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A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

Author

Listed:
  • Ceyhun Yıldız

    (Department of Electric and Energy, Elbistan Vocational School, University of K.Maraş Sütçü İmam, K.Maraş 46340, Turkey)

  • Mustafa Tekin

    (Department of Electrical and Electronics, Faculty of Engineering, University of K.Maraş Sütçü İmam, K.Maraş 46040, Turkey)

  • Ahmet Gani

    (Department of Electrical and Electronics, Faculty of Engineering, University of K.Maraş Sütçü İmam, K.Maraş 46040, Turkey)

  • Ö. Fatih Keçecioğlu

    (Department of Electrical and Electronics, Faculty of Engineering, University of K.Maraş Sütçü İmam, K.Maraş 46040, Turkey)

  • Hakan Açıkgöz

    (Department of Electrical Science, Kilis 7 Aralik University, Kilis 79000, Turkey)

  • Mustafa Şekkeli

    (Department of Electrical and Electronics, Faculty of Engineering, University of K.Maraş Sütçü İmam, K.Maraş 46040, Turkey)

Abstract

During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission system operators and wind power plant owners. To solve this problem, numerous wind power forecast systems have been developed. Unfortunately none of them can obtain absolutely accurate forecasts yet. Thus, researchers assumed that wind power generation is a stochastic process and they proposed a stochastic programming approach to solve problems arising from the uncertainty of wind power. It is well known that representing stochastic process by possible scenarios is a major issue in the stochastic programming approach. Large numbers of scenarios can represent a stochastic process accurately, but it is not easy to solve a stochastic problem that contains a large number of scenarios. For this reason scenario reduction methods have been introduced. Finally, the quality of this reduced scenario set must be at an acceptable level to use them in calculations. All of these reasons have encouraged authors to develop a wind power scenario tool that can generate and reduce the scenario set and test the quality of it. The developed tool uses historical data to model wind forecast errors. Scenarios are generated around 24 day-ahead point wind power forecasts. A fast forward reduction algorithm is used to reduce the scenario set. Two metrics are proposed to assess the quality of the reduced scenario set. Site measurements are used to test the developed wind power scenario tool. Results showed that the tool can generate and reduce the scenario set successfully and the proposed metrics are useful to assess the quality.

Suggested Citation

  • Ceyhun Yıldız & Mustafa Tekin & Ahmet Gani & Ö. Fatih Keçecioğlu & Hakan Açıkgöz & Mustafa Şekkeli, 2017. "A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool," Sustainability, MDPI, vol. 9(5), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:864-:d:99231
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    References listed on IDEAS

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    Cited by:

    1. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
    2. Julio César Cuenca Tinitana & Carlos Adrian Correa-Florez & Diego Patino & José Vuelvas, 2020. "Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty," Energies, MDPI, vol. 13(23), pages 1-26, December.
    3. Turk, Ana & Wu, Qiuwei & Zhang, Menglin & Østergaard, Jacob, 2020. "Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing," Energy, Elsevier, vol. 196(C).
    4. Yang, Mao & Shi, Chaoyu & Liu, Huiyu, 2021. "Day-ahead wind power forecasting based on the clustering of equivalent power curves," Energy, Elsevier, vol. 218(C).
    5. Zhou, Qingguo & Wang, Chen & Zhang, Gaofeng, 2019. "Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems," Applied Energy, Elsevier, vol. 250(C), pages 1559-1580.

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