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Wind Forecast at Medium Voltage Distribution Networks

Author

Listed:
  • Herbert Amezquita

    (Department of Electrical and Computer Engineering, Instituto Superior Técnico—IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, 1000-029 Lisboa, Portugal)

  • Pedro M. S. Carvalho

    (Department of Electrical and Computer Engineering, Instituto Superior Técnico—IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, 1000-029 Lisboa, Portugal)

  • Hugo Morais

    (Department of Electrical and Computer Engineering, Instituto Superior Técnico—IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, 1000-029 Lisboa, Portugal)

Abstract

Due to the intermittent and variable nature of wind, Wind Power Generation Forecast (WPGF) has become an essential task for power system operators who are looking for reliable wind penetration into the electric grid. Since there is a need to forecast wind power generation accurately, the main contribution of this paper is the development, implementation, and comparison of WPGF methods in a framework to be used by distribution system operators (DSOs). The methodology applied comprised five stages: pre-processing, feature selection, forecasting models, post-processing, and validation, using the historical wind power generation data (measured at secondary substations) of 20 wind farms connected to the medium voltage (MV) distribution network in Portugal. After comparing the accuracy of eight different models in terms of their relative root mean square error (RRMSE), extreme gradient boosting (XGBOOST) appeared as the best-suited forecasting method for wind power generation. The best average RRMSE achieved by the proposed XGBOOST model for 1-year training (January–December of 2020) and 6 months forecast (January–June of 2021) corresponds to 13.48%, outperforming the predictions of the Portuguese DSO by 20%.

Suggested Citation

  • Herbert Amezquita & Pedro M. S. Carvalho & Hugo Morais, 2023. "Wind Forecast at Medium Voltage Distribution Networks," Energies, MDPI, vol. 16(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2887-:d:1102987
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    References listed on IDEAS

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    1. Silvia Micheli, 2020. "Policy Strategy Cooperation in the 2030 Climate and Energy Policy Framework," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 48(2), pages 265-267, June.
    2. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    3. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Amin, Muhammad Yasir, 2020. "Solar and wind power generation systems with pumped hydro storage: Review and future perspectives," Renewable Energy, Elsevier, vol. 148(C), pages 176-192.
    4. Eduardo Machado & Tiago Pinto & Vanessa Guedes & Hugo Morais, 2021. "Electrical Load Demand Forecasting Using Feed-Forward Neural Networks," Energies, MDPI, vol. 14(22), pages 1-24, November.
    5. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    6. Gallego, C. & Pinson, P. & Madsen, H. & Costa, A. & Cuerva, A., 2011. "Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting," Applied Energy, Elsevier, vol. 88(11), pages 4087-4096.
    7. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    8. Nenad Sijakovic & Aleksandar Terzic & Georgios Fotis & Ioannis Mentis & Magda Zafeiropoulou & Theodoros I. Maris & Emmanouil Zoulias & Charalambos Elias & Vladan Ristic & Vasiliki Vita, 2022. "Active System Management Approach for Flexibility Services to the Greek Transmission and Distribution System," Energies, MDPI, vol. 15(17), pages 1-31, August.
    9. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
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    Cited by:

    1. António J. Arsénio Costa & Hugo Morais, 2024. "Power Quality Control Using Superconducting Magnetic Energy Storage in Power Systems with High Penetration of Renewables: A Review of Systems and Applications," Energies, MDPI, vol. 17(23), pages 1-32, November.

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