This paper presents a multi-objective production–maintenance optimisation framework for a hybrid solar–wind–battery energy system in northern Nigeria. The approach integrates component reliability, system availability, and lifecycle cost into a unified decision-making model, solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Production sizing of photovoltaic (PV) panels, wind turbines, and battery storage units, together with preventive maintenance scheduling, is jointly optimised over a 60-month planning horizon. The optimisation problem is formulated as a tri-objective mixed-integer nonlinear program (MINLP), and NSGA-II is employed to generate the full Pareto front in a single run. Three representative Pareto-optimal configurations are reported, demonstrating clear cost–reliability trade-offs: the lowest-cost solution achieved 87.83% reliability at €25.72 million; a balanced configuration improved reliability to 90.21% at €27.68 million; and the highest-reliability solution reached 96.12% at €30.92 million. The optimised maintenance strategies consistently identified the rotor shaft and gearbox as critical components requiring frequent intervention, while higher-reliability scenarios demanded increased storage capacity and more intensive preventive maintenance. The proposed framework offers a practical decision-support tool for designing hybrid renewable energy systems where cost, reliability, and availability must be considered simultaneously.

Keywords

Energy optimisation, Hybrid renewable energy systems, Maintenance scheduling, Power generation and storage, Reliability and maintenance.