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Potential of trading wind power as regulation services in the California short-term electricity market

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  • Zhang, Zhao-Sui
  • Sun, Yuan-Zhang
  • Cheng, Lin

Abstract

A short-term electricity market is usually composed of the energy market and ancillary service market. However, wind power is not allowed to be traded in ancillary service markets although it has been proven technically feasible to be regulation services. This paper aims to explore the market potential of trading wind power as regulation services in the California electricity market. A model for wind power trade in the day-ahead (DA) market is established considering the uncertainties of market prices and wind power. An optimal trading strategy for wind power producers is derived by using an analytical algorithm. Trading wind power as regulation is tested by using actual data and the impacts of market control on the market outcome are discussed. The results show that, based on the current framework of the California electricity market, wind power producers can earn much more money if they bid in the DA energy and regulation markets than if they only bid in the DA energy market. The results also show that the potential to enhance profit for wind power producers is larger in the regulation down market than in the regulation up market.

Suggested Citation

  • Zhang, Zhao-Sui & Sun, Yuan-Zhang & Cheng, Lin, 2013. "Potential of trading wind power as regulation services in the California short-term electricity market," Energy Policy, Elsevier, vol. 59(C), pages 885-897.
    • Handle: RePEc:eee:enepol:v:59:y:2013:i:c:p:885-897
    DOI: 10.1016/j.enpol.2013.04.056
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    References listed on IDEAS

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    1. 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.
    2. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
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    Cited by:

    1. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    2. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    3. Kiumars Rahmani & Mehrdad S Nazar, 2017. "Coordinated bidding of wind and thermal energy in joint energy and reserve markets of Spain by considering the uncertainties," Energy & Environment, , vol. 28(8), pages 846-869, December.
    4. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    5. Jenny Winkler & Rouven Emmerich & Mario Ragwitz & Benjamin Pfluger & Christian Senft, 2017. "Beyond the day-ahead market – effects of revenue maximisation of the marketing of renewables on electricity markets," Energy & Environment, , vol. 28(1-2), pages 110-144, March.
    6. Banshwar, Anuj & Sharma, Naveen Kumar & Sood, Yog Raj & Shrivastava, Rajnish, 2018. "An international experience of technical and economic aspects of ancillary services in deregulated power industry: Lessons for emerging BRIC electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 774-801.
    7. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    8. Hu, Jianming & Wang, Jianzhou & Xiao, Liqun, 2017. "A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts," Renewable Energy, Elsevier, vol. 114(PB), pages 670-685.
    9. Wei Sun & Qi Gao, 2019. "Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model," Energies, MDPI, vol. 12(12), pages 1-27, June.
    10. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    11. Hu, Junfeng & Yan, Qingyou & Kahrl, Fredrich & Liu, Xu & Wang, Peng & Lin, Jiang, 2021. "Evaluating the ancillary services market for large-scale renewable energy integration in China's northeastern power grid," Utilities Policy, Elsevier, vol. 69(C).
    12. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
    13. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    14. Banshwar, Anuj & Sharma, Naveen Kumar & Sood, Yog Raj & Shrivastava, Rajnish, 2017. "Market based procurement of energy and ancillary services from Renewable Energy Sources in deregulated environment," Renewable Energy, Elsevier, vol. 101(C), pages 1390-1400.

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