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<p>In this study, the Bidirectional Long Short-Term Memory (Bi-LSTM) model is used to optimize the unbalanced allocation mechanism in the electricity market, and an empirical analysis is made based on the Nord Pool dataset. It is found that the Bi-LSTM model is significantly better than the traditional method in reducing the total deviation power and unbalanced cost. Specifically, the proposed model reduces the total deviation power by 16.5% and the unbalanced cost by 12.3%. The incentive compatibility index shows that the incentive effect of the proposed model reaches 27.0%. It is higher than that of the F1 mechanism (23.5%) and the F2 mechanism (25.5%). In terms of cost allocation efficiency, the mean square error of the proposed model is 0.018. This shows higher cost allocation accuracy compared with 0.028 in the F1 mechanism and 0.022 in the F2 mechanism. These results show that the proposed model has obvious advantages in improving market operation efficiency, encouraging market participants to reduce deviation electricity, and realizing fair cost allocation. The research conclusion provides a new technical path for the design of the allocation mechanism in the power market and provides strong support for future market management and optimization.</p>

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Unbalanced cost allocation in electricity spot markets using Bi-LSTM: a case study on Nord Pool

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月15日

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  • Wenjun Zhu,
  • Jiaxun Liu,
  • Yuanming Huang,
  • Zhijian Zeng,
  • Xingan Yao,
  • Jie Zhang

摘要

In this study, the Bidirectional Long Short-Term Memory (Bi-LSTM) model is used to optimize the unbalanced allocation mechanism in the electricity market, and an empirical analysis is made based on the Nord Pool dataset. It is found that the Bi-LSTM model is significantly better than the traditional method in reducing the total deviation power and unbalanced cost. Specifically, the proposed model reduces the total deviation power by 16.5% and the unbalanced cost by 12.3%. The incentive compatibility index shows that the incentive effect of the proposed model reaches 27.0%. It is higher than that of the F1 mechanism (23.5%) and the F2 mechanism (25.5%). In terms of cost allocation efficiency, the mean square error of the proposed model is 0.018. This shows higher cost allocation accuracy compared with 0.028 in the F1 mechanism and 0.022 in the F2 mechanism. These results show that the proposed model has obvious advantages in improving market operation efficiency, encouraging market participants to reduce deviation electricity, and realizing fair cost allocation. The research conclusion provides a new technical path for the design of the allocation mechanism in the power market and provides strong support for future market management and optimization.