Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12–14, 2024, Ningbo, China

Research Article

Bidding Feature Extraction and Bidding Strategy Optimization of Generation Companies Based on Big Data Analysis and Machine Learning

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  • @INPROCEEDINGS{10.4108/eai.12-1-2024.2347200,
        author={Yang  Tang and Yifeng  Liu and Weiqiang  Huo and Meng  Chen and Junxuan  Zou and Xi  Chen},
        title={Bidding Feature Extraction and Bidding Strategy Optimization of Generation Companies Based on Big Data Analysis and Machine Learning},
        proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12--14, 2024, Ningbo, China},
        publisher={EAI},
        proceedings_a={BDEDM},
        year={2024},
        month={6},
        keywords={electricity market; bidding strategy; big data analyze; canonical correlation analysis; simulated annealing algorithm},
        doi={10.4108/eai.12-1-2024.2347200}
    }
    
  • Yang Tang
    Yifeng Liu
    Weiqiang Huo
    Meng Chen
    Junxuan Zou
    Xi Chen
    Year: 2024
    Bidding Feature Extraction and Bidding Strategy Optimization of Generation Companies Based on Big Data Analysis and Machine Learning
    BDEDM
    EAI
    DOI: 10.4108/eai.12-1-2024.2347200
Yang Tang1,*, Yifeng Liu1, Weiqiang Huo1, Meng Chen1, Junxuan Zou2, Xi Chen2
  • 1: Hubei Power Exchange Center
  • 2: Huazhong University of Science and Technology
*Contact email: ty6066@qq.com

Abstract

This paper introduces a method for optimizing bidding strategy for generation companies based on big data analysis and machine learning. This paper first proposes a bidding feature extraction model based on canonical correlation analysis, then proposes a generation company bidding prediction model, and finally solves the bi-level optimization model for generation companies bidding strategy optimization based on the simulated annealing method. A case scenario based on actual spot market data in China is provided to illustrate the relevant method. This paper optimizes the bidding strategy for a large coal-fired unit, and the profit of the unit increased by 76.6% after optimization. It is found that the key point of the bidding strategy for large coal-fired units is to keep the price of the first section low to ensure that the unit can always be turned on.