Balancing Efficiency and Longevity in Community Energy Storage Systems Using Predictive Scheduling

Community energy storage systems must balance equitable energy sharing among prosumers with long-term battery health. While forecast-driven allocation strategies improve fairness and operational efficiency of such systems, their impact on battery degradation remains underexplored. This study integrates supply-demand forecasting with a comprehensive battery aging model to examine the trade-offs between system performance and asset longevity in community storage applications. An extended power-law degradation model is used to capture the combined effects of state-of-charge variability, C-rate fluctuations, and thermal conditions on capacity fade mechanisms. To address these dynamics, a multi-objective optimization framework with special ordered sets linearization is proposed, balancing degradation minimization with renewable self-consumption maximization under adaptive power constraints. Validation using a 10-year dataset of five residential prosumers sharing a 20 kWh system shows that forecast-driven control enhances utilization while reducing capacity retention from 93.88% to 85.45% due to intensified cycling. The proposed degradation-aware optimization mitigates this penalty, retaining 91.01% capacity—representing a 6.5% improvement over the base forecast approach—while preserving efficiency gains. Results highlight that intelligent state-of-charge management with adaptive power limiting can reduce stress-induced aging while maintaining predictive scheduling advantages, particularly during periods of renewable energy surpluses when aggressive charging strategies become acceptable from a degradation perspective. The proposed framework demonstrates that sustainability and equity in community energy systems need not be mutually exclusive objectives.