An Energy Management Strategy for BESSs Based on the Multiverse Optimizer to Minimize Energy Purchase, Degradation, and Maintenance Costs

Abstract

This paper presents an energy management strategy for coordinating the active and reactive power of battery energy storage systems (BESSs) in active distribution networks. The model minimizes energy-purchase, maintenance, and degradation costs while considering efficiencies, self-discharge, converter limits, and cycling and calendar aging. The problem is solved using the Multiverse Optimizer (MVO) combined with an AC power flow based on Successive Approximations (SA). The methodology is evaluated in a 33-node network with photovoltaic generation through 100 independent demand and solar-generation scenarios. Compared with the Population-Based Genetic Algorithm (PGA), Salp Swarm Algorithm (SALPS), Grey Wolf Optimizer (GWO), and Vortex Search Algorithm (VSA), the MVO achieved the largest average cost reduction (150.43 USD/day; 2.23%) and shortest processing time (36.25 s), while maintaining low variability. The equivalent annual saving was 54,906.95 USD, with an estimated average battery lifetime of 12.54 years and satisfaction of the technical constraints.