Optimization of hybrid renewable energy systems: Reliability, cost, and environmental trade-offs using PSO and GJO algorithms

Hybrid Renewable Energy Systems (HRES) provide a sustainable and reliable solution for electrification in remote regions while reducing dependence on fossil fuels and minimizing environmental impact. This study develops an integrated optimization framework for designing an HRES composed of photovoltaic generation, diesel backup, and hydrogen storage components. The framework employs the emerging Golden Jackal Optimization (GJO) algorithm and compares its performance with the established Particle Swarm Optimization (PSO) method used as a benchmark. The optimization minimizes the Total Net Present Cost (TNPC) while satisfying reliability and renewable energy portion (REP) constraints. Results show that GJO achieves slightly lower TNPC and improved convergence compared to PSO, demonstrating higher efficiency and robustness. Increasing REP from 0.1 to 0.9 enhances system sustainability by significantly reducing CO2 emissions, although it raises TNPC by 39 %. The proposed framework provides a practical and scalable approach for cost-reliable design of hydrogen-integrated hybrid energy systems supporting Jordan's renewable energy goals.