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Wind Power Generation Scheduling Accuracy in Europe: An Overview of ENTSO-E Countries

Author

Listed:
  • Henrik Zsiborács

    (Renewable Energy Research Group, University Center for Circular Economy, University of Pannonia Nagykanizsa, 8800 Nagykanizsa, Hungary)

  • Gábor Pintér

    (Renewable Energy Research Group, University Center for Circular Economy, University of Pannonia Nagykanizsa, 8800 Nagykanizsa, Hungary)

  • András Vincze

    (Renewable Energy Research Group, University Center for Circular Economy, University of Pannonia Nagykanizsa, 8800 Nagykanizsa, Hungary)

  • Nóra Hegedűsné Baranyai

    (Renewable Energy Research Group, University Center for Circular Economy, University of Pannonia Nagykanizsa, 8800 Nagykanizsa, Hungary)

Abstract

Despite the rapid spread of the use of wind energy to generate electricity, harnessing this energy source remains a great challenge due to its stochastic nature. One way of dealing with this is to prepare accurate wind power forecasts. This paper explored the accuracy of day-ahead and intraday scheduling of energy generation of the onshore and offshore wind farms of the member countries of the European Network of Transmission System Operators (ENTSO-E) in the period from 2013 to 2021. The precision of the scheduling activities showed a varying picture: the onshore wind farms of Germany, Spain, France, and Sweden produced more precise forecasts than others, with annual downward and upward regulatory needs between 0.8% and 14.4%, and from 0.8% to 6.5%, of the yearly energy generation, respectively. In certain countries, however, the forecasts were less accurate, with discrepancies exceeding 41% for downward and 132% for upward regulation. As for offshore wind farms, the annual downward and upward regulatory needs ranged between 0.9% and 61.7%, and from 1.3% to 44.1%, respectively, with Germany and Denmark producing the most accurate schedules. The innovative novelty and practical contributions of this study are that it determines and presents information related to the accuracy of the day-ahead and intraday wind power generation forecasting of the ENTSO-E countries, which is of practical relevance to the transmission system operators (TSOs), the main actors in the energy market and the decision-makers, too. This information may also help investors who invest in onshore and offshore wind farms with the economic aspects, and it may also greatly contribute to the market-related development of the management systems of energy storage solutions related to these technologies.

Suggested Citation

  • Henrik Zsiborács & Gábor Pintér & András Vincze & Nóra Hegedűsné Baranyai, 2022. "Wind Power Generation Scheduling Accuracy in Europe: An Overview of ENTSO-E Countries," Sustainability, MDPI, vol. 14(24), pages 1-58, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16446-:d:997686
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    References listed on IDEAS

    as
    1. Heard, B.P. & Brook, B.W. & Wigley, T.M.L. & Bradshaw, C.J.A., 2017. "Burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1122-1133.
    2. Papaefthymiou, Georgios & Haesen, Edwin & Sach, Thobias, 2018. "Power System Flexibility Tracker: Indicators to track flexibility progress towards high-RES systems," Renewable Energy, Elsevier, vol. 127(C), pages 1026-1035.
    3. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
    4. Al-Yahyai, Sultan & Charabi, Yassine & Gastli, Adel, 2010. "Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3192-3198, December.
    5. Nicolosi, Marco, 2010. "Wind power integration and power system flexibility-An empirical analysis of extreme events in Germany under the new negative price regime," Energy Policy, Elsevier, vol. 38(11), pages 7257-7268, November.
    6. Dhiman, Harsh S. & Deb, Dipankar & Guerrero, Josep M., 2019. "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 369-379.
    7. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    8. Zhang, Yachao & Le, Jian & Liao, Xiaobing & Zheng, Feng & Li, Yinghai, 2019. "A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing," Energy, Elsevier, vol. 168(C), pages 558-572.
    9. Laugs, Gideon A.H. & Benders, René M.J. & Moll, Henri C., 2020. "Balancing responsibilities: Effects of growth of variable renewable energy, storage, and undue grid interaction," Energy Policy, Elsevier, vol. 139(C).
    10. Gerard, Helena & Rivero Puente, Enrique Israel & Six, Daan, 2018. "Coordination between transmission and distribution system operators in the electricity sector: A conceptual framework," Utilities Policy, Elsevier, vol. 50(C), pages 40-48.
    11. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    12. Papaefthymiou, G. & Dragoon, Ken, 2016. "Towards 100% renewable energy systems: Uncapping power system flexibility," Energy Policy, Elsevier, vol. 92(C), pages 69-82.
    13. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
    14. Lago, Jesus & De Ridder, Fjo & Vrancx, Peter & De Schutter, Bart, 2018. "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration," Applied Energy, Elsevier, vol. 211(C), pages 890-903.
    15. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    16. Holttinen, H., 2005. "Optimal electricity market for wind power," Energy Policy, Elsevier, vol. 33(16), pages 2052-2063, November.
    17. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    18. Prebeg, Pero & Gasparovic, Goran & Krajacic, Goran & Duic, Neven, 2016. "Long-term energy planning of Croatian power system using multi-objective optimization with focus on renewable energy and integration of electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 1493-1507.
    19. Hadush, Samson Yemane & Meeus, Leonardo, 2018. "DSO-TSO cooperation issues and solutions for distribution grid congestion management," Energy Policy, Elsevier, vol. 120(C), pages 610-621.
    20. Hu, Shuai & Xiang, Yue & Zhang, Hongcai & Xie, Shanyi & Li, Jianhua & Gu, Chenghong & Sun, Wei & Liu, Junyong, 2021. "Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction," Applied Energy, Elsevier, vol. 293(C).
    21. Hodge, Bri-Mathias & Brancucci Martinez-Anido, Carlo & Wang, Qin & Chartan, Erol & Florita, Anthony & Kiviluoma, Juha, 2018. "The combined value of wind and solar power forecasting improvements and electricity storage," Applied Energy, Elsevier, vol. 214(C), pages 1-15.
    22. Olauson, Jon & Bergkvist, Mikael, 2015. "Modelling the Swedish wind power production using MERRA reanalysis data," Renewable Energy, Elsevier, vol. 76(C), pages 717-725.
    23. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
    24. Fang, Xin & Hodge, Bri-Mathias & Du, Ershun & Zhang, Ning & Li, Fangxing, 2018. "Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach," Applied Energy, Elsevier, vol. 230(C), pages 531-539.
    25. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    26. Hu, Xiao & Jaraitė, Jūratė & Kažukauskas, Andrius, 2021. "The effects of wind power on electricity markets: A case study of the Swedish intraday market," Energy Economics, Elsevier, vol. 96(C).
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