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Site assessment, turbine selection, and local feed-in tariffs through the wind energy index

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  • Ritter, Matthias
  • Deckert, Lars

Abstract

Since wind energy is rapidly growing, new wind farms are installed worldwide and a discussion is going on concerning the optimal political framework to promote this development. In this paper, we present a wind energy index, which is supportive for wind park planners, operators, and policy-makers. Based on long-term and low-scale reanalysis wind speed data from MERRA and true production data, it can predict the expected wind energy production for every location and turbine type. After an in-sample and out-of-sample evaluation of the index performance, it is applied to assess the wind energy potential of locations in Germany, to compare different turbine types and to derive the required compensation in terms of locally different feed-in tariffs. We show that in many parts of South Germany, profitability of new wind parks cannot be achieved given the current legal situation.

Suggested Citation

  • Ritter, Matthias & Deckert, Lars, 2015. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," SFB 649 Discussion Papers 2015-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2015-046
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    7. Nansheng Pang & Mengfan Nan & Qichen Meng & Siyang Zhao, 2021. "Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    8. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    9. Alina Wilke & Zhiwei Shen & Matthias Ritter, 2021. "How Much Can Small-Scale Wind Energy Production Contribute to Energy Supply in Cities? A Case Study of Berlin," Energies, MDPI, vol. 14(17), pages 1-20, September.
    10. Marinić-Kragić, Ivo & Vučina, Damir & Milas, Zoran, 2019. "Concept of flexible vertical-axis wind turbine with numerical simulation and shape optimization," Energy, Elsevier, vol. 167(C), pages 841-852.
    11. Henckes, Philipp & Knaut, Andreas & Obermüller, Frank & Frank, Christopher, 2018. "The benefit of long-term high resolution wind data for electricity system analysis," Energy, Elsevier, vol. 143(C), pages 934-942.
    12. Allen, D.J. & Tomlin, A.S. & Bale, C.S.E. & Skea, A. & Vosper, S. & Gallani, M.L., 2017. "A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data," Applied Energy, Elsevier, vol. 208(C), pages 1246-1257.
    13. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices," Applied Energy, Elsevier, vol. 238(C), pages 1179-1191.
    14. Hou, Jin & Xu, Peng & Lu, Xing & Pang, Zhihong & Chu, Yiyi & Huang, Gongsheng, 2018. "Implementation of expansion planning in existing district energy system: A case study in China," Applied Energy, Elsevier, vol. 211(C), pages 269-281.
    15. Olena Myrna & Martin Odening & Matthias Ritter, 2019. "The Influence of Wind Energy and Biogas on Farmland Prices," Land, MDPI, vol. 8(1), pages 1-14, January.
    16. Salcedo-Sanz, S. & García-Herrera, R. & Camacho-Gómez, C. & Aybar-Ruíz, A. & Alexandre, E., 2018. "Wind power field reconstruction from a reduced set of representative measuring points," Applied Energy, Elsevier, vol. 228(C), pages 1111-1121.
    17. Lu, Yuehong & Zhang, Xiao-Ping & Huang, Zhijia & Lu, Jinli & Wang, Dong, 2019. "Impact of introducing penalty-cost on optimal design of renewable energy systems for net zero energy buildings," Applied Energy, Elsevier, vol. 235(C), pages 106-116.
    18. Miguel Á. Rodríguez-López & Emilio Cerdá & Pablo del Rio, 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    19. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    20. Henckes, Philipp & Frank, Christopher & Küchler, Nils & Peter, Jakob & Wagner, Johannes, 2020. "Uncertainty estimation of investment planning models under high shares of renewables using reanalysis data," Energy, Elsevier, vol. 208(C).
    21. Ahmadpour, Ali & Mokaramian, Elham & Anderson, Simon, 2021. "The effects of the renewable energies penetration on the surplus welfare under energy policy," Renewable Energy, Elsevier, vol. 164(C), pages 1171-1182.
    22. 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).
    23. Yılmaz Balaman, Şebnem & Scott, James & Matopoulos, Aristides & Wright, Daniel G., 2019. "Incentivising bioenergy production: Economic and environmental insights from a regional optimization methodology," Renewable Energy, Elsevier, vol. 130(C), pages 867-880.
    24. 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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    More about this item

    Keywords

    wind power; renewable energy; onshore wind; MERRA; feed-in tariff;
    All these keywords.

    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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