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Techno-economic optimization of a standalone solar/wind/battery hybrid system located in Xining, China, is the focus of this paper, and reliable and economic indicators are simultaneously employed to address the problem. To obtain a more precise Pareto set, a novel multi-objective differential evolution algorithm is proposed, where differential evolution with a parameter-adaptive mechanism is applied in the decomposition framework. The algorithm effectiveness is verified by performance comparisons on the benchmark test problems with two reference algorithms: a non-dominated sorting genetic algorithm and a multi-objective evolution algorithm based on decomposition. The applicability of the proposed algorithm for the capacity-optimization problem is also validated by comparisons with the same reference algorithms above, where the true Pareto set of the problem is approximated by combining of the three algorithms through the non-dominant relationship. The results show the proposed algorithm has the lowest inverted generational distance indicator and provides 85% of the true Pareto set. Analyses of the Pareto frontier show that it can produce significant economic benefits by reducing reliability requirements appropriately when loss of power supply probability is less than 0.5%. Furthermore, sensitivity analyses of the initial capital of wind turbine, photovoltaic panel and battery system are performed, and the results show that photovoltaic panel’s initial capital has the greatest impact on levelized cost of electricity, while the initial capital of wind turbine has the least impact.

Techno-Economic Optimization of an Off-Grid Solar/Wind/Battery Hybrid System with a Novel Multi-Objective Differential Evolution Algorithm