The energy storage is dispatched for peak shaving and forecast-deviation minimisation from around noon to late evening. . This paper proposes a deep reinforcement learning-based framework for optimizing photovoltaic (PV) and energy storage system scheduling. By modeling the control task as a Markov Decision Process and employing the Soft Actor-Critic (SAC) algorithm, the system learns adaptive charge/discharge. . The authors propose a two-stage look-ahead daily scheduling strategy for distributed energy storage located in distribution networks with a substantial photovoltaic (PV) penetration. Specifically, a price-based demand response model is. . key to the operation of smart energy systems. Jamahori and Rahman [ 25] highlighted that each energy storage tech time requirement has been established to date.
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