Optimal sizing of smart hybrid renewable energy system using different optimization algorithms

Abstract

Hybrid renewable energy (HRE) sources have become a popular alternative in isolated and rural locations; because of the increased prices of fossil fuels and their CO2 emissions. This paper investigates the application of recent optimization methods to dedicate the optimal configuration of HRE sources. These HRE sources comprise photovoltaic (PV) panels, wind turbines (WT) with a battery storage system (BSS), and backup diesel generators (DGs). The cost function for the optimization problem has been chosen to be the cost of energy (COE) and the loss of power supply probability of the HRE system. The main function of the optimization algorithms is to minimize this cost function. However, the optimal configuration cannot be accomplished without fulfilling the system reliability and operational constraints. Salp swarm algorithms (SSA), the grey wolf optimizer (GWO), and the improved grey wolf optimizer (IGWO) were employed as optimization techniques. To assess the efficacy of these optimization techniques, they were applied using MATLAB simulation. A comparison of their outcomes was conducted in order to determine which technique was the most effective. Furthermore, a statistical study was carried out to determine the performance stability of each optimization strategy. The results revealed the optimal variables, including the number of PVs, WTs, battery banks (BBs) and the capacity of DGs. Over the course of a year, the optimized configuration was put to the test against a study of its capital and fuel expenses. IGWO showed the best performance among the three techniques, while the statistical results prove the robustness of the SSA algorithm.