A Novel Sizing Methodology Based on Match Evaluation Method for. Optimal Sizing of Stand-Alone Hybrid Energy Systems Using NSGA-II

Wind and solar energy technologies offer clean and renewable energy sources, and are essential components of sustainable energy future. The current paper presents a new methodology for size optimization of stand-alone Wind/PV energy systems. The principal objective of this study is to maximize the electricity match rate between demand and supplies, able to accomplish the energy requirements of a given load distribution, for a specific site. Mathematical models of hybrid components are exploited to estimate the total output power using solar radiation, temperature and wind speed data, collected on the site of Zabol, located in Sistan and Baluchestan, Iran. Then, a new methodology has been proposed which can obtain the optimal size of each hybrid component. The objective functions are considered based on supply/demand match evaluation criteria, minimizing the inequality coefficient (IC) and also maximizing the correlation coefficient (CC), simultaneously. The elitist non-dominated sorting genetic algorithm (NSGA-II), which is one of the multi-objective evolutionary algorithm (MOEA), have been employed for a hybrid stand-alone renewable energy power system (Wind/PV) to find the optimal size of each components. The Pareto-optimal solution is obtained in order to have higher-level decision. The designers can select the best configuration among the Pareto set which fits their desire.

Hybrid Wind/PV systems; multi-objective optimization; sizing method; electricity match rate