Advanced optimization models for the location of charging stations in e-mobility

TL;DR

This paper develops advanced optimization models for strategically locating electric vehicle charging stations, considering costs, capacity limits, and coverage, to improve infrastructure planning amidst growing EV adoption.

Contribution

It introduces three novel extensions to the deterministic flow refuelling location problem, addressing coverage, cost considerations, and capacity constraints with comprehensive evaluations.

Findings

Cost scenarios significantly influence optimal station placement.

Limited capacity models affect the number and size of charging stations.

Extensions outperform traditional models in infrastructure planning accuracy.

Abstract

For a reduction in environmental pollution and dependency on petroleum, electric vehicles (EV) present an advantageous alternative to traditionally fossil-fuel powered automobiles. Rapid growth in the number of EVs requires an urgent need to develop an adequate charging station infrastructure to stimulate and facilitate their usage. Due to restricted investments in the development of a sufficient infrastructure, locations have to be chosen deliberately. In this paper, three extensions considering different objectives and various constraints to the deterministic flow refuelling location problem (DFRLP), described 2017 by de Vries and Duijzer, are introduced. In the first extension we ask how many charging stations (CS) are necessary to cover a pre-specified number of EVs and therefore exchange the original objective function for a minimizing cost function. Secondly, our research shows that, when considering location-dependent construction costs, results heavily depend on the relations of said cost differences. Tests for different cost scenarios are carried out and policy implications are discussed. In the last extension, we consider the capacity of a CS to be limited. The DFRLP assumes an unlimited capacity, meaning it is always possible to refuel all EVs at all CSs, where they stop. In our model the capacity is put into relation to the total sum of demands generated by all EVs, passing a particular CS, which means that our model determines the placement and the sizes of all CSs simultaneously. Finally, all extensions are evaluated using benchmarks instances based on test instances from the literature.