Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning

Haoen Weng, Yongli Hu, Min Liang, Jiayang Xi and Baocai Yin

Applied Energy, 2025, vol. 380, issue C, No S0306261924023626

Abstract: Formulating optimal bidding strategies is pivotal for market participants to enhance electricity market profits. The main challenge for finding optimal bidding strategies is how to deal with system uncertainty, which stems from the inherent unpredictability and fluctuation within the electricity market. In the previous works, deep reinforcement learning (DRL) is proved a promising approach in multi-agent system with uncertainty. But few works model the relevance between agents for processing system uncertainty, especially the dynamic correlation in the operation of market. For this purpose, this paper proposes to model the correlation between agents to cope with the system uncertainty in a representative centralized double-sided auction market by combining graph convolutional neural network (GCN) with deep deterministic policy gradient (DDPG) algorithm, which is not only able to deal with the system uncertainty by aggregating correlative information of neighboring agents, but also helps obtain superior bidding strategies for the market participants. The proposed algorithm is evaluated on 4-bus, 30-bus and 57-bus congested network, where both supply side and demand side with elastic demand are modeled as RL agents. The results demonstrate that the proposed algorithm achieves higher system profits than the DRL based algorithms without GCN.

Keywords: Electricity market; Optimal bidding strategy; Multi-agent systems (MAS); Graph convolutional neural network (GCN); Deep reinforcement learning (DRL) (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924023626
Full text for ScienceDirect subscribers only

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:eee:appene:v:380:y:2025:i:c:s0306261924023626

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124978

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().