An efficient sizing method with suitable energy management strategy for hybrid renewable energy systems

This paper propose a new method for designing a stand-alone hybrid wind-photovoltaic-diesel-battery system that minimizes the inequality coefficient and annualized cost of system and maximizes the correlation coefficient using multi-objective particle swarm optimization algorithm. The proposed method uses data from solar radiation, temperature, and wind speed that are collected from the city of Zabol, located in south-east of Iran. The results are presented as an optimal Pareto front set and the optimal number of devices, as well as objective functions, that is, inequality coefficient, annualized cost of system, and correlation coefficient. Additionally, a study of the operating hours of diesel generator in optimal configuration is carried out. Simulation results show the match rate between demand, supply, and energy storage. The optimal number of wind turbines, photovoltaic modules, and batteries ensuring that the system total cost is minimized, while guaranteeing a highly reliable source of load power is obtained. The proposed sizing method can be applied to any other locations with different weather data, load demands, and different characteristics

large-scale optimization;hybrid power systems;wind-PV-power systems;multi-objective optimization;sizing method;electricity match rate (EMR)