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

Author

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  • 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
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    References listed on IDEAS

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    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, September.
    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.
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    Cited by:

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