Energy Management and Improved Metaheuristic Optimization-Based Control of Photovoltaic/Hybrid Energy Storage System-Based Microgrid

As global energy demand escalates and fossil fuel reserves dwindle, the associated rise in greenhouse gas emissions and environmental concerns becomes increasingly urgent. This research addresses these challenges by focusing on the integration of photovoltaic (PV) systems with energy storage systems, particularly hybrid energy storage systems (HESS), to enhance sustainability and environmental friendliness. Despite the advantages of PV systems, their power generation is significantly influenced by weather and diurnal variations. This study introduces a novel control mechanism designed to bolster the stability of PV-HESS microgrids. The proposed microgrid utilizes both a lead-acid battery and a supercapacitor as part of the HESS, aiming at storing energy from PV system and balancing the variance between load power demand and the generated PV power. The microgrid's direct current bus voltage is meticulously regulated using a proportional-integral-based controller. This controller is optimized using three cutting-edge metaheuristic algorithms: particle swarm optimization, artificial ecosystem-based optimization (AEO), and wild horse optimizer. Additionally, an energy management strategy is proposed to protect microgrid components, efficiently distribute energy between the battery and supercapacitor, and manage the connection of microgrid components. Simulation results demonstrate the robustness of the proposed approach under various operating conditions. The AEO-based controller achieves lower discharging rate and both lower rise time and settling time compared to the other two controllers, highlighting a notable increase in energy preservation by 0.15% and higher response time advantages to the AEO-based controller.