Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm

Abstract Due to increasing energy demand and fossil fuel costs in island and remote areas, renewable energy resources are becoming increasingly attractive. The hybridization of these resources can help overcome their variability and intermittency and improve efficiency. Many independent hybrid renewable energy systems are used in remote and island areas for which weather data often is unavailable. To increase the accuracy of size optimization of such systems, more accurate weather data is needed and the use of weather forecasting data is helpful. In this article, a new hybrid optimization algorithm is proposed for the optimal sizing of a stand-alone hybrid solar and wind energy system based on three algorithms: chaotic search, harmony search and simulated annealing. To improve the accuracy of the size optimization algorithm results, weather forecasting is used along with artificial neural networks for solar radiation, ambient temperature, and wind speed forecasting. The main objective function of minimizing the total life cycle cost is used to assess the feasibility of the hybrid renewable energy system accounting for system reliability. The reliability of the system is assessed by the loss of power supply probability parameter. The new method is tested for the electrical load of the city of Khorasan, Iran. The results are compared with those obtained by the proposed algorithm (harmony search and simulated annealing-based artificial neural networks). The simulation results demonstrate the advantages of utilizing the hybrid optimization algorithm with weather forecasting data for a stand-alone hybrid renewable energy system.