摘要

Although automatic generation control under CPS standard can be addressed by classic reinforcement learning Q(λ) algorithm, such problems as slow convergence and small optimal searching space still exist from the view point of multi-agent equilibrium solution. Therefore, this paper proposes correlated-equilibrium-Q(λ) (CEQ(λ)) learning. According to the response time delay of thermal plants, AGC adjustment units are first divided into different kinds of unit, such as coal, gas, hydro and so on. Then dynamic allocation orders of generators are analyzed by CEQ(λ) learning based multi-agent control framework. Simulation tests of two-area load frequency control model and China South Power Grid demonstrate that the CEQ(λ)-learning algorithm is more suitable for CPS instruction dynamic optimal allocation in stochastic and complex interconnection network, and it can enhance the robustness and adaptability of power systems in CPS assessment.

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