Publication Type
Journal Article
Version
submittedVersion
Publication Date
9-2024
Abstract
To support the rapid growth in global electric vehicle adoption, public charging of electric vehicles is crucial. We study the problem of an electric vehicle charging service provider, which faces (1) stochastic arrival of customers with distinctive arrival and departure times, and energy requirements as well as (2) a total electricity cost including demand charges, costs related to the highest per-period electricity used in a finite horizon. We formulate its problem of scheduling vehicle charging to minimize the expected total cost as a stochastic program (SP). As this SP is large-scale, we solve it using exponential cone program (ECP) approximations. For the SP with unlimited chargers, we derive an ECP as an upper bound and characterize the bound on the gap between their theoretical performances. For the SP with limited chargers, we then extend this ECP by also leveraging the idea from distributionally robust optimization (DRO) of employing an entropic dominance ambiguity set: Instead of using DRO to mitigate distributional ambiguity, we use it to derive an ECP as a tractable upper bound of the SP. We benchmark our ECP approach with sample average approximation (SAA) and a DRO approach using a semi-definite program (SDP) on numerical instances calibrated to real data. As our numerical instances are large-scale, we find that while SDP cannot be solved, ECP scales well and runs eciently (about 50 times faster than SAA) and consequently results in a lower mean total cost than SAA. We then show that our ECP continues to perform well considering practical implementation issues, including a data-driven setting and an adaptive charging environment. We finally extend our ECP approaches (for both the uncapacitated and capacitated cases) to include the pricing decision and propose an alternating optimization algorithm, which performs better than SAA on our numerical instances. Our method of constructing ECPs can be potentially applicable to approximate more general two-stage linear SPs with fixed recourse. We also use ECP to generate managerial insights for both charging service providers and policymakers
Keywords
stochastic programming, exponential cone programming, electric vehicle, demand charge, robust optimization
Discipline
Operations and Supply Chain Management | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Operations Management
Publication
Operations Research
Volume
72
Issue
5
First Page
2215
Last Page
2240
ISSN
0030-364X
Identifier
10.1287/opre.2023.2460
Publisher
INFORMS
Embargo Period
3-9-2020
Citation
CHEN, Li; HE, Long; and ZHOU, Yangfang (Helen).
An exponential cone programming approach for managing electric vehicle charging. (2024). Operations Research. 72, (5), 2215-2240.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/6517
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1287/opre.2023.2460
Included in
Operations and Supply Chain Management Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
Comments
Accepted in Operations Research