IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i14p2765-d249651.html
   My bibliography  Save this article

Changing the Day-Ahead Gate Closure to Wind Power Integration: A Simulation-Based Study

Author

Listed:
  • Hugo Algarvio

    (LNEG–National Laboratory of Energy and Geology, 1649-038 Lisbon, Portugal
    Instituto Superior Técnico, 1049-001 Lisbon, Portugal)

  • António Couto

    (LNEG–National Laboratory of Energy and Geology, 1649-038 Lisbon, Portugal)

  • Fernando Lopes

    (LNEG–National Laboratory of Energy and Geology, 1649-038 Lisbon, Portugal)

  • Ana Estanqueiro

    (LNEG–National Laboratory of Energy and Geology, 1649-038 Lisbon, Portugal)

Abstract

Currently, in most European electricity markets, power bids are based on forecasts performed 12 to 36 hours ahead. Actual wind power forecast systems still lead to large errors, which may strongly impact electricity market outcomes. Accordingly, this article analyzes the impact of the wind power forecast uncertainty and the change of the day-ahead market gate closure on both the market-clearing prices and the outcomes of the balancing market. To this end, it presents a simulation-based study conducted with the help of an agent-based tool, called MATREM. The results support the following conclusion: a change in the gate closure to a time closer to real-time operation is beneficial to market participants and the energy system generally.

Suggested Citation

  • Hugo Algarvio & António Couto & Fernando Lopes & Ana Estanqueiro, 2019. "Changing the Day-Ahead Gate Closure to Wind Power Integration: A Simulation-Based Study," Energies, MDPI, vol. 12(14), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2765-:d:249651
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/14/2765/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/14/2765/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Roques, Fabien & Finon, Dominique, 2017. "Adapting electricity markets to decarbonisation and security of supply objectives: Toward a hybrid regime?," Energy Policy, Elsevier, vol. 105(C), pages 584-596.
    3. Amanda Lenzi & Ingelin Steinsland & Pierre Pinson, 2018. "Benefits of spatiotemporal modeling for short‐term wind power forecasting at both individual and aggregated levels," Environmetrics, John Wiley & Sons, Ltd., vol. 29(3), May.
    4. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.
    5. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, April.
    6. Soukissian, Takvor H. & Papadopoulos, Anastasios, 2015. "Effects of different wind data sources in offshore wind power assessment," Renewable Energy, Elsevier, vol. 77(C), pages 101-114.
    7. Hugo Algarvio & Fernando Lopes & António Couto & João Santana & Ana Estanqueiro, 2019. "Effects of regulating the European Internal Market on the integration of variable renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(6), November.
    8. 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.
    9. Hugo Algarvio & Fernando Lopes & António Couto & Ana Estanqueiro, 2019. "Participation of wind power producers in day‐ahead and balancing markets: An overview and a simulation‐based study," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(5), September.
    10. Moiseeva, Ekaterina & Wogrin, Sonja & Hesamzadeh, Mohammad Reza, 2017. "Generation flexibility in ramp rates: Strategic behavior and lessons for electricity market design," European Journal of Operational Research, Elsevier, vol. 261(2), pages 755-771.
    11. Vilim, Michael & Botterud, Audun, 2014. "Wind power bidding in electricity markets with high wind penetration," Applied Energy, Elsevier, vol. 118(C), pages 141-155.
    12. Zhang, Jie & Cui, Mingjian & Hodge, Bri-Mathias & Florita, Anthony & Freedman, Jeffrey, 2017. "Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales," Energy, Elsevier, vol. 122(C), pages 528-541.
    13. Chen Zhang & Wei Yan, 2019. "Spot Market Mechanism Design for the Electricity Market in China Considering the Impact of a Contract Market," Energies, MDPI, vol. 12(6), pages 1-23, March.
    14. Alessandrini, S. & Sperati, S. & Pinson, P., 2013. "A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data," Applied Energy, Elsevier, vol. 107(C), pages 271-280.
    15. Klaus Skytte & Lucien Bobo, 2019. "Increasing the value of wind: From passive to active actors in multiple power markets," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(3), May.
    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. Hugo Algarvio & Fernando Lopes & António Couto & Ana Estanqueiro & João Santana, 2019. "Variable Renewable Energy and Market Design: New Products and a Real-World Study," Energies, MDPI, vol. 12(23), pages 1-17, November.

    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. Hugo Algarvio & Fernando Lopes & António Couto & João Santana & Ana Estanqueiro, 2019. "Effects of regulating the European Internal Market on the integration of variable renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(6), November.
    2. Hugo Algarvio & Fernando Lopes & António Couto & Ana Estanqueiro & João Santana, 2019. "Variable Renewable Energy and Market Design: New Products and a Real-World Study," Energies, MDPI, vol. 12(23), pages 1-17, November.
    3. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
    4. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).
    5. Marino Marrocu & Luca Massidda, 2017. "A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast," Energies, MDPI, vol. 10(12), pages 1-14, November.
    6. Ziel, Florian & Croonenbroeck, Carsten & Ambach, Daniel, 2016. "Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity," Applied Energy, Elsevier, vol. 177(C), pages 285-297.
    7. Joos, Michael & Staffell, Iain, 2018. "Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 45-65.
    8. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    9. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    10. Sewdien, V.N. & Preece, R. & Torres, J.L. Rueda & Rakhshani, E. & van der Meijden, M., 2020. "Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting," Renewable Energy, Elsevier, vol. 161(C), pages 878-892.
    11. Lucheroni, Carlo & Boland, John & Ragno, Costantino, 2019. "Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models," Applied Energy, Elsevier, vol. 239(C), pages 1226-1241.
    12. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    13. 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.
    14. Peter D. Lund & John Byrne, 2020. "Little time left to reverse emissions—Growing hope despite disappointing CO2 trend," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(1), January.
    15. Hu, Jianming & Zhang, Liping & Tang, Jingwei & Liu, Zhi, 2023. "A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting," Energy, Elsevier, vol. 280(C).
    16. Fatras, Nicolas & Ma, Zheng & Duan, Hongbo & Jørgensen, Bo Nørregaard, 2022. "A systematic review of electricity market liberalisation and its alignment with industrial consumer participation: A comparison between the Nordics and China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    17. Chen, Shu-Hua & Yang, Shu-Chih & Chen, Chih-Ying & van Dam, C.P. & Cooperman, Aubryn & Shiu, Henry & MacDonald, Clinton & Zack, John, 2019. "Application of bias corrections to improve hub-height ensemble wind forecasts over the Tehachapi Wind Resource Area," Renewable Energy, Elsevier, vol. 140(C), pages 281-291.
    18. Yasemin Merzifonluoglu & Eray Uzgoren, 2018. "Photovoltaic power plant design considering multiple uncertainties and risk," Annals of Operations Research, Springer, vol. 262(1), pages 153-184, March.
    19. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(10), pages 1-28, September.
    20. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.

    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:gam:jeners:v:12:y:2019:i:14:p:2765-:d:249651. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.