Optimization algorithms for hybrid energy storage systems based microgrid performance enhancement

The research addresses critical challenges in microgrid reliability, stability, and energy management in microgrids through the optimization of a hybrid energy storage system (HESS). The system integrates photovoltaic modules with batteries, supercapacitors, and hydrogen-based fuel cells to enhance system performance. A proportional-integral controller optimized using particle swarm optimization (PSO) and backtracking search algorithm (BSA) to improve HESS performance. The optimized HESS reduces surplus photovoltaic power generation by 32.11 %, cuts grid power use by 2.21 %, improves system stability by 5.66 %, and maintains grid frequency within 0.03 Hz of the 50 Hz target. Battery state of charge increases by 51.65 %, extending lifespan. Compared to BSA, PSO achieves 3.33 % lower external grid reliance, 1.25 % better grid stability, and a 0.0546 higher fitness enhancement ratio, indicating superior convergence to optimal solutions. Dynamic performance analysis shows that PSO-optimized PI controllers reduce response time by 30.5 %, undershoot by 75.8 %, and steady-state error by 68.7 %, improving microgrid reliability. These advancements, driven by efficient utilization of HESS components, improve microgrid independence and resilience. The research highlights the critical role of PSO in optimizing resource utilization, contributing to sustainable energy systems for a greener future. • This research addresses the challenges of reliability, stability, and efficiency of MG. • MG integrates with PV, batteries, supercapacitors, and hydrogen-based fuel cells. • PI parameters are optimized for hybrid energy storage system's performance enhancement. • Obtained results show that the hybrid system outperforms the conventional systems. • The proposed approach substantially improves MG performance and energy sustainability.