JOURNAL ARTICLE
Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets.
Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2024, v. 26, n. 3. P. 814 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Mak, Ho-Yin; Tang, Runyu 3 of 3
Abstract
This article focuses on optimizing the operations of vehicle-to-grid (V2G) platforms that coordinate fleets of individually owned electric vehicles (EVs) to provide frequency regulation services to the electric grid. It develops a bilevel optimization model where the V2G platform sets hourly capacity bids and rebate incentives to encourage EV owners to plug in their vehicles, while drivers respond by adjusting their travel and charging schedules. Using real-world data from the California Household Travel Survey and PJM frequency regulation markets, the study finds that time-varying rebates and workplace charging infrastructure significantly enhance platform profits by better aligning EV availability with high-value regulation periods. Extensions consider workplace chargers as strategic substitutes to rebates and analyze state-of-charge guarantees, showing that such guarantees can reduce platform profits and driver rebates but partial guarantees during specific hours may mitigate losses. The findings provide insights into designing driver incentives and operational strategies for scalable and economically viable V2G frequency regulation services.
Additional Information
- Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2024/05, Vol. 26, Issue 3, p814
- Document Type:Article
- Subject Area:Engineering
- Publication Date:2024
- ISSN:1523-4614
- DOI:10.1287/msom.2022.0133
- Accession Number:177184352
- 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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