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Benchmarking Wind Farm Projects by Means of Series Two-Stage DEA

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  • Ioannis E. Tsolas

    (School of Applied Mathematics and Physics, National Technical University of Athens, 15780 Athens, Greece)

Abstract

This paper presents a data envelopment analysis (DEA) approach to benchmark a group of wind farm (WF) projects in Greece by employing a series two-stage structure. In the first stage, the investment performance of projects is evaluated using contract data and site wind conditions, though in the second stage the WF operational efficiency is evaluated using data on production inputs and output. Inefficiency occurs in both the construction and operating stages, but the construction process appears to be more inefficient relative to the operating phase. Moreover, WF size is related to operating efficiency and sensitivity analysis results identify wind speed and WF installation capacity as the factors that affect the investment performance and operational efficiency, respectively. The proposed approach is an addition to the existing literature and it can be used by managers and facility operators.

Suggested Citation

  • Ioannis E. Tsolas, 2020. "Benchmarking Wind Farm Projects by Means of Series Two-Stage DEA," Clean Technol., MDPI, vol. 2(3), pages 1-12, September.
    • Handle: RePEc:gam:jcltec:v:2:y:2020:i:3:p:22-376:d:408726
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    References listed on IDEAS

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    1. Iribarren, Diego & Martín-Gamboa, Mario & Dufour, Javier, 2013. "Environmental benchmarking of wind farms according to their operational performance," Energy, Elsevier, vol. 61(C), pages 589-597.
    2. 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.
    3. 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.
    4. 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.
    5. Rodríguez, Xosé A. & Regueiro, Rosa M. & Doldán, Xoán R., 2020. "Analysis of productivity in the Spanish wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    6. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    7. Rajiv D. Banker & Richard C. Morey, 1986. "Efficiency Analysis for Exogenously Fixed Inputs and Outputs," Operations Research, INFORMS, vol. 34(4), pages 513-521, August.
    8. Sarıca, Kemal & Or, Ilhan, 2007. "Efficiency assessment of Turkish power plants using data envelopment analysis," Energy, Elsevier, vol. 32(8), pages 1484-1499.
    9. Jradi, Samah & Ruggiero, John, 2019. "Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier," European Journal of Operational Research, Elsevier, vol. 278(2), pages 385-393.
    10. Chiang Kao, 2014. "Efficiency Decomposition in Network Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 55-77, Springer.
    11. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    12. Dadi Wang, 2017. "Benchmarking the Performance of Solar Installers and Rooftop Photovoltaic Installations in California," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    13. 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.
    14. 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.
    15. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, September.
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    Cited by:

    1. Dorota Kuchta, 2023. "Project implementation scenario selection for sustainable project and product lifecycle management. Application of network data envelopment analysis," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(4), pages 133-154.
    2. Zhang, Zumeng & Ding, Liping & Wang, Chaofan & Dai, Qiyao & Shi, Yin & Zhao, Yujia & Zhu, Yuxuan, 2022. "Do operation and maintenance contracts help photovoltaic poverty alleviation power stations perform better?," Energy, Elsevier, vol. 259(C).

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