This paper proposes a deep reinforcement learning-based framework for optimizing photovoltaic (PV) and energy storage system scheduling. . With the rapid integration of high-penetration renewable energy, its inherent uncertainty complicates power system day-ahead/intra-day scheduling, leading to challenges like wind curtailment and high operational 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. . As renewable energy adoption accelerates globally, energy storage scheduling has become critical for maintaining grid stability. Power generation facilities now face unprecedented challenges in balancing supply-demand mismatches caused by intermittent solar/wind resources. Liquid cooling and advanced fire suppression. .
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