Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage

This manuscript focuses on optimizing a Hybrid Renewable Energy System (HRES) that integrates photovoltaic (PV) panels, wind turbines (WT), and various energy storage systems (ESS), including batteries, supercapacitors (SCs), and hydrogen storage. The system uses a multi-objective optimization strategy to balance power management, aiming to minimize costs and reduce the likelihood of loss of power supply probability (LPSP). Seven different algorithms are assessed to identify the most efficient one for achieving these objectives, with the goal of selecting the algorithm that best balances cost efficiency and system performance. The system is assessed across three operational scenarios: (1) when energy supply meets demand with help from backup systems, (2) when demand exceeds supply and energy storage systems are depleted, and (3) when energy generation surpasses demand and storage systems are full. The HBA-based optimization effectively manages energy flow and storage, ensuring grid stability and minimizing overcharging risks. This system offers a reliable and sustainable power supply for isolated microgrids, effectively managing energy production, storage, and distribution. The research sets a new benchmark for future studies in decentralized energy systems, particularly in balancing technical efficiency and economic feasibility.