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This paper proposes a deep reinforcement learning-based framework for optimizing photovoltaic (PV) and energy storage system scheduling. . Abstract We study the optimal management of a photovoltaic system's battery owned by a self-consumption group that aims to minimize energy consumption costs. By modeling the control task as a Markov Decision Process and employing the Soft Actor-Critic (SAC) algorithm, the system learns adaptive charge/discharge. . Integrating a battery energy storage system (BESS) with a solar photovoltaic (PV) system or a wind farm can make these intermittent renewable energy sources more dispatchable. In this thesis, three different control methods for BESS are proposed for this purpose.
