Optimization and evaluation of operational and economic performance of grid-connected battery storage at different stages of battery health

Battery storage systems play a crucial role in modern energy infrastructure by enhancing grid flexibility. However, their long-term performance is limited by capacity degradation, which impacts operational efficiency and economic viability. This study proposes a degradation-aware optimization framework to evaluate the operational and economic performance of grid-connected battery systems across different stages of battery health, including new, mid-life, and near end-of-life conditions. The framework dynamically optimizes daily operational schedules, including cycle frequency, charge /discharge timing, and durations, in response to evolving degradation. The objective of optimization is to simultaneously maximize revenue and minimize degradation-related cost. The model incorporates both calendric and cyclic aging as functions of real-life operational conditions, ensuring informed and adaptive battery management. The results demonstrate that, despite a reduction in energy output per cycle from 95% in the first year to 77% near end-of-life, the proposed strategy stabilizes revenue across all stages by adjusting cycle characteristics. In the early stage, cycling is limited to once per day on over 80% of days, with extended charge/discharge durations (4–8 h) to mitigate initial degradation. In later stages, the strategy shifts to shorter charge/discharge durations (1–2 h) and increases the frequency to two cycles per day on up to 60% of days, thereby sustaining profitability. The findings offer valuable insights for grid operators, investors, and energy market participants in developing financially viable battery storage systems.