基于改进SAC算法的微电网优化调度OA

In response to the intermittency and uncertainty of wind power generation,which result in low wind power utilization and high electricity purchase costs in microgrids,an intelligent dispatch strategy based on a residual-like soft actor-critic(R-SAC)algorithm was proposed.The scheduling problem was formulated as a partially observable Markov decision process(PO-MDP),and short-term predictions of wind power output and load variations were achieved by combining long short-term memory(LSTM)networks with the attention mechanism.A residual-like network structure was then incorporated into the traditional soft actor-critic(SAC)framework to construct an improved R-SAC algorithm,which enhanced the convergence speed and exploration efficiency of the policy.Finally,simulation experiments based on data from an actual microgrid in the northwest region validated the effectiveness and superiority of the proposed strategy.