Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting
Abstract
Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.
Keywords
Cite
@article{arxiv.2107.00165,
title = {Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting},
author = {Justin Luke and Mauro Salazar and Ram Rajagopal and Marco Pavone},
journal= {arXiv preprint arXiv:2107.00165},
year = {2024}
}
Comments
9 pages, 7 figures. Corrected typos, revised text for clarification purposes, results unchanged. A version of this submission, with minor formatting changes, is to be published in the proceedings of the 24th IEEE International Conference on Intelligent Transportation Systems (ITSC 2021)