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Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning

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  • Wu, Jiahui
  • Wang, Jidong
  • Kong, Xiangyu

Abstract

The electricity market will tend to be diverse and competitive to realize Carbon Neutrality goals under Energy Internet. Moreover, bidding strategies and methods are essential for the stable and benign operation of the electricity market. With the development of artificial intelligence and computer simulation technology, multi-agent simulation has gradually become a significant method for electricity market bidding. Among them, Multi-Agent Reinforcement Learning (MARL) can help agents adapt to changing environments. In contrast, Multi-Agent Transfer Learning (MATL) can help agents learn from not only the target task but also other similar tasks. This paper proposes an intelligent strategic bidding theoretical framework in a competitive electricity market using MATL based on MARL and studies four MATL algorithms, including RNN, LSTM, GRU and BGRU. An intelligent bidding simulation model based on the four MATL algorithms is established, and the performance of the intelligent bidding simulation model in the electricity market using the four MATL algorithms based on the MARL Q-learning algorithm is compared and analyzed from the perspective of accuracy and convergence speed. And based on the multi-agent simulation model, examples of bidding strategies are carried out to verify the rationality and effectiveness of the intelligent bidding method using MATL based on MARL.

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  • Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222015602
    DOI: 10.1016/j.energy.2022.124657
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    2. Adeel Luqman & Qingyu Zhang & Shalini Talwar & Meena Bhatia & Amandeep Dhir, 2024. "Artificial intelligence and corporate carbon neutrality: A qualitative exploration," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 3986-4003, July.
    3. Mi, Hanning & Chen, Sijie & Li, Qingxin & Shi, Ming & Hou, Shuoming & Zheng, Linfeng & Xu, Chengke & Yan, Zheng & Li, Canbing, 2024. "Strategic bidding by predicting locational marginal price with aggregated supply curve," Energy, Elsevier, vol. 304(C).
    4. Xian Huang & Kun Liu, 2023. "Impact of Electricity Price Expectation in the Planning Period on the Evolution of Generation Expansion Planning in the Market Environment," Energies, MDPI, vol. 16(8), pages 1-21, April.
    5. Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
    6. Helder Pereira & Bruno Ribeiro & Luis Gomes & Zita Vale, 2022. "Smart Grid Ecosystem Modeling Using a Novel Framework for Heterogenous Agent Communities," Sustainability, MDPI, vol. 14(23), pages 1-20, November.
    7. Alrobaian, Abdulrahman A. & Alsagri, Ali Sulaiman, 2023. "Multi-agent-based energy management for a fully electrified residential consumption," Energy, Elsevier, vol. 282(C).
    8. Marcelle Caroline Thimotheo de Brito & Amaro O. Pereira Junior & Mario Veiga Ferraz Pereira & Julio César Cahuano Simba & Sergio Granville, 2022. "Competitive Behavior of Hydroelectric Power Plants under Uncertainty in Spot Market," Energies, MDPI, vol. 15(19), pages 1-22, October.
    9. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    10. Li, Qirui & Yang, Zhifang & Yu, Juan & Li, Wenyuan, 2023. "Impacts of previous revenues on bidding strategies in electricity market: A quantitative analysis," Applied Energy, Elsevier, vol. 345(C).

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