Parameter Optimization of Battery Energy Storage System Considering Degradation Using Reinforcement Learning - Muhammad Dzaky Ashidqi; Silviana Windasari; Rahmat Rahmat; Probokusumo Probokusumo (Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 2026)
Accurate and sustainable operation of battery energy storage systems (BESS) is critical for supporting renewable energy integration, ensuring both short-term reliability and long-term asset preservation. This study proposed a reinforcement learning (RL)-based scheduling framework designed to minimize power mismatch while mitigating degradation in lithium-ion batteries. The framework dynamically adapted to fluctuations in photovoltaic generation and residential load, enabling real-time decision-making. The performance was evaluated over a 30-day horizon using three indicators: average power mismatch, cumulative capacity loss, and system stability index (SSI). Results demonstrated that the proposed method achieved near-perfect load balance with an average mismatch of only 0.002 kW, while cumulative degradation remained limited to 0.22% and SSI was maintained at 0.96, reflecting high operational stability. The estimated daily degradation rate of 0.0073% corresponded to an annual capacity loss of approximately 2.7%, significantly lower than the 5–6% typically observed in uncontrolled cycling scenarios. Comparative analysis with simulated annealing (SA) and multi-objective genetic algorithm (MOGA) highlighted the balanced performance of the RL method. While MOGA eliminated mismatch at the expense of excessive degradation (0.60%) and simulated annealing reduced degradation but suffers from high mismatch (0.012 kW), the RL framework delivered the most balanced trade-off across all metrics. These findings confirm the potential of RL as a practical and sustainable strategy for PV–BESS integration, providing both technical resilience and extended battery lifetime.