An intelligent energy management strategy for standalone PV–fuel cell–battery system using elk herd optimization approach

• EHOA-based EMS for standalone PV–Fuel Cell–Battery systems optimize power distribution and reduces hydrogen consumption. • Dynamically allocates power, ensuring stable DC bus voltage and fast response under varying conditions. • Limits battery depletion/overcharge, enhancing fuel cell and battery lifespan and system robustness. • Comparative analyses show the EMS outperforms PSO, PI, ECMA, and EEMS in efficiency, adaptability, and scalability. • Simulation results show 89.91% efficiency, 61.77% SOC, and competitive hydrogen use, confirming superior performance. This research paper presents an enhanced power management strategy (PMS) for an off-grid hybrid power system combining photovoltaic sources, fuel cells, and batteries using Elk Herd Optimization (EHO). The hybrid system utilizes photovoltaic as the primary clean energy source, with fuel cells as supplementary units for maintaining power during low solar availability. The battery storage serves as the main energy reservoir, managing power fluctuations, meeting peak loads, and reducing stress on the fuel cells across varying climates and operational conditions. The proposed power management system is crucial for the efficient allocation of power among various energy sources, which significantly influences the overall efficiency of the system. Its primary objectives include minimizing hydrogen consumption, enhancing energy efficiency, and prolonging the lifespan of fuel cells and batteries. The EMS utilizes an EHO-based PMS that dynamically optimizes power distribution among the photovoltaic (PV) array, fuel cell, and battery to meet load demands while adhering to system constraints. The investigation evaluates the effectiveness and robustness of a proposed energy management system based on EHO, comparing it with existing techniques such as particle swarm optimization (PSO), traditional rule-based methods like PI controllers, and two established approaches—ECMA and EEMS. The main goal is to reduce hydrogen use and improve the utilization of energy to improve daily operations. Simulation results indicate that the EHO-based PMS significantly decreases hydrogen consumption while maintaining or enhancing the longevity of the energy source and overall system efficiency compared to current methodologies.