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Designing an Index for Assessing Wind Energy Potential

Author

Listed:
  • Matthias Ritter
  • Zhiwei Shen
  • Brenda López Cabrera
  • Martin Odening
  • Lars Deckert

Abstract

To meet the increasing global demand for renewable energy such as wind energy, more and more new wind parks are installed worldwide. Finding a suitable location, however, requires a detailed and often costly analysis of the local wind conditions. Plain average wind speed maps cannot provide a precise forecast of wind power because of the non-linear relationship between wind speed and production. In this paper, we suggest a new approach of assessing the local wind energy potential: Meteorological reanalysis data are applied to obtain long-term low-scale wind speed data at turbine location and hub height; then, with actual high-frequency production data, the relation between wind data and energy production is determined via a five parameter logistic function. The resulting wind energy index allows for a turbine-specific estimation of the expected wind power at an unobserved location. A map of wind power potential for whole Germany exemplifies the approach.

Suggested Citation

  • Matthias Ritter & Zhiwei Shen & Brenda López Cabrera & Martin Odening & Lars Deckert, 2014. "Designing an Index for Assessing Wind Energy Potential," SFB 649 Discussion Papers SFB649DP2014-052, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2014-052
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    References listed on IDEAS

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    1. Kubik, M.L. & Coker, P.J. & Barlow, J.F. & Hunt, C., 2013. "A study into the accuracy of using meteorological wind data to estimate turbine generation output," Renewable Energy, Elsevier, vol. 51(C), pages 153-158.
    2. Caporin, Massimiliano & Preś, Juliusz, 2012. "Modelling and forecasting wind speed intensity for weather risk management," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3459-3476.
    3. Kotroni, V. & Lagouvardos, K. & Lykoudis, S., 2014. "High-resolution model-based wind atlas for Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 479-489.
    4. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    5. Kubik, M.L. & Brayshaw, D.J. & Coker, P.J. & Barlow, J.F., 2013. "Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland," Renewable Energy, Elsevier, vol. 57(C), pages 558-561.
    6. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
    7. Sanchez, Ismael, 2006. "Short-term prediction of wind energy production," International Journal of Forecasting, Elsevier, vol. 22(1), pages 43-56.
    8. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
    9. Chang, Tsang-Jung & Wu, Yu-Ting & Hsu, Hua-Yi & Chu, Chia-Ren & Liao, Chun-Min, 2003. "Assessment of wind characteristics and wind turbine characteristics in Taiwan," Renewable Energy, Elsevier, vol. 28(6), pages 851-871.
    10. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
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    Citations

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    Cited by:

    1. Murthy, K.S.R. & Rahi, O.P., 2016. "Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India," Renewable Energy, Elsevier, vol. 99(C), pages 1137-1145.
    2. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, Open Access Journal, vol. 10(3), pages 1-16, March.
    3. Awdesch Melzer & Wolfgang K. Härdle & Brenda López Cabrera, 2017. "Pricing Green Financial Products," SFB 649 Discussion Papers SFB649DP2017-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Ritter, Matthias & Deckert, Lars, 2017. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," Applied Energy, Elsevier, vol. 185(P2), pages 1087-1099.
    5. repec:eee:energy:v:147:y:2018:i:c:p:1092-1107 is not listed on IDEAS
    6. repec:eee:appene:v:238:y:2019:i:c:p:1179-1191 is not listed on IDEAS
    7. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    8. repec:eee:eneeco:v:72:y:2018:i:c:p:542-557 is not listed on IDEAS
    9. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    10. repec:gam:jeners:v:10:y:2017:i:8:p:1229-:d:108869 is not listed on IDEAS
    11. repec:eee:renene:v:119:y:2018:i:c:p:777-786 is not listed on IDEAS
    12. repec:eee:renene:v:133:y:2019:i:c:p:1468-1478 is not listed on IDEAS
    13. Tajeddin, Alireza & Fazelpour, Farivar, 2016. "Towards realistic design of wind dams: An innovative approach to enhance wind potential," Applied Energy, Elsevier, vol. 182(C), pages 282-298.

    More about this item

    Keywords

    Wind power; energy production; renewable energy; onshore wind; MERRA;

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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