A Probabilistic Multilayer Framework for Electric Vehicle User Behavior in Urban Areas

The rapid growth in Electric Vehicle (EV) adoption presents both opportunities and challenges for modern power grids, especially in urban areas where users can act as prosumers—consuming and supplying energy. A key challenge in assessing grid reliability lies in understanding and modeling the complexity of EV user behavior, which is often oversimplified or treated in isolation in existing approaches. This study introduces a multilayer probabilistic framework that explicitly models EV user behavior through interconnected random variables, enabling the joint representation of parking, charging, traveling, and energy exchange decisions. The framework adopts a Bayesian hierarchical structure to manage uncertainties and correlations across behavioral layers, providing a robust and explainable basis for scenario analysis. Simulations based on a real urban environment demonstrate the methodology’s ability to capture diverse behavioral patterns, perform sensitivity analyses, and assess the impact of individual user decisions on the performance of the EV charging infrastructure. Key findings highlight that optimal system outcomes often emerge from intermediate, not extreme, behavior probabilities—underscoring the importance of nuanced behavioral modeling. A primary limitation of the current work is its reliance on synthetic behavior profiles and the exclusion of external environmental variables such as weather or renewable energy availability. Addressing these aspects in future studies will enhance the framework’s realism and applicability in real-world planning.