基于类脑记忆协同经验回放算法的自动发电控制OA

The large-scale integration of renewable energy sources and electric vehicle clusters has introduced strong stochastic disturbances,which amplify frequency oscillations and challenge the performance of automatic generation control(AGC).However,existing reinforcement learning approaches exhibit low efficiency in utilizing experience samples,which hinders their ability to accurately reflect the dynamic state changes of the AGC system,thereby leading to suboptimal control strategies.To overcome these limitations,this paper proposes a twin delayed deep deterministic policy gradient algorithm for AGC,incorporating brain-inspired memory cooperative experience replay.By integrating short-term,long-term,and expert experience samples,it cooperatively utilizes and interactively samples multi-source experiences,enabling the agent to consider real-time feedback,historical data,and external guidance for more accurate state reflection and optimal control.The simulation results of both the two-area load frequency control model with electric vehicle cluster access and the four-area model of the Central China power grid indicate that the proposed algorithm can effectively suppress frequency oscillations and improve the performance of AGC.