Efficiency assessment of wind farms in China using two-stage data envelopment analysis

Wu, Yunna; Hu, Yong; Xiao, Xinli; Mao, Chunyu

doi:10.1016/j.enconman.2016.06.014

Cited by 61 publications

(32 citation statements)

References 36 publications

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“…Wu et al [12] apply a two stage DEA for efficiency assessment of 42 onshore wind farms in China. They use the installed capacity, electricity consumption and wind power density as the inputs and the generated electricity and availability as the outputs.…”

Section: Applications Of Dea In the Renewable Energy Sectormentioning

confidence: 99%

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

2020

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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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Section: Applications Of Dea In the Renewable Energy Sectormentioning

confidence: 99%

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

2020

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“…It should be noted that the series two-stage DEA model is wholly different from the two-stage DEA approach used to regress the produced efficiency scores on control variables not included in the DEA assessment [32]. For the two-stage DEA applications, the reader is referred to the recent works by Wu et al [33] and Saglam [34,35].…”

Section: Survey On Dea Approaches For Infrastructure Project Evaluationmentioning

confidence: 99%

Benchmarking Engineering, Procurement and Construction (EPC) Power Plant Projects by Means of Series Two-Stage DEA

Tsolas

2020

Electricity

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A lot of companies in the power sector use Engineering, Procurement, and Construction (EPC) contracts for complex infrastructure projects such as power plants. This paper presents a series two-stage data envelopment analysis (DEA) approach for the ex ante benchmarking of EPC power plant projects. The current study aims to improve over single-stage DEA and evaluate the efficiency of a group of twelve domestic (located in Greece) and international natural gas-fired power plant projects of different technologies (combined cycle power plant (CCPP) projects with single and multi-shaft configuration, and open cycle power plant (OCPP) projects) by employing a series two-stage DEA model. In the first stage, performance of the EPC mode is evaluated, whereas in the second stage the plant annual operational efficiency is assessed. In the light of the results, there is a lower level of performance in the EPC mode than in operating efficiency. The OCPP projects have the best operating efficiency, whereas they are ranked in-between the CCPP projects with single and multi-shaft configuration in EPC mode performance.

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“…The results showed that the overall technical efficiency of wind power industry is not ideal. Wu et al 8 developed two-stage DEA and Tobit analysis to assess the efficiency of 42 wind farms in China. The findings showed that most of the wind farms operate efficiently.…”

Section: Introductionmentioning

confidence: 99%

Analysis of China’s regional wind power generation efficiency and its influencing factors

Pan

Zhang

et al. 2018

Energy & Environment

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Based on the data of 30 Chinese provinces from 2011 to 2014, we use a meta-frontier data envelopment analysis model and a symbolic regression method to study wind power generation efficiency and its influencing factors. From our analytical results, the main findings are as follows: (1) The wind power generation efficiency of the eastern region is the highest, followed by the western region, and the wind power generation efficiency of the central region is the lowest. (2) The technology gap ratio of eastern region is at a high level and is stable. In contrast, the technology gap ratios of the central and western regions are at a low level and have a larger fluctuation range. (3) The room for improving internal management is huge in the three regions of China mainland, but the advancement of technical level only in the Central and Western is significant. (4) Geographical location has the most impact on wind power generation efficiency, followed by technical progress and carbon regulation, while wind energy reserve has the least impact on wind power generation efficiency.

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Efficiency assessment of wind farms in China using two-stage data envelopment analysis

Cited by 61 publications

References 36 publications

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

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

Benchmarking Engineering, Procurement and Construction (EPC) Power Plant Projects by Means of Series Two-Stage DEA

Analysis of China’s regional wind power generation efficiency and its influencing factors

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