## 1 Introduction  
Despite the steady increase in electric power production, it is still below the required level, due to the increase in load demand caused by the population increase as well as the increased use of technology in the residential, industrial and agricultural fields. According to the International Energy Agency (IEA), the global electricity demand will grow at an annual rate of 2.1% until 2040. This increases electricity's share in the total energy consumption to 24% in 2040. It is expected that renewable energy sources (RES) will face a significant increase in global investment in the coming years, to cover more than half of the energy consumption in the world by 2040. These energies will make up for the shortfall in electrical energy production and contribute to a reduction in carbon dioxide emissions in the atmosphere, thereby reducing pollution significantly [1; 2; 3]. In order to invest in RES to optimize electrical energy production and raise the efficiency of the systems, many studies in the world recommend combining different technologies to form hybrid renewable energy systems (HRES) [4; 5]. Consequently, these sources complement each other, support the national grid, and reduce the use of traditional power plants depending on fossil fuels that release greenhouse gases and pollute the environment [6]. However, the design of these hybrid systems needs sophisticated programs and smart algorithms capable of reaching the optimal solution taking into consideration all the conditions and constraints such as reliability aspects, economic cost, sensitivity factors, availability of RES, etc. [1; 2; 7; 8]. Several studies have been conducted on the technical and economic feasibility of hybrid systems in past years to determine their viability. Many of these studies have used different modeling of HRES, and they have applied different algorithms and various software tools to achieve their goals. According to the literature, these challenges still exist and are the focus of a lot of research, especially on finding the best algorithms and modern techniques in reaching the optimal solutions of the optimization problem of finding the optimal sizing of the installed capacities of the components of HRES [9; 10; 11; 12; 13; 14; 15]. In [16], the pre-feasibility analysis of a stand-alone energy system using HRES including renewable and conventional energy sources was applied using HOMER software in Newfoundland, Canada. In one of the earlier studies [17], the authors conducted a feasibility study of generating electricity using RES for a hybrid system in a stand-alone village in Chhattisgarh, India. In [18], the authors introduce a realistic solution for energy demand from a hybrid power system consists of wind turbines (WT), photovoltaics (PV), and battery energy storage systems (BESS). Through a real measurement of meteorological data in 2017, concerning especially the wind speed, solar radiation and temperature, the output power of the proposed hybrid system is calculated. Load satisfaction is considered to evaluate the feasibility of the system. The optimum solution is found using the linear TORSCHE optimization technique, while a comparative study between PV/WT/battery and PV/WT has been accomplished and an economic analysis was presented. As a result, the hybrid PV/WT/battery is proved more economical than using each system individually. [PERSON] et al. [19] designed and investigated a hybrid PV/WT/hydropower/pump storage as a case of study. The optimal configuration of the HRES is found using a techno-economic index that respects the maximum Loss of Power Supply Probability (LPSP) and minimum investment cost. The Multi-Objective Particle Swarm Optimization (MOPSO) is used to trade off analysis between two objectives. Besides, the curtailment rate (CR) of the WT and PV are taken into consideration due to policy requirements. The authors in [20] proposed an optimized design of an energy system featuring the highest penetration of renewable energy.