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A cross-European efficiency assessment of offshore wind farms: A DEA approach

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  • Akbari, Negar
  • Jones, Dylan
  • Treloar, Richard

Abstract

Offshore wind energy is recognized as an important source of renewable energy and has experienced rapid growth in recent years especially in north-western European countries. In this paper, the efficiency of 71 offshore wind farms across five north-western European countries is assessed using the Data Envelopment Analysis (DEA) Method. The number of turbines, cost, distance to shore, and area of the wind farms are selected as the inputs and the connectivity to population centres, the produced electricity and the water depth are considered as the outputs. The results show that the average CCR efficiency score of all offshore wind farms considered in this study is 87%, and the relative median efficiency of offshore wind farms in different countries is not statistically different. This study offers a practical and holistic performance assessment to the offshore wind stakeholders and policy makers via including economic, environmental, technical and social inputs and outputs in the analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:151:y:2020:i:c:p:1186-1195
    DOI: 10.1016/j.renene.2019.11.130
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    5. Abramic, A. & García Mendoza, A. & Haroun, R., 2021. "Introducing offshore wind energy in the sea space: Canary Islands case study developed under Maritime Spatial Planning principles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
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    8. 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.
    9. Bartłomiej Kizielewicz & Jarosław Wątróbski & Wojciech Sałabun, 2020. "Identification of Relevant Criteria Set in the MCDA Process—Wind Farm Location Case Study," Energies, MDPI, vol. 13(24), pages 1-40, December.
    10. Han, Yongming & Lou, Xiaoyi & Feng, Mingfei & Geng, Zhiqiang & Chen, Liangchao & Ping, Weiying & Lu, Gang, 2022. "Energy consumption analysis and saving of buildings based on static and dynamic input-output models," Energy, Elsevier, vol. 239(PC).
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