Performance enhancement of a hybrid energy storage systems using meta-heuristic optimization algorithms: Genetic algorithms, ant colony optimization, and grey wolf optimization

The efficient integration of Energy Storage Systems (ESS) into the electricity requires an effective Energy Management System (EMS) to improve the stability, reliability and resilience of the overall interconnected power system. This article explores the viability of using Hybrid Energy Storage System (HESS) combining batteries and Supercapacitors (SC) connected to Renewable Energy Sources (RES) such as solar Photovoltaic (PV) systems. The challenges that may arise in the design of an effective EMS include the SC peak rate current limitations, slow dynamic response, and the batteries' susceptibility to stress induced by meteorological conditions like irradiation and variable load. Metaheuristic optimization methods have proved very effective to solve complex, multicriteria optimization problems. This paper presents the modeling and optimization of an EMS for a HESS based on Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Grey Wolf optimization (GWO). The proposed EMS is simulated using MATLAB/Simulink and Sim Power Systems toolbox and extensively tested under different operating conditions such as variable solar irradiance and load demand and compared with other approaches based on classical Proportional-Integral (PI) control. The simulation results have shown that the PI controller tuned using the GWO algorithm features a simple implementation and does not require significant computation time. Furthermore, this control approach provides a fast transient response, therefore improving the charge and discharge performance of the HESS, leading to a reduced battery stress and increased lifespan.