A new optimal energy management strategy of microgrids using chaotic map-based chameleon swarm algorithm

Abstract

This study provides an optimal and efficient energy management strategy (EMS) for the cost-effective performance of a combined solar and green energy microgrid in both independent and grid-connected modes. A microgrid is formed by the system that includes a fuel cell (FC), a battery storage (BS), and a photovoltaic system (PV). Evidently, the unguaranteed features of the renewable energy and load electricity generate instability problems as well as economic ones, like operational expenses. To tackle these issues, a novel procedure is proposed that has been improved by a modified metaheuristic algorithm, called chaotic map-based chameleon Swarm Algorithm (CCSA). In this method, the simulation is based on a one-day planning perspective. The method aims to supply the power requirements of the load at the lowest possible cost under a constant DC bus voltage, protect the battery from overcharging and depletion, and improve the efficiency of the total system. To illustrate the suggested method's effectiveness, the simulation results of CCSA are compared with some studied methods in the literature, including GAMS, bald eagle search optimization algorithm (BEOA), original chameleon swarm algorithm (CSA), and grey wolf optimization algorithm (GWOA).