IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v59y2013icp885-897.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421513003091
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2013.04.056?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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).
    13. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thao Pham & Killian Lemoine, 2020. "Impacts of subsidized renewable electricity generation on spot market prices in Germany : Evidence from a GARCH model with panel data," Working Papers hal-02568268, HAL.
    2. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    3. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    4. Vijay, Avinash & Fouquet, Nicolas & Staffell, Iain & Hawkes, Adam, 2017. "The value of electricity and reserve services in low carbon electricity systems," Applied Energy, Elsevier, vol. 201(C), pages 111-123.
    5. González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
    6. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2013. "Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities," Applied Energy, Elsevier, vol. 101(C), pages 363-375.
    7. Tryggvi Jónsson & Pierre Pinson & Henrik Madsen & Henrik Aalborg Nielsen, 2014. "Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression," Energies, MDPI, vol. 7(9), pages 1-25, August.
    8. McConnell, Dylan & Hearps, Patrick & Eales, Dominic & Sandiford, Mike & Dunn, Rebecca & Wright, Matthew & Bateman, Lachlan, 2013. "Retrospective modeling of the merit-order effect on wholesale electricity prices from distributed photovoltaic generation in the Australian National Electricity Market," Energy Policy, Elsevier, vol. 58(C), pages 17-27.
    9. Jichun Liu & Yangfang Yang & Yue Xiang & Junyong Liu, 2019. "A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads," Energies, MDPI, vol. 12(6), pages 1-17, March.
    10. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    11. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    12. Diongue, Abdou Kâ & Guégan, Dominique & Vignal, Bertrand, 2009. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Applied Energy, Elsevier, vol. 86(4), pages 505-510, April.
    13. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).
    14. Durmaz, Tunç, 2016. "Precautionary Storage in Electricity Markets," Discussion Papers 2016/5, Norwegian School of Economics, Department of Business and Management Science.
    15. Sinha, Pankaj & Mathur, Kritika, 2016. "Empirical Analysis of Developments in the Day Ahead Electricity Markets in India," MPRA Paper 72969, University Library of Munich, Germany.
    16. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    17. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    18. Marie Bessec & Julien Fouquau & Sophie Meritet, 2016. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Applied Economics, Taylor & Francis Journals, vol. 48(5), pages 361-378, January.
    19. repec:dui:wpaper:1504 is not listed on IDEAS
    20. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
    21. Elberg, Christina & Hagspiel, Simeon, 2015. "Spatial dependencies of wind power and interrelations with spot price dynamics," European Journal of Operational Research, Elsevier, vol. 241(1), pages 260-272.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:59:y:2013:i:c:p:885-897. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.