Efficient Quantification and Representation of Aggregate Flexibility in Electric Vehicles

TL;DR

This paper introduces a scalable, efficient method to quantify and represent the aggregate flexibility of electric vehicle fleets within fixed time windows, enabling better integration into power networks.

Contribution

It proposes a novel, scalable representation of EV flexibility that is independent of fleet size and captures the entire flexibility region with fewer constraints.

Findings

The method accurately captures the aggregate flexibility region.

It is computationally more efficient than direct aggregation methods.

The approach effectively handles uncertainty in EV arrivals and departures.

Abstract

Aggregation is crucial to the effective use of flexibility, especially in the case of electric vehicles (EVs) because of their limited individual battery sizes and large aggregate impact. This research proposes a novel method to quantify and represent the aggregate charging flexibility of EV fleets within a fixed flexibility request window. These windows can be chosen based on relevant network operator needs, such as evening congestion periods. The proposed representation is independent of the number of assets but scales only with the number of discrete time steps in the chosen window. The representation involves $2T$ parameters, with T being the number of consecutive time steps in the window. The feasibility of aggregate power signals can be checked using $2T$ constraints and optimized using $2(2^T-1)$ constraints, both exactly capturing the flexibility region. Using a request window eliminates uncertainty related to EV arrival and departure times outside the window. We present the necessary theoretical framework for our proposed methods and outline steps for transitioning between representations. Additionally, we compare the computational efficiency of the proposed method with the common direct aggregation method, where individual EV constraints are concatenated.