A Multi-Battery Model for the Aggregate Flexibility of Heterogeneous Electric Vehicles

TL;DR

This paper introduces a multi-battery model that simplifies the collective flexibility of heterogeneous electric vehicles into a few representative battery sets, enabling better integration into electricity markets.

Contribution

It proposes a novel multi-battery flexibility model using a clustering approach to identify base sets that approximate the aggregate EV flexibility.

Findings

The model accurately captures the aggregate flexibility of diverse EVs.

The convex approximation enables efficient computation of the flexibility set.

Numerical experiments demonstrate the method's effectiveness.

Abstract

The increasing prevalence of electric vehicles (EVs) in the transportation sector will introduce a large number of highly flexible electric loads that EV aggregators can pool and control to provide energy and ancillary services to the wholesale electricity market. To integrate large populations of EVs into electricity market operations, aggregators must express the aggregate flexibility of the EVs under their control in the form of a small number of energy storage (battery) resources that accurately capture the supply/demand capabilities of the individual EVs as a collective. To this end, we propose a novel multi-battery flexibility model defined as a linear combination of a small number of base sets (termed batteries) that reflect the differing geometric shapes of the individual EV flexibility sets, and suggest a clustering approach to identify these base sets. We study the problem of computing a multi-battery flexibility set that has minimum Hausdorff distance to the aggregate flexibility set, subject to the constraint that the multi-battery flexibility set be a subset of the aggregate flexibility set. We show how to conservatively approximate this problem with a tractable convex program, and illustrate the performance achievable by our method with several numerical experiments.