Optimal Design of Remote Green Hydrogen-Based Power-to-Power Microgrids Using Starfish and Artificial Hummingbird Algorithms

This study centers on the optimal design and performance assessment of a green hydrogen power-to-power system (GHP2P) tailored for off-grid applications. The proposed system combines photovoltaic (PV) panels, wind turbines (WT), and a fuel cell (FC) integrated with a hydrogen storage tank, facilitating efficient energy storage in chemical form. The optimal sizing of the system components is achieved using two advanced metaheuristic optimization methods: the Starfish Optimization Algorithm (SFOA) and the Artificial Hummingbird Algorithm (AHA). The optimization process tackles a Mult-objective problem, focusing on minimizing the cost of energy (COE), improving system reliability by reducing the loss of power supply probability (LPSP), and minimizing excess energy, all while adhering to critical operational constraints. To verify the precision, stability, and robustness of the proposed optimization approach, the algorithms are evaluated on three distinct off-grid GHP2P configurations. Simulation results consistently highlight the superior performance of the AHA algorithm, which attains the lowest fitness function value with reduced computational time compared to SFOA. Additionally, extensive statistical analyses validate the efficiency and effectiveness of the proposed methodology. Based on the simulation outcomes, the AHA algorithm is the most competitive solution, demonstrating its potential to address complex engineering optimization challenges and establishing itself as a promising alternative to state-of-the-art optimization techniques.