A CBGA-Driven Framework for Cost-Effective BESS Scheduling in Active Distribution Networks: Coordinating Active/Reactive Power with Degradation Awareness

This paper introduces a computationally efficient optimization framework for the day-ahead scheduling of battery energy storage systems (BESS) in active distribution networks (ADNs), in an attempt to address the critical challenge of reconciling economic objectives with battery lifespan preservation and AC network feasibility. Unlike existing approaches that separately handle active and reactive power dispatch or rely on linearized power flow approximations, the proposed methodology simultaneously optimizes both BESS active and reactive power setpoints using a Chu and Beasley Genetic Algorithm (CBGA) while explicitly quantifying degradation costs through a rainflow counting-based aging model that accounts for the depth of discharge, the average state of charge, and temperature effects. Each candidate dispatch plan is evaluated via a multi-period AC power flow solver based on the matrix successive approximations (AC-MSA) method, ensuring the rigorous enforcement of voltage and current constraints. The framework is validated on modified 33- and 69-bus ADNs under both deterministic conditions and a scenario-based robustness evaluation comprising 100 demand and photovoltaic generation profiles representative of Colombian grid conditions. The optimization itself is deterministic; the scenarios are used exclusively for the ex post evaluation of the resulting schedule. The framework is benchmarked against five metaheuristics: the adaptive JAYA algorithm (AJAYA), the parallel genetic algorithm (PGA), the multiverse optimizer (MVO), particle swarm optimization (PSO), and differential evolution (DE). Numerical results show that the CBGA achieved the lowest observed objective values among the compared methods under the tested configurations, with low dispersion across independent runs (standard deviations below 0.06%) and processing times under 0.05 h for a 24 h horizon. The margin over the closest competitors (AJAYA and MVO) is below 0.1% in the 33-bus deterministic case, so the comparison should be interpreted as an empirical ranking under a common experimental protocol rather than as a statistically established superiority. The proposed approach offers a practical decision-support tool that allows distribution system operators and BESS owners to strike an optimal balance between short-term operational revenues and long-term asset service life, thereby enhancing the economic viability of BESS investments in increasingly renewable-rich distribution networks.