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Wind power bidding strategy in the short-term electricity market

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  • Li, Shaomao
  • Park, Chan S.

Abstract

This paper presents an analytical trading electricity model for wind power producers (WPPs) in the short-term electricity market in the U.S. This model addresses four specific uncertainties: real-time (RT) wind power generation, day-ahead (DA) locational marginal prices (LMPs), RT LMPs, and deviation penalty rates. The model is designed to find the optimal bidding strategy to maximize the expected revenue under these uncertainties. In addition, this paper shows that advanced forecasting techniques could be used with the proposed bidding strategy to help WPPs trade energy in short-term markets. A case study is presented to illustrate the effectiveness of this proposed bidding strategy and advanced forecasting techniques using a set of real data taken from a wind farm in the PJM electricity market.

Suggested Citation

  • Li, Shaomao & Park, Chan S., 2018. "Wind power bidding strategy in the short-term electricity market," Energy Economics, Elsevier, vol. 75(C), pages 336-344.
    • Handle: RePEc:eee:eneeco:v:75:y:2018:i:c:p:336-344
    DOI: 10.1016/j.eneco.2018.08.029
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    References listed on IDEAS

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    1. Haifeng Zhang & Feng Gao & Jiang Wu & Kun Liu & Xiaolin Liu, 2012. "Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market," Energies, MDPI, vol. 5(11), pages 1-20, November.
    2. Vilim, Michael & Botterud, Audun, 2014. "Wind power bidding in electricity markets with high wind penetration," Applied Energy, Elsevier, vol. 118(C), pages 141-155.
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    Citations

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    Cited by:

    1. Jiakai Hu & Chuanwen Jiang & Yangyang Liu, 2019. "Short-Term Bidding Strategy for a Price-Maker Virtual Power Plant Based on Interval Optimization," Energies, MDPI, vol. 12(19), pages 1-22, September.
    2. Shojaabadi, Saeed & Talavat, Vahid & Galvani, Sadjad, 2022. "A game theory-based price bidding strategy for electric vehicle aggregators in the presence of wind power producers," Renewable Energy, Elsevier, vol. 193(C), pages 407-417.
    3. Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022. "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, vol. 112(C).
    4. Liu, Tingting & Chen, Zhe & Xu, Jiuping, 2022. "Empirical evidence based effectiveness assessment of policy regimes for wind power development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    5. Endemaño-Ventura, Lázaro & Serrano González, Javier & Roldán Fernández, Juan Manuel & Burgos Payán, Manuel & Riquelme Santos, Jesús Manuel, 2021. "Optimal energy bidding for renewable plants: A practical application to an actual wind farm in Spain," Renewable Energy, Elsevier, vol. 175(C), pages 1111-1126.
    6. Kenis, Michiel & Höschle, Hanspeter & Bruninx, Kenneth, 2022. "Strategic bidding of wind power producers in electricity markets in presence of information sharing," Energy Economics, Elsevier, vol. 110(C).
    7. Shinji Kuno & Kenji Tanaka & Yuji Yamada, 2022. "Effectiveness and Feasibility of Market Makers for P2P Electricity Trading," Energies, MDPI, vol. 15(12), pages 1-24, June.
    8. Mohammad Nure Alam, 2021. "Accessing the Effect of Renewables on the Wholesale Power Market," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 341-360.

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    More about this item

    Keywords

    Wind power; Bidding strategy; Forecasting model; Short-term market; Analytical method;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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