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Imbalance settlement evaluation for China's balancing market design via an agent-based model with a multiple criteria decision analysis method

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  • Wu, Zhaoyuan
  • Zhou, Ming
  • Zhang, Ting
  • Li, Gengyin
  • Zhang, Yan
  • Liu, Xiaojuan

Abstract

Constructing spot markets is the core objective of the new round of electricity market reform kicked off in 2015 in China. A balancing market, as a critical part of a spot market, is an institutional arrangement that deals with balancing electricity demand and supply. Imbalance settlement provides a mechanism for settling the inevitable discrepancies between contractual agreements and physical delivery. Large proportions of long-term non-financially contracted electricity and a high share of renewable generation represent specific market situations in China and make balancing market operation and relevant imbalance settlement more difficult. This paper aims to investigate the effect of imbalance settlement design and exploit an effective evaluation method. An investigation model combining the methods of agent-based modelling (ABM) and multiple criteria decision analysis (MCDA) is proposed to search for the optimal design elements for China's imbalance settlement. Different tolerance margins, Programme Time Units (PTUs) and imbalance pricing mechanisms in imbalance settlement design are analysed. The impacts of imbalance settlement on the behaviour of market participants and overall market are revealed. Finally, corresponding policy implications for imbalance settlement in China's balancing market are put forward. The proposed model also offers a tool for evaluating other design elements in a balancing market.

Suggested Citation

  • Wu, Zhaoyuan & Zhou, Ming & Zhang, Ting & Li, Gengyin & Zhang, Yan & Liu, Xiaojuan, 2020. "Imbalance settlement evaluation for China's balancing market design via an agent-based model with a multiple criteria decision analysis method," Energy Policy, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:enepol:v:139:y:2020:i:c:s0301421520300550
    DOI: 10.1016/j.enpol.2020.111297
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    2. Wang, Yaxian & Zhao, Zhenli & Baležentis, Tomas, 2023. "Benefit distribution in shared private charging pile projects based on modified Shapley value," Energy, Elsevier, vol. 263(PB).
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    4. Guo, Hongye & Davidson, Michael R. & Chen, Qixin & Zhang, Da & Jiang, Nan & Xia, Qing & Kang, Chongqing & Zhang, Xiliang, 2020. "Power market reform in China: Motivations, progress, and recommendations," Energy Policy, Elsevier, vol. 145(C).
    5. Heeseung Moon & Dongsu Lee & Jeongmin Han & Yongtae Yoon & Seungwan Kim, 2021. "Impact of Imbalance Pricing on Variable Renewable Energies with Different Prediction Accuracies: A Korean Case," Energies, MDPI, vol. 14(13), pages 1-19, July.
    6. Sinan Deng & John Inekwe & Vladimir Smirnov & Andrew Wait & Chao Wang, 2023. "Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices," Working Papers 2023-03, University of Sydney, School of Economics.
    7. Wu, Zhaoyuan & Zhou, Ming & Zhang, Zhi & Zhao, Huiru & Wang, Jianxiao & Xu, Jiayu & Li, Gengyin, 2022. "An incentive profit-sharing mechanism for welfare transfer in balancing market integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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