A new operational framework addresses a critical challenge for utilities deploying battery energy storage in active distribution networks: how to schedule daily charging and discharging to minimize costs while respecting grid constraints and understanding battery aging implications.
The research integrates several key components often treated separately in prior work. The scheduling objective minimizes hourly operating costs for battery power exchanges, while an AC power-flow verification step ensures all proposed schedules are physically feasible on the actual distribution network—not just on simplified linear models. The team also developed a correction procedure to handle infeasible state-of-charge trajectories and implemented a consistent testing environment to fairly compare five different optimization algorithms.
Validation occurred across three progressively larger distribution feeders (33, 69, and 136 buses) using both deterministic and probabilistic scenarios. The Coyote Optimization Algorithm emerged as the most cost-effective approach, achieving 1.0–1.8% operational savings compared to competing methods. Importantly, all schedules satisfied both storage limits and AC network voltage and thermal constraints.
A noteworthy contribution is the post-dispatch battery-aging assessment. After generating optimal schedules, the researchers calculated daily cycling profiles and projected battery state-of-health degradation. Results showed equivalent full-cycle rates near 0.8 cycles per day with estimated 80% state-of-health lifespans of 6–8 years—realistic durations that help utilities evaluate total cost of ownership.
The framework's practical value lies in its reproducibility and transparency. By standardizing the evaluation chain—correction procedures, benchmarking methods, computational environment, and aging calculation—the work provides utilities a replicable methodology for their own networks. The inclusion of post-dispatch aging analysis, though not incorporated into the optimization itself, offers operators visibility into long-term battery health consequences of cost-driven scheduling, enabling more informed investment and operational decisions.
