Optimization study demonstrates superior cost reductions in standalone hydrogen-based microgrids via particle swarm optimization, highlighting practical designs for off-grid power.
Key Points
To determine the optimal component sizing and economic viability of an off-grid hybrid renewable energy system supported by hydrogen storage.
Formulated a techno-economic optimization model integrating photovoltaic modules, wind turbines, an electrolyzer, hydrogen storage tanks, and a fuel cell.
Implemented and evaluated three metaheuristic algorithms—Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing—to minimize Net Present Cost and Cost of Energy while maintaining reliability constraints.
All three metaheuristic techniques successfully converged on technically viable system configurations that met reliability criteria.
Particle Swarm Optimization achieved the lowest Net Present Cost and Cost of Energy compared to Genetic Algorithm and Simulated Annealing.