## 1 Introduction  
As CO\({}_{2}\) emissions increasingly contribute to environmental pollution and the climate crisis, renewable energy sources (RES) are being rapidly adopted, particularly in developed and developing countries. In particular, the rate of use of wind and solar energy for electricity generation is increasing faster than other renewable energy sources [1, 2]. However, wind and solar energy availability fluctuate throughout the day due to varying atmospheric conditions [3]. In addition, due to unforeseen meteorological conditions, the continuity of energy production in power generation facilities based on these resources cannot be ensured [4]. Therefore, to ensure the continuity of energy in electricity networks, either fossil-based power plants or energy storage systems are required [5, 6]. Due to the negative environmental effects of fossil-based resources, energy storage systems (ESS) are becoming more prominent today. ESS used with RES not only ensures the continuity of electrical energy but also prevents voltage and frequency fluctuations caused by power plants based on RES. Since ESS has become competitive in terms of cost, both academic studies and industrial studies are carrying out studies to integrate ESS into power plants [7,8]. In many studies, it has been shown that ESSs eliminate the instability problems caused by the integration of RESs into the electrical grid to a large extent [9]. The ability of ESSs to inject and absorb active or reactive power makes RES very useful for integrating into a power system network [10,11]. [PERSON] and [PERSON] (2023) [12], the authors presented a strategy to optimally determine the size and discharge time of BSSs in a distribution system (DS) to improve the performance and reliability of ESSs. For this purpose, the weight-aggregated particle swarm optimization (WAPSO) technique was used. [PERSON] et al. analyzed the effect of operating at different voltage levels on the system in battery PV systems based on the self-consumption model [13]. Thus, they determined the effect of operating voltage level and demanded electrical power on the energy efficiency of the system. [PERSON] et al. created a method and economic model based on actual regulation to evaluate the use of ESS in a power grid for different scenarios. The proposed method reveals potential barriers, challenges, and the feasibility of incorporating energy storage into RES [14]. Optimum sizing of ESS is necessary to meet load demand, reduce energy costs, and increase system security [15]. While facilitating the optimum sizing of the ESS, it is necessary to pay attention to many factors such as the optimum charge/discharge cycle, sudden load changes, power oscillations, the amount of carbon emission prevention, and interruption of transmission/distribution systems [16]. Therefore, many researchers have developed different optimization algorithms to find the best possible result from the traditional ESS system considering low cost, high lifetime, reliability, and lower environmental impact. Energy storage technologies are classified into five categories: mechanical, electromechanical, electrical, chemical, and thermal energy storage technologies. In this study, batteries, which are chemical storage technology, are discussed [17]. The oldest chemical energy storage technology is lead-acid battery technology. The lifespan and efficiency of lead-acid batteries are moderate compared to other batteries. It also has a low energy-to-weight ratio and energy-to-volume ratio. The most important advantage is that it has a high power-to-weight ratio, so it can provide a high ripple current [18]. The most widely used chemical energy storage technology is lithium-ion battery technology. There are many different types of lithium-ion batteries, such as lithium cobalt oxide (LiCoO2), lithium iron phosphate (LiFePO4), lithium-ion manganese oxide batteries (Li2 MnO4, Li2 MnO3, LMO), and lithium nickel manganese cobalt oxide (LiNiMnCoO2).