Quantum-inspired multi-modal multi-objective optimization for integrated electric and hydrogen mobility in renewable energy systems: Case study in Islamabad, Pakistan

This study develops an optimized hybrid renewable energy system (HRES) for Islamabad, Pakistan, that simultaneously addresses the integrated electrical demand of conventional loads, electric vehicle (EV) charging, and hydrogen refueling requirements for fuel cell electric vehicles (FCEVs). A novel multi-modal, multi-objective quantum-inspired particle swarm optimization (MMOQPSO) algorithm is employed to solve the tri-objective optimization problem, balancing the levelized cost of energy (LCOE) as the economic objective, carbon emissions as the environmental sustainability indicator, and loss of power supply probability (LPSP) as a measure of system reliability. The system architecture integrates photovoltaic arrays, hydrogen storage tanks, a biogas generator, and fuel cells to establish a resilient multi-energy supply framework. Through annual simulations using real-world meteorological and load profile data, the optimized configuration achieves a minimum LCOE of $0.2146/kWh, a 67% reduction in annual carbon dioxide (CO₂) emissions, and a sustainable decrease in LPSP to below 3%, ensuring high energy reliability. The hydrogen subsystem produces up to 6000 kg/year, with storage levels peaking at 120.02 kg, enabling consistent fuel cell dispatch. Additionally, the system attains a hydrogen environmental advantage to levelized cost of energy (HEA/LCOE) ratio above $6 per kg of CO₂ avoided, underlining its strong combined environmental and economic performance. Furthermore, the MMOQPSO algorithm exhibits faster convergence and enhanced solution diversity, enabling effective exploration of trade-offs. These findings underscore the system’s robustness against demand fluctuations and renewable intermittency, offering a practical pathway toward decarbonized, integrated energy infrastructure in developing urban contexts such as Islamabad. This work advances multi-objective optimization methodologies and their application in supporting the transition toward low-carbon transportation and integrated HRES.