Capacity Prediction and Modeling of Virtual Energy Storage System for Electric Vehicles

To optimize the dispatch of Electric Vehicle Virtual Energy Storage (EVVES) across wide-ranging networks, this research presents a highly precise virtual energy storage capacity estimation model. By clustering the types of EVs participating in EVVES service, and taking into account the usage habits of various EV owners (e.g. daily mileage, charging/discharging pattern, expected spare travel power, etc.), the influence of the market on the responsiveness of EVVES, and the design of the available energy storage capacity redundancy, etc., we will comprehensively establish a virtual energy storage capacity estimation model for scheduling. Seamlessly aligned with the requirements of the power system, the model formulates tailored EVVES supply-demand matching strategies for various remunerated auxiliary services, including emergency power augmentation, peak-valley balancing, and seamless integration of renewable energy outputs. Employing Monte Carlo simulation, the model demonstrates exceptional accuracy in real-time estimation of the available capacity for VES services across distinct timeframes, thereby establishing a robust framework for the successful operationalization and subsequent scaling of EVVES initiatives. This work lays a cornerstone for advancing the practical application of EVVES in contemporary energy systems.