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A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations

Author

Listed:
  • Jussi Ekström

    (Department of Electrical Engineering and Automation, Aalto University, FI-00076 AALTO, 02150 Espoo, Finland)

  • Matti Koivisto

    (Department of Wind Energy, Technical University of Denmark (DTU), 4000 Roskilde, Denmark)

  • Ilkka Mellin

    (Department of Mathematics and Systems Analysis, Aalto University, FI-00076 AALTO, 02150 Espoo, Finland)

  • Robert John Millar

    (Department of Electrical Engineering and Automation, Aalto University, FI-00076 AALTO, 02150 Espoo, Finland)

  • Matti Lehtonen

    (Department of Electrical Engineering and Automation, Aalto University, FI-00076 AALTO, 02150 Espoo, Finland)

Abstract

In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.

Suggested Citation

  • Jussi Ekström & Matti Koivisto & Ilkka Mellin & Robert John Millar & Matti Lehtonen, 2018. "A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations," Energies, MDPI, vol. 11(9), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2442-:d:169796
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    References listed on IDEAS

    as
    1. Goić, R. & Krstulović, J. & Jakus, D., 2010. "Simulation of aggregate wind farm short-term production variations," Renewable Energy, Elsevier, vol. 35(11), pages 2602-2609.
    2. Ekström, Jussi & Koivisto, Matti & Mellin, Ilkka & Millar, John & Saarijärvi, Eero & Haarla, Liisa, 2015. "Assessment of large scale wind power generation with new generation locations without measurement data," Renewable Energy, Elsevier, vol. 83(C), pages 362-374.
    3. González-Longatt, F. & Wall, P. & Terzija, V., 2012. "Wake effect in wind farm performance: Steady-state and dynamic behavior," Renewable Energy, Elsevier, vol. 39(1), pages 329-338.
    4. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
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    Cited by:

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    2. Irina Meghea, 2023. "Comparison of Statistical Production Models for a Solar and a Wind Power Plant," Mathematics, MDPI, vol. 11(5), pages 1-16, February.
    3. Paula Medina Maçaira & Yasmin Monteiro Cyrillo & Fernando Luiz Cyrino Oliveira & Reinaldo Castro Souza, 2019. "Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning," Energies, MDPI, vol. 12(5), pages 1-20, March.
    4. Reinhold Lehneis & Daniela Thrän, 2023. "Temporally and Spatially Resolved Simulation of the Wind Power Generation in Germany," Energies, MDPI, vol. 16(7), pages 1-16, April.

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