Evaluating the performance of wind farms in China: An empirical review

Lo, Shih-Fang; Wu, Chou-Yen

doi:10.1016/j.ijepes.2014.12.034

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…Pei et al [8] show that the utilization hours of wind power equipment began to drop abruptly since 2011 and the wind curtailment problem has deteriorated year by year. Lo et al [9] find that the wind farms have developed out of harmony with the electricity grid construction, which has resulted in a large proportion of the electricity generated by wind turbines being wasted. Liu [10] also demonstrates that about 30% of the installed wind capacity sits idle owing to overinvestment.…”

Section: Introductionmentioning

confidence: 99%

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

Xiao

et al. 2016

Energy Conversion and Management

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Section: Introductionmentioning

confidence: 99%

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

Xiao

et al. 2016

Energy Conversion and Management

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“…where v i,k n is the membership degree of the i-th wind farm and the n-th indicator belongs to the rank L k , and k was taken as 1, 2, 3, 4 in this paper. (6) Determining the variable weight values of the evaluation indicators by the improved CRITIC weighting method, of which the calculation steps are illustrated in Section 3.1, as shown in Formula (27).…”

Section: Modeling Processmentioning

confidence: 99%

“…Aldersey et al [5] used the capacity factor to measure the power generation performance of offshore wind farm and analyzed the inter-year and intra-year variability of capacity factor. Lo et al [6] used capacity factor to measure the operation performance of wind farms and discussed the relationship between the operation performance of wind farms and key factors such as resource area, regional location, and scale. Niu et al [7] made a comparative analysis of the correlation, consistency, and representation of three efficiency measurement indicators such as wind farm availability, power generation efficiency, and power coefficient from the aspects of probability distribution, pairwise difference, and linear correlation.…”

Section: Introductionmentioning

confidence: 99%

Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences

et al. 2022

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The accurate evaluation and fair comparison of wind farms power generation performance is of great significance to the technical transformation and operation and maintenance management of wind farms. However, problems exist in the evaluation indicator systems such as confusion, coupling and broadness, and the influence of wind energy resource differences not being able to be effectively eliminated, which makes it difficult to achieve the fair comparison of power generation performance among different wind farms. Thus, the evaluation indicator system and comprehensive evaluation method of wind farm power generation performance, including the influence of wind energy resource differences, are proposed in this paper to address the problems above, to which some new concepts such as resource conditions, ideal performance, reachable performance, actual performance, and performance loss are introduced in the proposed indicator system; the combination of statistical and comparative indicators are adopted to realize the quantitative evaluation, indicator decoupling, fair comparison, and loss attribution of wind farm power generation performance. The proposed comprehensive evaluation method is based on improved CRITIC (Criteria Importance though Intercrieria Correlation) weighting method, in which the uneven situation of different evaluation indicators and the comprehensive comparison of power generation performance among different wind farms shall be overcome and realized. Several sets of data from Chinese wind farms in service are used to validate the effectiveness and applicability of the proposed method by taking the comprehensive evaluation models based on CRITIC weighting method and entropy weighting method as the benchmarks. The results demonstrated that the proposed evaluation indicator system works in the quantitative evaluation and fair comparison of wind farm design, operation, and maintenance and traces the source of power generation performance loss. In addition, the results of the proposed comprehensive evaluation model are more in line with the actual power generation performance of wind farms and can be applied to the comprehensive evaluation and comparison of power generation performance of different wind farms.

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“…With 282.5 GW installed capacity in 2012 compared with 94 GW in 2007, wind energy is potentially one of the fastest emerging renewable energy technology worldwide [1]. Motivated by the desire to reduce fossil fuel emissions, policy makers implement incentives for increasing investment in wind energy worldwide [2].…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the desire to reduce fossil fuel emissions, policy makers implement incentives for increasing investment in wind energy worldwide [2]. By the end of 2013, China possessed 91.4 GW cumulative capacity fostering its rank in the global wind market [1]. Wind farms (WFs) are geographically constructed in remotely located areas with favourable wind speed conditions [3, 4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive voltage control strategy for variable‐speed wind turbine connected to a weak network

Abulanwar

Chen

et al. 2016

IET Renewable Power Generation

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Evaluating the performance of wind farms in China: An empirical review

Cited by 6 publications

References 7 publications

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

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

Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences

Adaptive voltage control strategy for variable‐speed wind turbine connected to a weak network

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