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Efficiency of Wind Power Production and its Determinants

Author

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  • Pieralli, Simone
  • Ritter, Matthias
  • Odening, Martin

Abstract

This article examines the efficiency of wind energy production. Using non-convex efficiency analysis, we quantify production losses for 19 wind turbines in four wind parks across Germany. In a second stage regression, we adapt the linear regression results of Kneip, Simar, and Wilson (2014) to explain electricity losses by means of a bias-corrected truncated regression analysis. The results show that electricity losses amount to 27% of the maximal producible electricity. Most of these losses are from changing wind conditions, while 6% are from turbine errors.

Suggested Citation

  • Pieralli, Simone & Ritter, Matthias & Odening, Martin, 2015. "Efficiency of Wind Power Production and its Determinants," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205415, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
  • Handle: RePEc:ags:aaea15:205415
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    File URL: http://purl.umn.edu/205415
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    References listed on IDEAS

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    1. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2015. "When Bias Kills The Variance: Central Limit Theorems For Dea And Fdh Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 31(02), pages 394-422, April.
    2. Iglesias, Guillermo & Castellanos, Pablo & Seijas, Amparo, 2010. "Measurement of productive efficiency with frontier methods: A case study for wind farms," Energy Economics, Elsevier, vol. 32(5), pages 1199-1208, September.
    3. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    4. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    5. Iribarren, Diego & Vázquez-Rowe, Ian & Rugani, Benedetto & Benetto, Enrico, 2014. "On the feasibility of using emergy analysis as a source of benchmarking criteria through data envelopment analysis: A case study for wind energy," Energy, Elsevier, vol. 67(C), pages 527-537.
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    Cited by:

    1. Matthias Ritter & Simone Pieralli & HMartin Odening, 2016. "Neighborhood Effects in Wind Farm Performance: An Econometric Approach," SFB 649 Discussion Papers SFB649DP2016-012, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. repec:gam:jeners:v:10:y:2017:i:10:p:1626-:d:115343 is not listed on IDEAS
    3. 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).
    4. 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.

    More about this item

    Keywords

    wind energy; efficiency; free disposal hull; bias correction; Environmental Economics and Policy; Production Economics; Productivity Analysis; Research Methods/ Statistical Methods; Resource /Energy Economics and Policy; D20; D21; Q42;

    JEL classification:

    • D20 - Microeconomics - - Production and Organizations - - - General
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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