Degradation-Aware AC-Constrained Day-Ahead Active and Reactive Power Scheduling of Battery Energy Storage Systems in Active Distribution Networks

The increasing penetration of distributed energy resources requires battery energy storage systems (BESSs) to be scheduled economically while preserving network security and accounting for battery-life consumption. This work’s primary technical contribution is an integrated, degradation-aware framework for coordinated day-ahead active–reactive power dispatch of multiple BESS units in active distribution networks. The formulation minimizes grid-energy purchases, PV and BESS operation and maintenance costs, and monetized cycling and calendar degradation while enforcing nonlinear AC power-flow equations, voltage and branch-current limits, converter apparent-power capability, non-ideal state-of-charge dynamics, and terminal-energy requirements. Each candidate 24-h SoC trajectory is processed through a Rainflow-based semi-empirical aging model, allowing degradation to be included directly in the fitness evaluation. SELGO is employed as the optimization engine and coupled with a parallel successive-approximation AC power-flow evaluator. The framework is assessed through 100 independent executions under deterministic operating conditions and 100 stochastic demand and PV-generation scenarios in a modified 33-bus system, together with 100 stochastic scenarios in a modified 69-bus system. SELGO is compared with AJAYA, GA, MVO, PSO, and TDE under equivalent models, constraints, scenarios, and parameter-tuning conditions. Among the evaluated methods, SELGO yields the smallest mean operating cost for each of the three analyzed cases, reducing the corresponding base costs by 2.2015%, 2.4017%, and 3.0108%, respectively, while satisfying voltage, thermal, converter, and inter-temporal BESS constraints. The cumulative ablation analysis also supports the conditional benefit of the complete SELGO architecture. Across the analyzed cases, cycling intensity ranges from 0.7690 to 0.8386 EFC/day and daily aging from 6.4864×10−5 to 7.5160×10−5 day−1. Under the repeated-day assumption, the 80% SoH threshold is reached after 5.9756–6.9238 years, whereas the corresponding interval for 70% SoH is 10.8433–12.5639 years; these values are intended for comparative aging interpretation rather than as field-life predictions. The results show that the framework identifies low-cost, AC-feasible schedules while explicitly quantifying the associated battery-life consumption.