## 6 Challenges and Future Directions
### Data Challenges and Considerations  
One of the primary challenges in integrating AI-driven methods is derived from data and their collection. For instance, in a V2G system formed by millions of vehicles, with each of them updating its status frequently to enable the algorithm to compute the optimal solution at each time point, the volume of data required would be immense. This poses significant problems not only in terms of storage capacity but also in processing power to analyze the data within a reasonable time frame. Given the importance of obtaining solutions to the problem in real time, a good combination of both hardware and software is needed to tackle the problem. Although the hardware component is important, a well-designed algorithm ensures that the hardware infrastructure is utilized optimally, enabling timely decision-making and action. Moreover, one should consider the safety and robustness of the model. Most of them are only focused on maximizing or minimizing some objective function, without considering the physical constraints regarding the stability properties, such as voltage regulations. Therefore, investing in research and development is essential to create advanced control algorithms and technologies that are capable of managing the complexity of V2G integration.  
Furthermore, as highlighted in [44], another set of challenges arising from the substantial data requirements includes identifying suitable data sources for algorithm development and evaluation. In order to develop algorithms suitable for real-life use, the need of high-quantity real-world data arises. However, it is possible that certain algorithms, such as those employing reinforcement learning, might require more data than what are publicly available for their training phase [49], potentially impacting their accuracy. Therefore, in numerous cases, it becomes necessary to generate sample data, which could lead to problems regarding the scalability and representativeness of the model. Determining appropriate methods for evaluating and verifying large-scale V2G schemes and systems is also crucial, and the algorithms that have been developed should be subjected to testing in simulations or real-world scenarios to validate their functionality and evaluate their potential economic benefits across a wide range of settings, which includes diverse socio-economic contexts within cities or countries. For instance, [PERSON] et al. [50] developed a model covering 28 European countries, capable of simulating the charging profiles of individual EVs based on their mobility patterns.  
Moreover, a related challenge in this domain is ensuring the security and privacy of individuals utilizing the V2G grid. Since the data contain sensitive information about where each person charges his/her vehicle and for how long, there exists the potential to find people's mobility patterns if a third party or a malicious user gains access to the data system. Therefore, regulating data collection and its access by third parties, as well as having a reasonable policy that is also socially accepted, could be an important milestone for establishing a V2G grid system. For instance, in an effort to mitigate some of the privacy issues from the algorithmic standpoint, [PERSON] et al. [51] propose a multi-agent reinforcement learning algorithm. In this approach, each agent undergoes centralized training, yet they make decisions autonomously at the local level. This setup enables the maintenance of both autonomy and privacy, as individual agents retain control over their decision-making processes while benefiting from collective learning through centralized training. However, in actuality, V2G remains a niche, and only a limited number of users can currently benefit from it. As a result, the challenges posed by big data and their potential solutions are still largely unexplored.