Techno-economic and environmental optimization of hydrogen-based hybrid energy systems for remote off-grid australian communities

This study presents a techno-economic and environmental optimization of hydrogen-based hybrid energy systems (HESs) for Broken Hill City Council in New South Wales, Australia. Two configurations are evaluated: Configuration 1 includes solar PV, battery, fuel cell, electrolyzer, and hydrogen storage, while Configuration 2 includes solar PV, fuel cell, electrolyzer, and hydrogen storage but excludes the battery. The system is optimized using advanced metaheuristic algorithms, such as Harris Hawks Algorithm (HHA), Red-Tailed Hawk Algorithm, and Non-Dominated Sorting Genetic Algorithm-II, while ensuring real-time supply–demand balance and system stability through a robust energy management strategy. This integrated approach simultaneously determines the optimal sizes of PV arrays, battery storage (where applicable), fuel cells, electrolyzers, and hydrogen storage units, and maintains reliable energy supply. Results show that HHA Configuration 1 achieves the lowest net present cost of $338,111, a levelized cost of electricity of $0.185/kWh, and a levelized cost of hydrogen of $4.60/kg. Sensitivity analysis reveals that PV module and hydrogen storage costs significantly impact system economics, while improving fuel cell efficiency from 40% to 60% can reduce costs by up to 40%. Beyond cost-effectiveness, life cycle analysis demonstrates annual CO₂ emission reductions exceeding 500,000 kg compared to an equivalent diesel generator system meeting the same load demand. Socio-economic assessments further indicate that the HES can support improvements in the Human Development Index by enhancing access to healthcare, education, and economic opportunities, while also creating local jobs in PV installation, battery maintenance, and hydrogen infrastructure. These findings establish hydrogen-based HES as a scalable, cost-effective, and environmentally sustainable solution for energy access in remote areas.