Energy management and capacity planning of photovoltaic-wind-biomass energy system considering hydrogen-battery storage

This article proposed a Salp Swarm nature-inspired metaheuristic optimization algorithm (SSA) for the energy management and capacity planning of a standalone hybrid photovoltaic wind-biomass-hydrogen-battery energy system. The SSA is used to determine the optimum system configuration that will fulfill the demand reliably considering technical (loss of power supply probability (LPSP)) and economical (annualized system cost (ASC)) aspects. The energy management system (EMS) of the energy system is implemented using a rule-based algorithm to effectively manage the power flow of the devised hybrid energy system components. The comparative evaluation of the algorithms shows that EMS-SSA produces a better result as it offers the least levelized cost of energy (LCOE), of $0.939737/kW h, as compared to the EMS-LFA, EMS-GA and HOMER, which offer LCOE of $0.949737/kW h, $0.958660/kW h and $1.075351/kW h, respectively. Similarly, for the optimal system configuration, the annualized system cost (ASC) is found to be 1.887995 M$. This research presents a viable and environmentally sustainable electrification solution, serving as a valuable reference for making electricity investments in the energy-deficient Northeastern part of Nigeria.