A review on scale and siting of wind farms in China

Deng, Yuanchang; Yu, Zhi Min; Sha, Liqing

doi:10.1002/we.427

Cited by 20 publications

(8 citation statements)

References 4 publications

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“…Wind speed density and wind direction for a given terrain play a significant role in maximizing power capture. With advanced manufacturing technologies and the availability of automation, the production costs for a wind turbine have lowered in the past decade [13]. Apart from the wind resource, complex terrains add a significant problem to the turbine siting process where the irregular land and scanty resources increase transportation and labor cost significantly.…”

Section: Problem Formulationmentioning

confidence: 99%

Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

et al. 2019

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Hybrid operation of wind farms has been in the limelight in recent years wherein the stochastic nature of wind causes market operators to choose an optimal strategy to maximize profit. The current work deals with a multi-criteria decision making approach to choose the best possible alternatives for a hybrid wind farm operation. A set of three, non-beneficial criteria, namely wind wakes, wind curtailment, and forced outages, were chosen to evaluate the best alternative. Three methods, (i) Simple Additive Weighting (SAW), (ii) the Technique for Order or Preference by Similarity to Ideal Solution (TOPSIS) and (iii) Complex Proportional Assessment (COPRAS), were applied to identify the best alternative, and the results revealed that for all three methods, borrowing deficit power from a neighboring wind farm is the best alternative. Comparative analyses in terms of the data requirement, the effect of dynamic decision matrices, and rank reversal in wind farm application have also been pioneered.

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Section: Problem Formulationmentioning

confidence: 99%

Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

et al. 2019

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“…The method utilised historical data from existing wind farms and the aim of the study was to predict power as a decision factor for future investment of a wind farm. A review of the scale and siting of wind farms in China was undertaken by Deng et al (2011). They found that the average capacity of wind farms is increasing and complex terrain can limit the size of wind farms due to increased costs.…”

Section: Introductionmentioning

confidence: 99%

Development of a quantitative analysis system for greener and economically sustainable wind farms

Simons

Cheung

2016

Journal of Cleaner Production

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“…Proximity of urban areas, transportation infrastructure and slope were also frequently incorporated in past suitability analyses. A recent review of wind farms in China indicates the practice of placing wind turbines in sparsely vegetated and populated areas, as well as on land with low slopes . Mann et al .…”

Section: Introductionmentioning

confidence: 99%

Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa, USA

Petrov

Wessling

2014

Wind Energy

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Because of the current shift away from fossil fuels and toward renewable energy sources, it is necessary to plan for the installation of new infrastructure to meet the demand for clean energy. Traditional methods for determining wind turbine site suitability suffer from the selection of arbitrary criteria and model parameters by experts, which may lead to a degree of uncertainty in the models produced. An alternative empirically based methodology for building a wind turbine siting model for the state of Iowa is presented in the study. We employ 'ecological niche' principles traditionally utilized to model species allocation to develop a new multicriteria, spatially explicit framework for wind turbine placement. Using information on suitability conditions at existing turbine locations, we incorporate seven variables (wind speed, elevation, slope, land cover, distance of infrastructure and settlements, and population density) into two machine-learning algorithms [maximum entropy method (Maxent) and Genetic Algorithm for Rule Set Prediction (GARP)] to model suitable areas for installation of wind turbines. The performance of this method is tested at the statewide level and a six-county region in western Iowa. Maxent and GARP identified areas in the Northwest and North Central regions of Iowa as the optimum location for new wind turbines. Information on variable contributions from Maxent illuminates the relative importance of environmental variables and its scale-dependent nature. It also allows validating existing assumptions about the relationship between variables and wind turbine suitability. The resultant models demonstrate high levels of accuracy and suggest that the presented approach is a possible methodology for developing wind turbine siting applications.

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A review on scale and siting of wind farms in China

Abstract: Using a wind power information system, which is based on the geographic information system (GIS), this paper presents a statistical analysis of built and approved wind farms to the end of 2008. The average capacity of wind turbine generators (

Cited by 20 publications

References 4 publications

Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

Development of a quantitative analysis system for greener and economically sustainable wind farms

Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa, USA

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