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Optimal energy bidding for renewable plants: A practical application to an actual wind farm in Spain

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  • Endemaño-Ventura, Lázaro
  • Serrano González, Javier
  • Roldán Fernández, Juan Manuel
  • Burgos Payán, Manuel
  • Riquelme Santos, Jesús Manuel

Abstract

Finding an optimal bidding strategy for a wind farm in the electricity market is not straightforward due to the wind variability. This issue is becoming more relevant as renewable plants are more exposed to market signals. Considering the characteristics of the European markets, operators must submit the energy bidding between 12 and 36 h ahead the actual delivery time. This bidding can be adjusted later in any session of the intraday markets, but sometimes this is not enough to reduce significantly the deviation risk. This paper presents a practical application of a method to analytically calculate the optimal bidding of a wind power plant, based on the maximisation of the income function. The results show that, given the characteristics of the deviation markets in Spain, the optimal bidding strategy depends essentially on the deviation of the system. The proposed technique has been tested and validated in a real application by considering actual data for energy production and forecasts for an operating wind farm in Spain, as well as real market deviations and prices provided by the Spanish system and market operators; analysing the advantages of the proposed optimal bidding strategy over the most plausible option, based on bidding the forecasted energy.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:175:y:2021:i:c:p:1111-1126
    DOI: 10.1016/j.renene.2021.05.054
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    References listed on IDEAS

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    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Trading Stochastic Production in Electricity Pools," International Series in Operations Research & Management Science, in: Integrating Renewables in Electricity Markets, edition 127, chapter 7, pages 205-242, Springer.
    3. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    4. Laia, R. & Pousinho, H.M.I. & Melíco, R. & Mendes, V.M.F., 2016. "Bidding strategy of wind-thermal energy producers," Renewable Energy, Elsevier, vol. 99(C), pages 673-681.
    5. Afshar, Karim & Ghiasvand, Farshad Shamsini & Bigdeli, Nooshin, 2018. "Optimal bidding strategy of wind power producers in pay-as-bid power markets," Renewable Energy, Elsevier, vol. 127(C), pages 575-586.
    6. Dhillon, Javed & Kumar, Arun & Singal, S.K., 2014. "Optimization methods applied for Wind–PSP operation and scheduling under deregulated market: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 682-700.
    7. Holttinen, H., 2005. "Optimal electricity market for wind power," Energy Policy, Elsevier, vol. 33(16), pages 2052-2063, November.
    8. Jónsson, Tryggvi & Pinson, Pierre & Madsen, Henrik, 2010. "On the market impact of wind energy forecasts," Energy Economics, Elsevier, vol. 32(2), pages 313-320, March.
    9. Moghaddam, Iman Gerami & Nick, Mostafa & Fallahi, Farhad & Sanei, Mohsen & Mortazavi, Saeid, 2013. "Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market," Renewable Energy, Elsevier, vol. 55(C), pages 252-259.
    10. 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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