The paper "Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods" by Marc Escoto, Antoni Guerrero, Elnaz Ghorbani, and Angel A. Juan addresses the significant challenges in optimizing V2G systems, which enable bidirectional energy flow between electric vehicles (EVs) and the grid. The authors highlight the dynamic nature of energy demand, grid constraints, and user preferences as key obstacles. They explore the potential of artificial intelligence (AI) methods, including machine learning, metaheuristics, and agile optimization, to overcome these challenges. The paper provides a comprehensive analysis of existing research, identifies gaps, and demonstrates the efficacy of AI-driven algorithms through case studies. These algorithms improve grid stability, reduce costs, and enhance user satisfaction. The authors also discuss the integration of AI-driven methods into V2G systems, emphasizing the need for further research and addressing environmental, socio-economic, and policy implications. The main contributions include novel AI-based methodologies for solving V2G optimization problems, such as simheuristics, learnheuristics, and agile-optimization heuristics. The paper concludes with a discussion on future directions, highlighting the potential for intelligent algorithms to enhance V2G systems.The paper "Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods" by Marc Escoto, Antoni Guerrero, Elnaz Ghorbani, and Angel A. Juan addresses the significant challenges in optimizing V2G systems, which enable bidirectional energy flow between electric vehicles (EVs) and the grid. The authors highlight the dynamic nature of energy demand, grid constraints, and user preferences as key obstacles. They explore the potential of artificial intelligence (AI) methods, including machine learning, metaheuristics, and agile optimization, to overcome these challenges. The paper provides a comprehensive analysis of existing research, identifies gaps, and demonstrates the efficacy of AI-driven algorithms through case studies. These algorithms improve grid stability, reduce costs, and enhance user satisfaction. The authors also discuss the integration of AI-driven methods into V2G systems, emphasizing the need for further research and addressing environmental, socio-economic, and policy implications. The main contributions include novel AI-based methodologies for solving V2G optimization problems, such as simheuristics, learnheuristics, and agile-optimization heuristics. The paper concludes with a discussion on future directions, highlighting the potential for intelligent algorithms to enhance V2G systems.