Adaptation

Photovoltaic energy storage power adaptation

This paper proposes a deep reinforcement learning-based framework for optimizing photovoltaic (PV) and energy storage system scheduling. Sometimes two is better than one. 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 increasing deployment of renewable energy sources is reshaping power systems and presenting new challenges for the integration of distributed generation and energy storage.