Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

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Deep reinforcement learning (DRL) methods for microgrid economic dispatch often suffer from reduced decision accuracy due to environmental changes within control periods. To address this challenge, this paper proposes an information-enhanced DRL approach (IE-DRL) that incorporates predicted future load and photovoltaic power generation into the state space, enhancing the robustness of dispatch decisions. The proposed method is evaluated using real-world microgrid data from Shandong Province, China. Experimental results demonstrate that the extended versions of the four baseline DRL algorithms (IE-DDPG, IE-SAC, IE-TD3, and IE-PPO) achieved reductions in operating costs of 5.63%, 12.85%, 7.87%, and 6.52%, respectively, validating the effectiveness and generalizability of the proposed approach.