An Integer Clustering Approach for Modeling Large-Scale EV Fleets with Guaranteed Performance

TL;DR

This paper introduces a novel integer-clustering method for large-scale EV fleet modeling that guarantees performance and improves computational efficiency in planning and operation tasks.

Contribution

The paper develops a new integer-clustering approach with theoretical bounds, enabling scalable and guaranteed-performance modeling of large EV fleets.

Findings

The method achieves substantial speedups in computation.

Minimal loss in solution quality compared to exact methods.

Validated on Boston's public transit network case study.

Abstract

Large-scale integration of electric vehicles (EVs) leads to a tighter integration between transportation and electric energy systems. In this paper, we develop a novel integer-clustering approach to model a large number of EVs that manages vehicle charging and energy at the fleet level yet maintain individual trip dispatch. The model is then used to develop a spatially and temporally-resolved decision-making tool for optimally planning and/or operating EV fleets and charging infrastructure. The tool comprises a two-stage framework where a tractable disaggregation step follows the integer-clustering problem to recover an individually feasible solution. Mathematical relationships between the integer clustering, disaggregation, and individual formulations are analyzed. We establish theoretical lower and upper bounds on the true individual formulation which underpins a guaranteed performance of the proposed method. The optimality accuracy and computational efficiency of the integer-clustering formulation are also numerically validated on a real-world case study of Boston's public transit network under extensive test instances. Substantial speedups with minimal loss in solution quality are demonstrated.