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Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference

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  • Wu, Shengyang
  • Ding, Zhaohao
  • Wang, Jingyu
  • Shi, Dongyuan

Abstract

With the ever-increasing level of bidding freedom bestowed to participants in deregulated electricity markets, bidding strategies have become more diversified and complicated, inevitably giving rise to the growth of market uncertainties. Some researchers have developed tools to predict the bidding behaviors of generation companies (GENCOs) considering uncertainties. However, there still remains a gap in enhancing the performance of probabilistic Bidding Behavior Forecasting (BBF) and understanding the sources of bidding uncertainties in electricity markets. This paper proposes a holistic Bayesian Deep Learning (BDL) framework based on Accurate Variational Inference (AVI) to capture both aleatoric uncertainty and epistemic uncertainty, two important uncertainties in bidding behaviors. The introduced framework also procures higher BBF accuracy and reasonable computation cost compared with existing techniques. Derivatives of GENCOs’ bidding uncertainty are ascertained by implementing a new metric to quantify the impact of influence factors. A numerical experiment is conducted using data from National Electricity Market (NEM) in Australia to demonstrate the performance of the proposed framework.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223006801
    DOI: 10.1016/j.energy.2023.127286
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    1. Sirin, Selahattin Murat & Erdogan, Fakir H., 2013. "R&D expenditures in liberalized electricity markets: The case of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 491-498.
    2. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    3. 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).
    4. Liu, Jinqi & Wang, Jihong & Cardinal, Joel, 2022. "Evolution and reform of UK electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Liu, Yangyang & Shen, Zhongqi & Tang, Xiaowei & Lian, Hongbo & Li, Jiarui & Gong, Jinxia, 2019. "Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties," Applied Energy, Elsevier, vol. 256(C).
    6. Mahvi, M. & Ardehali, M.M., 2011. "Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis," Energy, Elsevier, vol. 36(11), pages 6367-6374.
    7. Kara, Güray & Pisciella, Paolo & Tomasgard, Asgeir & Farahmand, Hossein & Crespo del Granado, Pedro, 2022. "Stochastic local flexibility market design, bidding, and dispatch for distribution grid operations," Energy, Elsevier, vol. 253(C).
    8. Lee, Kuan-Cheng & Yang, Hong-Tzer & Tang, Wenjun, 2022. "Data-driven online interactive bidding strategy for demand response," Applied Energy, Elsevier, vol. 319(C).
    9. Matamala, Yolanda & Feijoo, Felipe, 2021. "A two-stage stochastic Stackelberg model for microgrid operation with chance constraints for renewable energy generation uncertainty," Applied Energy, Elsevier, vol. 303(C).
    10. Corsatea, Teodora Diana & Giaccaria, Sergio, 2018. "Market regulation and environmental productivity changes in the electricity and gas sector of 13 observed EU countries," Energy, Elsevier, vol. 164(C), pages 1286-1297.
    11. Shafiekhani, Morteza & Ahmadi, Abdollah & Homaee, Omid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads," Energy, Elsevier, vol. 239(PD).
    12. Xiao, Xiangsheng & Wang, Jianxiao & Lin, Rui & Hill, David J. & Kang, Chongqing, 2020. "Large-scale aggregation of prosumers toward strategic bidding in joint energy and regulation markets," Applied Energy, Elsevier, vol. 271(C).
    13. Guo, Hongye & Chen, Qixin & Shahidehpour, Mohammad & Xia, Qing & Kang, Chongqing, 2022. "Bidding behaviors of GENCOs under bounded rationality with renewable energy," Energy, Elsevier, vol. 250(C).
    14. Zhou, Anhua & Li, Jun, 2021. "Investigate the impact of market reforms on the improvement of manufacturing energy efficiency under China’s provincial-level data," Energy, Elsevier, vol. 228(C).
    15. Zhang, Xinyue & Guo, Xiaopeng & Zhang, Xingping, 2023. "Bidding modes for renewable energy considering electricity-carbon integrated market mechanism based on multi-agent hybrid game," Energy, Elsevier, vol. 263(PA).
    16. Liu, Xin & Li, Yang & Lin, Xueshan & Guo, Jiqun & Shi, Yunpeng & Shen, Yunwei, 2022. "Dynamic bidding strategy for a demand response aggregator in the frequency regulation market," Applied Energy, Elsevier, vol. 314(C).
    17. Ochoa, Tomás & Gil, Esteban & Angulo, Alejandro & Valle, Carlos, 2022. "Multi-agent deep reinforcement learning for efficient multi-timescale bidding of a hybrid power plant in day-ahead and real-time markets," Applied Energy, Elsevier, vol. 317(C).
    18. Shen, Ziqi & Wei, Wei & Wu, Lei & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Economic dispatch of power systems with LMP-dependent demands: A non-iterative MILP model," Energy, Elsevier, vol. 233(C).
    19. Saeian, Hosein & Niknam, Taher & Zare, Mohsen & Aghaei, Jamshid, 2022. "Coordinated optimal bidding strategies methods of aggregated microgrids: A game theory-based demand side management under an electricity market environment," Energy, Elsevier, vol. 245(C).
    20. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
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