(λ) for Smart Generation Control of Power Systems. Transactions of China Electrotechnical Society, 2015, 30(23): 93-101.
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2015, Vol. 30 
