Multi-objective optimization of hybrid renewable energy systems with green hydrogen integration and hybrid storage strategies

Abstract

This study proposes a hybrid renewable energy system (HRES) that integrates photovoltaic panels (PVs), wind turbines (WTs), and continuous green hydrogen production via reformers. To enhance system reliability and efficiency, hybrid storage solutions, including hydrogen tanks and batteries, are incorporated. An integrated size and design optimization algorithm (i-NSGA-II), based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is proposed, combining the system design optimization with an advanced power management strategy (PMS). The proposed approach is validated through comparison with multi-objective particle swarm optimization (MOPSO). In the hydrogen storage scenario, the minimum annualized cost of system (ACS) solutions achieved a 10% reduction in ACS, the minimum loss of power supply probability (LPSP) solutions showed a 78% decrease in LPSP, and the minimum percentage of energy waste (PEW) solutions resulted in an 83% reduction in PEW compared to MOPSO. In the hybrid storage scenario, the results showed a 9% reduction in ACS and an 85% decrease in LPSP for the corresponding objective functions compared to MOPSO. For both minimum PEW solutions in the hybrid storage scenario, the PEW value was 0%, achieving 100% system efficiency. Simulations conducted in three regions demonstrated the system's adaptability to varying climatic and regional conditions. A comprehensive sensitivity analysis reveals the impacts of cost variations, storage types, and capacities on system performance, providing insights into the trade-offs involved in optimizing HRES.