Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8–10, 2023, Guangzhou, China

Research Article

Day-ahead Bidding Model for Virtual Power Plant Based on Segmented Bidirectional Pinch Forcing Theorem

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  • @INPROCEEDINGS{10.4108/eai.8-12-2023.2344465,
        author={Jianghong  Nie and Ling  Gan and Nianbin  Chen},
        title={Day-ahead Bidding Model for Virtual Power Plant Based on Segmented Bidirectional Pinch Forcing Theorem},
        proceedings={Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8--10, 2023, Guangzhou, China},
        publisher={EAI},
        proceedings_a={MSIEID},
        year={2024},
        month={4},
        keywords={virtual power plant; pinch forcing theorem; bidding model},
        doi={10.4108/eai.8-12-2023.2344465}
    }
    
  • Jianghong Nie
    Ling Gan
    Nianbin Chen
    Year: 2024
    Day-ahead Bidding Model for Virtual Power Plant Based on Segmented Bidirectional Pinch Forcing Theorem
    MSIEID
    EAI
    DOI: 10.4108/eai.8-12-2023.2344465
Jianghong Nie1,*, Ling Gan1, Nianbin Chen1
  • 1: Hubei power exchange center
*Contact email: njh@hbepc.com

Abstract

In developing the bidding strategy for the day-ahead market, the virtual power plant needs to forecast the marginal day-ahead market clearing tariff, as well as the output of wind turbines and photovoltaic (PV) units within the virtual power plant. The characteristic scenarios comprising the key risk factors can be classified by simultaneously considering the time-period levels of the key risk factors affecting the above three uncertain variables over the historical statistical period and by performing a cluster analysis of the curve similarity representations based on the segmented bi-directional pinch forcing theorem. By categorizing the results after predicting the values of the day-ahead key risk factors on a future date, i.e., the distribution of the three uncertain variables under the same category can be obtained, and the values of the uncertain variables can be predicted with a certain probability. The uncertain variables are brought into the objective function of virtual power plant bidding, the virtual power plant day-ahead bidding strategy can be obtained. The simulation results verify the effectiveness of the proposed method.