JOURNAL ARTICLE
From Curtailed Renewable Energy to Green Hydrogen: Infrastructure Planning for Hydrogen Fuel-Cell Vehicles.
Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2024, v. 26, n. 5. P. 1750 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: He, Long; Ke, Nan; Mao, Ruijiu; Qi, Wei; Zhang, Hongcai 3 of 3
Abstract
This article focuses on promoting hydrogen fuel-cell vehicles (HFVs) as a green transportation alternative while addressing challenges related to infrastructure planning, renewable energy curtailment, and power grid constraints. It proposes an integrated optimization model that jointly determines the locations and capacities of hydrogen refueling stations (HRSs), hydrogen plants, and necessary grid upgrades, explicitly incorporating drivers’ HFV adoption behavior and route choices under uncertainty. Applied to Sichuan Province, China—a region with abundant hydropower curtailment and spatial mismatch between energy supply and demand—the model reveals that optimal infrastructure deployment varies with vehicle miles traveled (VMT) targets, balancing proximity to hydropower sources and population centers. The study finds that promoting HFVs can reduce hydropower curtailment effectively, with cost-effectiveness influenced by grid upgrade and electricity purchase costs, and that HFVs currently offer greater flexibility and potential cost savings compared to electric vehicles (EVs), especially for heavy-duty freight transport and under grid constraints.
Additional Information
- Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2024/09, Vol. 26, Issue 5, p1750
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2024
- ISSN:1523-4614
- DOI:10.1287/msom.2022.0381
- Accession Number:179561471
- Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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