Optimum siting of wind turbine generators

Jangamshetti, Suresh H.; Ran, V.G.

doi:10.1109/60.911396

Cited by 133 publications

(48 citation statements)

References 4 publications

(1 reference statement)

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“…This estimation of the CDFs can be done parametrically or non-parametrically. In this paper for brevity, we shall follow the nonparametric approach on the NREL data, mindful that as in [29] we could have employed a given parametric CDF (with its parameters obtained either through sample fitting or from a transformation of a Weibull distribution through the turbine curve; see for instance [37]) with similar results.…”

Section: Mixed Formulation Of the Wind Power Distributionmentioning

confidence: 99%

Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation

Díaz

Casielles

Coto

2014

International Journal of Electrical Power & Energy Systems

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In clusters of wind generators spread over small geographic areas, the spatial correlation of wind power production is strong. Simulation of joint power production in such cases-such as for instance for determining the available power in a microgrid-is flawed if the correlation is not properly defined.Several methods have been proposed in the literature for producing scenarios of correlated samples; mostly focused on wind speed. In this paper we analyze three popular choices: classical Monte Carlo (with correlation induced by Cholesky factorization), Latin Hypercube Sampling (with correlation induced by rank sorting), and the recent copula theory. We put together a variety of statistical tools to transform an uncorrelated multivariate sample into a correlated one; and supplement other works by introducing a detailed definition of the wind power distribution and by expanding the Archimedean copula analysis to dimensions beyond the bivariate case analyzed in some related works.We analyze a year of wind production of 210 wind site from NREL data base. We cluster them to give a view of prospective microgrids, and employ several statistical techniques to measure the adequacy of the simulated samples to the original measured data.Our results show that, for generation in small geographic areas, the higher the number of generators, the better the wind power dependence structure is described by LHS. On the contrary, copulas-Gumbel or Gaussian for two-and three-dimensional problems, and Gaussian for higher dimensions-are better suited for representing correlated wind speed. The results are different when the generators are spread over large geographic areas.Compared with LHS endowed with rank sorting for inducing correlation, copula theory is in some sense cumbersome to apply for modeling and simulating wind power data. However, simulations can be performed in prospective microgrids in small geographical areas with larger accuracy by means of LHS if wind power is analyzed rather than wind speed. This advantage is lost for large distances or when small number of generators is considered.

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Section: Mixed Formulation Of the Wind Power Distributionmentioning

confidence: 99%

Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation

Díaz

Casielles

Coto

2014

International Journal of Electrical Power & Energy Systems

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“…The mono-objective function to be maximized is the total instantaneous mechanical power conversion (see Sections 3.1.1 and 3.1.2 for details) of the wind farm (defined as the sum of the power conversion of all the WTs, which can be determined using Equation (2)), while considering wake effects (e.g., the Jensen-Katic wake model, Equations (15) and (39), and the wake superposition by sum of squares of velocity deficits method, Equation (41), are assumed). The design variables ( ∈ {0,1}, ∀ ∈ {1,2, … , } ) represent if a WT is located or not on each discretized space.…”

Section: The Computational Complexity Of the Wfdo Problemmentioning

confidence: 99%

“…Based on this coarse wake-interaction modeling, it was concluded that quite large separations between WTs were required to avoid significant wake-based energy losses. In the subsequent years, staggered equally-spaced (or grid-like) siting schemes were slowly adopted as a rule-of-thumb by wind farm designers [11][12][13][14][15][16][17]. In the late 1970s and the early 1980s, just after the construction of the world's first electricity-generating wind farm in New Hampshire (USA) in 1980, the first engineering wake models [18][19][20][21][22][23][24], mostly based on flow momentum conservation and linearized far wake expansion assumptions (with the notable exception of the Larsen [25] and Ainslie [26] models), appeared, laying the groundwork for the first study on Wind Farm Design and Optimization (WFDO) [27], which aimed at optimizing the spacing between WTs in a linear array by using a generalized reduced gradient method that maximized the total power conversion of the array.…”

Section: Introductionmentioning

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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“…Therefore, a variety of PDFs have been proposed in literature to describe wind , are also applied to wind energy analysis recently, and they have been proved to be more effective than unimodal types for wind regimes with bimodal distribution. A number of detailed reviews on modeling the probability distributions of wind speed in wind energy analysis can be found in references [20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Li²,

Yang

et al. 2016

J. Mod. Power Syst. Clean Energy

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An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation (PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution (e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation (KDE) method which is a nonparametric way to estimate the probability density function (PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites.The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the longterm wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system (IEEE-RTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.

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Optimum siting of wind turbine generators

Cited by 133 publications

References 4 publications

Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation

Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation

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

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

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