This paper presents a novel reinforcement learning (RL)-based methodology for optimizing microgrid energy management. Specifically, we propose an RL agent that learns optimal energy trading and storage policies by leveraging historical data on energy production, consumption, and. . Abstract—The increasing integration of renewable energy sources (RESs) is transforming traditional power grid networks, which require new approaches for managing decentralized en-ergy production and consumption. By applying QNN to. . This paper develops a new management framework for optimal operation of the hybrid AC–DC microgrids incorporating renewable energy sources and storages. Hybrid microgrid consists of two parts of AC and DC to supply the AC and DC loads, respectively. The power exchange capability of hybrid. .
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