EV Fleet Flexibility Estimation and Forecasting for V2X Applications

TL;DR

This paper introduces a new aggregate polytope-based method for estimating and forecasting the flexibility potential of EV fleets in V2X systems, supporting sustainable energy integration.

Contribution

It presents a novel, robust approach for V2X flexibility estimation that accounts for uncertainties and can be integrated into various optimization problems.

Findings

Effective in capturing V2X flexibility potential

Robust against individual EV owner uncertainties

Applicable to diverse V2X applications

Abstract

Forecasting the flexibility potential of Vehicle-to-Everything (V2X) systems is important for the future of energy networks, where the integration of renewable energy sources and electric vehicles poses significant challenges. In this paper, we present a novel method for estimating and predicting V2X flexibility potential of an EV fleet, based on an aggregate polytope representation, addressing the need for accurate and reliable forecasting methods in the realm of sustainable transportation. The method is robust against individual uncertainties of EV owners behaviours as it is applied at an aggregate level, and the reformulation of the V2X potential as a set of linear constraints allows the proposed method to be integrated into different optimisation problems and therefore be applied for diverse V2X applications. Case studies showcase the capability of the method in capturing the V2X flexibility potential and demonstrate it effectiveness for different V2X applications.