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Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

Author

Listed:
  • Haifeng Zhang

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

  • Feng Gao

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

  • Jiang Wu

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

  • Kun Liu

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

  • Xiaolin Liu

    (State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, China)

Abstract
Wind Power Producers (WPPs) seek to maximize profit and minimize the imbalance costs when bidding into the day-ahead market, but uncertainties in the hourly available wind and forecasting errors make the bidding risky. This paper assumes that hourly wind power output given by the forecast follows a normal distribution, and proposes three different bidding strategies, i.e. , the expected profit-maximization strategy (EPS), the chance-constrained programming-based strategy (CPS) and the multi-objective bidding strategy (ECPS). Analytical solutions under the three strategies are obtained. Comparisons among the three strategies are conducted on a hypothetical wind farm which follows the Spanish market rules. Results show that bid under the EPS is highly dependent on market clearing price, imbalance prices, and also the mean value and standard deviation of wind forecast, and that bid under the CPS is largely driven by risk parameters and the mean value and standard deviation of the wind forecast. The ECPS combining both EPS and CPS tends to choose a compromise bid. Furthermore, the ECPS can effectively control the tradeoff between expected profit and target profit for WPPs operating in volatile electricity markets.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:11:p:4804-4823:d:21596
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    References listed on IDEAS

    as
    1. Holttinen, H., 2005. "Optimal electricity market for wind power," Energy Policy, Elsevier, vol. 33(16), pages 2052-2063, November.
    2. Rahimiyan, Morteza & Morales, Juan M. & Conejo, Antonio J., 2011. "Evaluating alternative offering strategies for wind producers in a pool," Applied Energy, Elsevier, vol. 88(12), pages 4918-4926.
    3. J. Muñoz & J. Contreras & J. Caamaño & P. Correia, 2011. "A decision-making tool for project investments based on real options: the case of wind power generation," Annals of Operations Research, Springer, vol. 186(1), pages 465-490, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Mazaher Haji Bashi & Gholamreza Yousefi & Claus Leth Bak & Jayakrishnan Radhakrishna Pillai, 2016. "Long Term Expected Revenue of Wind Farms Considering the Bidding Admission Uncertainty," Energies, MDPI, vol. 9(11), pages 1-17, November.
    2. Sergey Voronin & Jarmo Partanen, 2013. "Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks," Energies, MDPI, vol. 6(11), pages 1-24, November.
    3. Meng Xiong & Feng Gao & Kun Liu & Siyun Chen & Jiaojiao Dong, 2015. "Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control," Energies, MDPI, vol. 8(8), pages 1-32, August.
    4. Chuntian Cheng & Fu Chen & Gang Li & Qiyu Tu, 2016. "Market Equilibrium and Impact of Market Mechanism Parameters on the Electricity Price in Yunnan’s Electricity Market," Energies, MDPI, vol. 9(6), pages 1-17, June.
    5. Perica Ilak & Slavko Krajcar & Ivan Rajšl & Marko Delimar, 2014. "Pricing Energy and Ancillary Services in a Day-Ahead Market for a Price-Taker Hydro Generating Company Using a Risk-Constrained Approach," Energies, MDPI, vol. 7(4), pages 1-26, April.
    6. Perica Ilak & Ivan Rajšl & Josip Đaković & Marko Delimar, 2018. "Duality Based Risk Mitigation Method for Construction of Joint Hydro-Wind Coordination Short-Run Marginal Cost Curves," Energies, MDPI, vol. 11(5), pages 1-12, May.
    7. 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.

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