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Evaluating alternative offering strategies for wind producers in a pool


  • Rahimiyan, Morteza
  • Morales, Juan M.
  • Conejo, Antonio J.


As wind power technology matures and reaches break-even cost, wind producers find it increasingly attractive to participate in pool markets instead of being paid feed-in tariffs. The key issue is then how a wind producer should offer in the pool markets to achieve maximum profit while controlling the variability of such profit. This paper compares two families of offering strategies based, respectively, on a naive use of wind production forecasts and on stochastic programming models. These strategies are compared through a comprehensive out-of-sample chronological analysis based on real-world data. A number of relevant conclusions are then duly drawn.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:88:y:2011:i:12:p:4918-4926
    DOI: 10.1016/j.apenergy.2011.06.038

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    References listed on IDEAS

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    Cited by:

    1. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    2. Suomalainen, Kiti & Pritchard, Geoffrey & Sharp, Basil & Yuan, Ziqi & Zakeri, Golbon, 2015. "Correlation analysis on wind and hydro resources with electricity demand and prices in New Zealand," Applied Energy, Elsevier, vol. 137(C), pages 445-462.
    3. Arjmand, Reza & Rahimiyan, Morteza, 2016. "Impact of spatio-temporal correlation of wind production on clearing outcomes of a competitive pool market," Renewable Energy, Elsevier, vol. 86(C), pages 216-227.
    4. Exizidis, Lazaros & Kazempour, S. Jalal & Pinson, Pierre & de Greve, Zacharie & Vallée, François, 2016. "Sharing wind power forecasts in electricity markets: A numerical analysis," Applied Energy, Elsevier, vol. 176(C), pages 65-73.
    5. Mahmoudi, Nadali & Saha, Tapan K. & Eghbal, Mehdi, 2014. "Modelling demand response aggregator behavior in wind power offering strategies," Applied Energy, Elsevier, vol. 133(C), pages 347-355.
    6. Xiao, Yunpeng & Wang, Xifan & Wang, Xiuli & Dang, Can & Lu, Ming, 2016. "Behavior analysis of wind power producer in electricity market," Applied Energy, Elsevier, vol. 171(C), pages 325-335.
    7. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    8. Tryggvi Jónsson & Pierre Pinson & Henrik Aa. Nielsen & Henrik Madsen, 2014. "Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets," Energies, MDPI, Open Access Journal, vol. 7(6), pages 1-23, June.
    9. Osmani, Atif & Zhang, Jun, 2014. "Optimal grid design and logistic planning for wind and biomass based renewable electricity supply chains under uncertainties," Energy, Elsevier, vol. 70(C), pages 514-528.
    10. 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, Open Access Journal, vol. 5(11), pages 1-20, November.


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