A Bidding Strategy for Virtual Power Plants in the Day-Ahead Market

Yueping Kong, Yuqin Chen, Jiao Du, Yongbiao Yang and Qingshan Xu ()
Additional contact information
Yueping Kong: Jiangsu Power Company, State Grid, Nanjing 210024, China
Yuqin Chen: Jiangsu Power Company, State Grid, Nanjing 210024, China
Jiao Du: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Yongbiao Yang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Qingshan Xu: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2025, vol. 18, issue 18, 1-20

Abstract: Under the context of rapid distributed energy development and ongoing electricity market reforms, this paper investigates bidding strategies for virtual power plants (VPPs) formed by aggregated distributed renewable energy (DRE) in China’s evolving day-ahead electricity market. To address privacy concerns of DRE participants and VPP aggregators during dynamic aggregation, an enhanced Benders decomposition framework is proposed. The methodology first characterizes market uncertainties (e.g., electricity prices and renewable generation output) by clustering them into representative scenarios using K-medoids clustering. A privacy-preserving decentralized optimization model is then formulated: the VPP aggregator solves a master problem to determine bidding decisions, while DRE units independently address subproblems via privacy-protected mathematical constraints that avoid revealing explicit operational details. The framework ensures secure information exchange and computational efficiency. Case studies demonstrate that the proposed model effectively balances privacy protection and bidding performance, outperforming traditional centralized optimization approaches in terms of solution quality and scalability.

Keywords: virtual power plant; benders decomposition; bidding strategy; day-ahead market; privacy preservation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/18/4874/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/18/4874/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:18:p:4874-:d:1748917

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().