Leveraging machine learning for efficient EV integration as mobile battery energy storage systems: Exploring strategic frameworks and incentives

The emergence of electric vehicles is reshaping the energy landscape, requiring the development of innovative energy integration mechanisms to engage prosumers. However, current methods face numerous challenges when actively involving communities. Some key challenges include system uncertainties and time issues, optimizing charging strategies, real-time decision-making needs, privacy concerns, and battery degradation. For instance, the unpredictability of driver behavior and traffic conditions introduces complexities in devising efficient energy integration strategies and economic incentive models. The intricate interplay of these factors necessitates advanced computational techniques, making machine learning an invaluable tool. This paper concisely reviews prominent machine learning algorithms, encompassing supervised and unsupervised learning, focusing on their distinctive capabilities in prediction, clustering, dimensionality reduction, and generative modeling. Additionally, it explores reinforcement learning, emphasizing its aptitude for real-time decision-making. The focus of the study lies in the application of advanced algorithms, specifically examining their effectiveness in various strategic operational frameworks. The aim is to integrate electric vehicles into power systems efficiently. These frameworks include bargaining, contracts, auctions, game theory, and economic incentives such as pricing and cost-profit optimization. Each application includes a concise overview of the general methodology and investigates in-depth discussions regarding the suitability and challenges of deploying machine learning techniques. This paper will guide industry professionals in implementing solutions for electric vehicle dispatching problems and provide valuable insights to academics for further research and development. •Various economic incentives and strategies for EV energy integration are presented.•Machine learning methods for EV energy integration are discussed.•The challenges of implementing these methods and possible remedies are explored.•Real-world projects and their technical intricacies are examined, and areas for future academic study are recommended.