Multi-objective optimal configuration of off-grid residential hybrid renewable energy system based on hypervolume-improved Non-dominated Sorting Genetic Algorithm III

The optimal configuration of a hybrid renewable energy system (HRES) is a multi-objective, multi-constraint, nonlinear, high-dimensional complex problem. In this work, the detailed system model and optimization objectives of a novel HRES are established. By integrating the Hypervolume method and the algorithmic framework, an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) method is proposed, addressing the tendency to degrade the Pareto front through random selection. Finally, the system optimization configurations and operations are analyzed and evaluated. The results indicate that the hydrogen subsystem significantly enhances system reliability and environmental sustainability. The proposed algorithm achieves the highest Pareto front superiority index (SI) value of 82.19%, demonstrating excellent robustness and convergence speed. Analysis of HRES operations reveals a negative correlation between battery capacity and decay, with fuel cell decay minimizing at 1.66% for a 3.5 kW capacity.