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

Abstract

Offshore wind energy is recognized as an important source of renewable energy and has experienced a rapid growth in recent years especially in northwestern European countries. In this paper the efficiency of 71 offshore wind farms across five northwestern 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.

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“…Lu et al [17] discussed the operation performance of lathe industry in Taiwan; input variables are fixed asset, number of employees, and R&D expense, and the output variable is business revenue. Akbari et al [18] discussed the operation performance of offshore wind power plants in Europe. Their input variables are number of turbines, cost, distance to shore, and area; output variables are connectivity, generated electricity, and water depth.…”

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“…After completing the simulation, the program passed the data on to the genetic algorithm to calculate the value of the objective function f 2 (12). The range of current values was limited by the penalty function f c (I) in the form (13), which has been included in function f 2 .…”

“…Development in a given location is determined mainly by wind conditions [1][2][3][4][5][6][7][8][9][10]. Most wind farms are built in coastal areas and on continental shelves [10][11][12][13][14][15][16][17][18]. Inland, wind conditions, being determined by various topographical and other factors, vary greatly [1,11,[19][20][21][22].…”

Active power optimisation for wind farms under generator inter‐turn short‐circuit fault

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