Enhancing off-grid hybrid renewable energy systems through multi-objective optimization with Pareto front analysis

This paper presents a comprehensive multi-objective optimization framework for designing PV–Wind–Diesel–Battery hybrid energy systems, targeting off-grid and reliability-critical applications. The analysis simultaneously considers three key objectives: cost of energy (COE), loss of power supply probability (LPSP), and the Excess Energy Penalty (Pdummy). Many system configurations are evaluated using simulation-based optimization, and the resulting solution space is explored through Pareto front analysis and principal component analysis (PCA). The MGWO optimization algorithm has been applied. Moreover, the results are further validated through comparison with the MPSO and NSGA-II algorithms. The results reveal strong trade-offs between COE, LPSP, and Pdummy, with a well-defined knee point at COE = 0.178 $/kWh and LPSP = 1.78%, offering a balanced design compromise. PCA confirms that the first two principal components explain 74.99% of system variance, with PV and WT capacities contributing most significantly to PC1. The proposed method enables informed design decisions by revealing hidden relationships and supporting data-driven system planning. A representative knee-point solution is identified and validated, offering a benchmark for efficient and resilient hybrid energy deployment.