Multi-Objective Hybrid Optimization for Optimal Sizing of a Hybrid Renewable Power System for Home Applications

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Automated summary

This study proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid microgrid system. The method combines Non-dominant Sorting Genetic Algorithm II and Grey Wolf Optimizer to minimize the total energy cost and loss of power supply probability. Comparative analysis shows that the proposed method outperforms other optimization algorithms in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and higher standard deviation. The analysis also reveals that relaxing the loss of power supply probability can lead to an additional cost reduction.