Multi-Objective Battery Dispatching using an Enhanced SAC Algorithm

Renewable Energy Communities (RECs) have emerged as a promising solution to the intermittent nature of Renewable Energy Sources (RES). The stochastic nature of RES demands the presence of Energy Storage Systems (ESS) to increase self-consumption. Such systems, however, must be properly managed to enhance battery life, minimize wear costs, and ensure safe operating conditions. RECs face challenges such as low self-consumption and limited battery lifespan. This paper proposes an enhanced Soft Actor-Critic (SAC) algorithm tailored to optimize battery dispatch in RECs. Unlike standard SAC, which primarily relies on action clipping, our approach directly penalizes constraint violations within the agent's objective, guiding it toward more feasible and profitable dispatch strategies. The method maintains optimal battery State of Charge (SoC), extends battery life, and maximizes economic returns from solar energy usage. Compared to the standard SAC model, our Lagrange-SAC approach achieves an 18.2% improvement in the mean Self-Sufficiency Ratio (SSR), significantly increasing the efficiency of solar energy utilization. These findings highlight the potential of advanced reinforcement learning techniques to enhance real-world energy systems, promote sustainable practices, and improve the resilience of energy infrastructures.