## 7 Conclusions  
This paper provides an overview of the challenges that must be addressed to implement V2G systems using AI solving methods. Some of the main functions modeled are discussed, including grid load control, economic cost optimization, and pollution generation minimization. Furthermore, the paper recognizes the necessity of accounting for uncertainties as well as the dynamic nature of the problem. Therefore, in order to provide realistic solutions, it is imperative to employ models capable of handling dynamism and uncertainties, such as those incorporating AI. These methods may include heuristics, machine learning approaches, or a combination of both such as those found in simineuristics and learnheuristics. Furthermore, the paper introduces a case study to highlight the potential that V2G systems could have in the future. Our focus was on solving a scheduling problem related to determining the optimal times for charging or discharging a vehicle. While the study offers a simplified version of the problem, results demonstrate that even the integration of a basic algorithm can significantly reduce the operational costs of the grid compared to taking no action. Specifically, the experiment shows that the cost of inaction is roughly 50% higher than when employing the algorithm, highlighting the importance of V2G systems in order to reduce cost. In addition, the case study also shows how the use of newer technologies, such as AI-based methods, can significantly reduce the computation time required for solving these kind of problems when compared to classic exact methods. For future research, we aim to expand upon this concept by developing a model that incorporates dynamism and uncertainty, which will enable us to better capture the complexities of V2G systems and provide more accurate predictions and solutions. Finally, the paper addresses some of the challenges encountered during model preparation. These include considerations such as accounting for charging speed and battery degradation, addressing safety concerns associated with AI algorithms, and ensuring public acceptance. Moreover, once V2G systems become widespread, data concerns such as privacy and security should be a priority. While V2G holds significant potential for the near future, there remains considerable groundwork to be performed to ensure its viability. Key challenges that need to be addressed include (i) investing in upgrading the power system infrastructure to accommodate the demands of V2G integration; (ii) improving lithium-ion batteries and developingmodels to better understand and mitigate their degradation over time; (iii) ensuring the scalability and interoperability of V2G systems to facilitate widespread adoption and seamless operation across different platforms and networks; (iv) addressing privacy and security concerns associated with the collection and utilization of sensitive data in V2G systems; (v) garnering public acceptance by finding a balance between optimizing the objective function of V2G systems and respecting the preferences and concerns of users; and (vi) developing more realistic models that not only address the challenges mentioned above but also align with the main goals of V2G implementations. Conceptualization, A.A.J. ; methodology, M.E., A.G., E.G. and A.A.J. ; investigation, M.E., A.G. and E.G. ; writing--original draft preparation, M.E., A.G. and E.G. ; writing--review and editing, A.A.J. All authors have read and agreed to the published version of the manuscript. This work has been partially funded by the Spanish Government with the European Union Next Generation funds (TSI-100408-2021-0), the Spanish Ministry of Science and Innovation (PID2022-138860 NB-I00, RED2022-134703-T), and the Horizon Europe program (HORIZON-CL4-2021-TWIN-TRANSITION-01-07 101057294 AIDEAS). Not applicable. All data required are included in the main text. [PERSON] was employed by the company baobab soluciones.