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Measuring the long run technical efficiency of offshore wind farms

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  • Benini, Giacomo
  • Cattani, Gilles

Abstract

Offshore wind energy has emerged as an attractive alternative to conventional resources to meet the Paris agreement commitment. The present paper studies the long run capacity of offshore wind farms to transform kinetic energy into electricity. We start estimating the technical efficiency of twenty-six farms over a thirteen years interval using a fully parametric and a semiparametric stochastic frontier model. The latter allows the factors of production to impact non-linearly on the quantity of electricity produced, those reducing the possibility of committing a functional misspecification error. Our results suggest that fully parametric specifications fails to identify the non-linear effect of labour cost on volumes of electricity produced. Then, we regress the estimated technical efficiency over the farm age, while controlling for the technological change of the wind power industry, to single out the resilience of the technical efficiency to aging. According to our calculations, technical efficiency ranges from 83% to 98% and it does not decline with age. This result shades light on the capacity of offshore wind farms to be a long term solution of the energy transition.

Suggested Citation

  • Benini, Giacomo & Cattani, Gilles, 2022. "Measuring the long run technical efficiency of offshore wind farms," Applied Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:appene:v:308:y:2022:i:c:s0306261921014835
    DOI: 10.1016/j.apenergy.2021.118218
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    1. Bosch, Jonathan & Staffell, Iain & Hawkes, Adam D., 2019. "Global levelised cost of electricity from offshore wind," Energy, Elsevier, vol. 189(C).
    2. Costoya, X. & deCastro, M. & Carvalho, D. & Gómez-Gesteira, M., 2020. "On the suitability of offshore wind energy resource in the United States of America for the 21st century," Applied Energy, Elsevier, vol. 262(C).
    3. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    4. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
    5. Ederer, Nikolaus, 2015. "Evaluating capital and operating cost efficiency of offshore wind farms: A DEA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1034-1046.
    6. Aldersey-Williams, John & Broadbent, Ian D. & Strachan, Peter A., 2019. "Better estimates of LCOE from audited accounts – A new methodology with examples from United Kingdom offshore wind and CCGT," Energy Policy, Elsevier, vol. 128(C), pages 25-35.
    7. Pestana Barros, Carlos & Sequeira Antunes, Olinda, 2011. "Performance assessment of Portuguese wind farms: Ownership and managerial efficiency," Energy Policy, Elsevier, vol. 39(6), pages 3055-3063, June.
    8. Wen, Yi & Kamranzad, Bahareh & Lin, Pengzhi, 2021. "Assessment of long-term offshore wind energy potential in the south and southeast coasts of China based on a 55-year dataset," Energy, Elsevier, vol. 224(C).
    9. Myhr, Anders & Bjerkseter, Catho & Ågotnes, Anders & Nygaard, Tor A., 2014. "Levelised cost of energy for offshore floating wind turbines in a life cycle perspective," Renewable Energy, Elsevier, vol. 66(C), pages 714-728.
    10. Akbari, Negar & Jones, Dylan & Treloar, Richard, 2020. "A cross-European efficiency assessment of offshore wind farms: A DEA approach," Renewable Energy, Elsevier, vol. 151(C), pages 1186-1195.
    11. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    12. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    13. Dismukes, David E. & Upton, Gregory B., 2015. "Economies of scale, learning effects and offshore wind development costs," Renewable Energy, Elsevier, vol. 83(C), pages 61-66.
    14. Konstantinos Giannakas & Kien C. Tran & Vangelis Tzouvelekas, 2003. "On the choice of functional form in stochastic frontier modeling," Empirical Economics, Springer, vol. 28(1), pages 75-100, January.
    15. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    16. 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.
    17. Schweizer, Joerg & Antonini, Alessandro & Govoni, Laura & Gottardi, Guido & Archetti, Renata & Supino, Enrico & Berretta, Claudia & Casadei, Carlo & Ozzi, Claudia, 2016. "Investigating the potential and feasibility of an offshore wind farm in the Northern Adriatic Sea," Applied Energy, Elsevier, vol. 177(C), pages 449-463.
    18. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    19. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    20. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    21. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    22. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    23. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
    24. Soares, Pedro M.M. & Lima, Daniela C.A. & Cardoso, Rita M. & Nascimento, Manuel L. & Semedo, Alvaro, 2017. "Western Iberian offshore wind resources: More or less in a global warming climate?," Applied Energy, Elsevier, vol. 203(C), pages 72-90.
    25. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2021. "Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis," Energies, MDPI, vol. 14(4), pages 1-25, February.
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    Cited by:

    1. Davide Astolfi & Ravi Pandit & Ludovico Terzi & Andrea Lombardi, 2022. "Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis," Energies, MDPI, vol. 15(15), pages 1-17, July.
    2. Dario Maradin & Bojana Olgić Draženović & Saša Čegar, 2023. "The Efficiency of Offshore Wind Energy Companies in the European Countries: A DEA Approach," Energies, MDPI, vol. 16(9), pages 1-16, April.
    3. Davide Astolfi & Ravi Pandit, 2022. "Wind Turbine Performance Decline with Age," Energies, MDPI, vol. 15(14), pages 1-4, July.

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    More about this item

    Keywords

    Wind; Power industry; Technical efficiency; Stochastic Frontier Analysis; Age;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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