A Multivariate and Multimodal Wind Distribution model

Zhang, Jie; Chowdhury, Souma; Messac, Achille; Castillo, Luciano

doi:10.1016/j.renene.2012.09.026

Cited by 89 publications

(64 citation statements)

References 22 publications

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“…Erdem and Shi [15] constructed seven different bivariate joint distributions using three construction approaches, namely, angular-linear, Farlie-Gumbel-Morgenstern and anisotropic lognormal approaches. Zhang et al [16] presented a multivariate distribution of wind speed, wind direction and air density by using a non-parametric approach, multivariate kernel density estimation. However, these joint distributions have not yet been widely known or used in the wind energy field.…”

Section: Introductionmentioning

confidence: 99%

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Feng

Shen

2015

Energies

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Abstract:Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint distributions of wind speed and wind direction, which is based on the parameters of sector-wise Weibull distributions and interpolations between direction sectors. It is applied to the wind measurement data at Horns Rev and three different joint distributions are obtained, which all fit the measurement data quite well in terms of the coefficient of determination . Then, the best of these joint distributions is used in the layout optimization of the Horns Rev 1 wind farm and the choice of bin sizes for wind speed and wind direction is also investigated. It is found that the choice of bin size for wind direction is especially critical for layout optimization and the recommended choice of bin sizes for wind speed and wind direction is finally presented.

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Section: Introductionmentioning

confidence: 99%

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Feng

Shen

2015

Energies

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“…Since wind speed and density (which is a function of other thermodynamic variables such as pressure, temperature and humidity) are time-and space-dependent, as mentioned in Section 2.1, they are often considered stochastic variables that in most cases show well-defined statistical distributions; often these distributions are assumed to be Weibullian [216] and Normal [217] distributions, respectively [43,218]. Each statistical distribution is defined in terms of statistical parameters, namely the Weibull scale ( ) and shape ( ) parameters, and the normal mean ( ) and standard deviation ( ) parameters, respectively.…”

Section: Objective Functionsmentioning

confidence: 99%

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

et al. 2014

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This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research. OPEN ACCESSEnergies 2014, 7 6931

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“…Appreciable work has been done (and is ongoing) (i) to account for the variation of wind conditions at a particular site (e.g., wind distribution modeling [1,2]) and (ii) to address the intermittency of wind resources in the context of grid integration (e.g., energy storage technologies [3,4]). In contrast, there has been a limited amount of work that investigates the complex demands on wind turbine performance (when In this paper, we are particularly concerned with the role of the third factor in wind farm performance.…”

Section: A Temporally-and Spatially-varying Energy Resourcementioning

confidence: 99%

Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

et al. 2016

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Abstract:The suitability of turbine configurations to different wind resources has been traditionally restricted to considering turbines operating as standalone entities. In this paper, a framework is thus developed to investigate turbine suitability in terms of the minimum cost of energy offered when operating as a group of optimally-micro-sited turbines. The four major steps include: (i) characterizing the geographical variation of wind regimes in the onshore U.S. market; (ii) determining the best performing turbines for different wind regimes through wind farm layout optimization; (iii) developing a metric to quantify the expected market suitability of available turbine configurations; and (iv) exploring the best tradeoffs between the cost and capacity factor yielded by these turbines. One hundred thirty one types of commercial turbines offered by major global manufacturers in 2012 are considered for selection. It is found that, in general, higher rated power turbines with medium tower heights are the most favored. Interestingly, further analysis showed that "rotor diameter/hub height" ratios greater than 1.1 are the least attractive for any of the wind classes. It is also observed that although the "cost-capacity factor" tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar.

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A Multivariate and Multimodal Wind Distribution model

Cited by 89 publications

References 22 publications

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

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