## 3 Related Work  
Among the various approaches to implementing and optimizing V2G systems, evolutionary algorithms are the most widely utilized. These algorithms provide robust, near-optimal solutions for these problems [15]. However, alternative strategies such as heuristics and other metaheuristic approaches have been explored in the literature. The exploration of exact methods to tackle these problems has been studied as well. These methods employ precise algorithms to achieve optimal solutions for V2G systems. [PERSON] and [PERSON] [16] proposed an optimal scheduling algorithm for a V2G framework, which schedules energy sales and ancillary services. This problem is formulated as a convex linear multi-objective problem and relies on real data from the Houston, Texas area. Simulations are conducted hourly over a three-month period, considering a group of 10,000 EVs, to determine the hourly optimal schedule. The simulation results show substantial financial advantages for both customers and aggregators for different battery replacement costs. Another approach is taken in the paper presented by [PERSON] et al. [17], where a decentralized charging algorithm for EVs is developed. Initially, the problem is optimally solved using linear programming techniques. However, due to excessive computing time requirements, a decentralized charging algorithm for load shifting is formulated. This tactic emulates the charging pattern identified through linear programming optimization solutions. The problem involves two objectives: minimizing a cost function comprising electricity generation costs and total carbon dioxide emissions, while simultaneously maximizing frequency regulation to minimize the grid's impact. Simulation studies show that the proposed algorithm not only minimizes electricity generation costs and CO\({}_{2}\) emissions but also effectively reduces dependence on conventional regulation power plants while maintaining optimal battery charging performance. Both papers indicate that exact methods offer optimal solutions for these problems. However, the significant computational time needed for these solutions results in the adoption of such methods being impractical for real-life applications, where time constraints are often limited. Moreover, these methods especially struggle against additional constraints resulting from uncertainty and dynamism, which can further increase the complexity of these problems or significantly worsen the solutions provided. Due to this reason, when addressing more complex problems, the use of more agile methods is required. Evolutionary algorithms provide near-optimal solutions in a shorter computing time compared to exact methods. Additionally, because of their evolutionary component, these methods do not suffer from convergence problems. Different evolutionary algorithms have been utilized to tackle these problems. In the paper presented by [PERSON] et al. [18], a multi-layer framework is introduced for V2G operations, where bidirectional energy transmission is considered. A particle swarm optimization (PSO) approach is employed to optimize both cost and emissions. Simulation experiments were conducted to test the variations in cost and emissions when each factor or both of them were considered in the fitness function, showcasing the significance of integrating both considerations and demonstrating that the proposed grid was feasible and effective in its purpose. Another method is utilized by [PERSON] et al. [19], who introduce a Genetic Algorithm (GA) to coordinate a V2G system. In this problem, electricity is generated in two ways: firstly, through a wind turbine, which provides non-constant and intermittent energy, and secondly through a conventional method utilized when the electricity provided by the wind turbine is insufficient to meet the demand. The objective of the problem is to minimize the conventional generation required.