An Explainable Model Framework for Vehicle-to-Grid Available Aggregated Capacity Prediction

Vehicle-to-grid (V2G) technology has proven to be a promising solution for integrating electric vehicles (EVs) into the electricity grid, offering benefits such as grid stabilization and demand response. Predicting the aggregate available capacity (AAC) of EVs is crucial for effectively utilizing their energy storage capabilities. Here, a comprehensive methodological framework for predicting AAC in V2G systems is presented. It mainly includes data preprocessing and feature selection methods tailored to manage complex datasets with multiple data sources such as GPS, weather, vehicle characteristics, historical data, and calendar information. In addition, data augmentation methods are presented to address the problem of data scarcity that is typical of EV infrastructures. The core of such a framework then focuses on interpretable predictive models based on explainable machine learning or a state-space representation. The discussion on the framework under development aims to highlight the importance of interpretable models in V2G systems and provide insights into future research directions for such a prominent area, considering the evolution of the energy sector.