Microgrids (MGs) provide a promising solution by enabling localized control over energy generation, storage, and distribution. This paper presents a novel reinforcement learning (RL)-based methodology for optimizing microgrid energy management. . Global eforts to mitigate climate change have led to a significant increase in the integration of renewable energy resources into the electricity grid. This transition not only necessitates the adoption of renewable energy technologies but also requires rethinking and redesigning existing power. . Electrical power systems are evolving, with a shift from large-scale centralized generators and one-way power flow to distributed generators and two-way power flows.
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