Introducing a system of wind speed distributions for modeling properties of wind speed regimes around the world

Jung, Christopher; Schindler, Dirk; Laible, Jessica; Buchholz, Alexander

doi:10.1016/j.enconman.2017.04.044

Cited by 67 publications

(25 citation statements)

References 38 publications

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“…19 Subsequently, some of the previous authors represented wind speed data of Nordic regions of Canada, in addition to the two-component homogeneous distributions, also through heterogeneous (the two components are represented by two different distributions) twocomponent distributions. 22 In addition, some of these authors used the previous single and mixture distributions to evaluate the robustness of a wind speed distribution system compared with the typical deficiencies of the wind speed data, such as errors of measurement, missing data, and low temporal resolution. Hu et al 21 proposed a more general model, called the hierarchical mixture of multiple distributions, in which it is assumed that each component distribution is itself a mixture.…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

“…Kappa and Wakeby unimodal distributions and Burr-generalized extreme value mixture distribution were proposed by Jung et al as a single system able to reproduce a large majority of existing wind regimes around the world with very high accuracy. 22 In addition, some of these authors used the previous single and mixture distributions to evaluate the robustness of a wind speed distribution system compared with the typical deficiencies of the wind speed data, such as errors of measurement, missing data, and low temporal resolution. 23 To assess the suitability to model wind speed data in the UAE, gamma, Weibull, extreme value type I, and normal distributions were used to construct mixture distributions, while the Weibull and Kappa distributions were considered as conventional unimodal distributions.…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

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A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

2019

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Summary The use of wind speed probability density functions is a standard practice to represent different wind regimes. Generally, these regimes are distinguished by the following three characteristics: the shape of the distribution in the central wind speeds, amount of the calm wind speeds (CWS), and extreme wind speeds (EWS). An in‐depth review has highlighted that none of the parametric distributions available is suitable to represent the three main characteristics at the same time. To overcome this gap, the use of the corrected mixture of two truncated normal distributions (CMTTND) and corrected single truncated normal distribution (CTND) are proposed to represent, respectively, bimodal and unimodal wind speed distribution shapes. The CMTTND and CTND are obtained by introducing a correction, respectively, to the mixture of two truncated normal distributions (MTTND) and to the single truncated normal distribution (TND). The MTTND and TND permit an accurate representation of distributions with high levels of CWS. The CMTTND and CTND employ a new parameter, to accurately quantifying also the relative frequencies associated with EWS. The performance of the CMTTND and CTND was assessed using a goodness‐of‐fit (GOF) test and statistical measures of error in the evaluation of the characteristic mean wind speeds. The analytical expressions of these mean wind speeds are obtained and validated by a numerical integration method for the first time in this work. The accuracy of these distributions is compared with that of other conventional probability distribution models, of which three are unimodal and six bimodal, in four Italian locations and three American locations. The analysis of the results showed that the CTND and CMTTND allow obtaining high GOF of the experimental distributions with R2 and RMSE higher and lower than, respectively, 0.977 and 0.054. Moreover, the CTND results in the most accurate distribution in the estimation of the characteristic mean wind speeds in the case of localities with unimodal experimental distributions and the CMTTND in the case of localities with bimodal experimental distributions. Contrary to other distribution, CTND and CMTTND accuracies grow by increasing the grade of the characteristic mean wind speed by reaching also estimation values lower than 2% of the real ones. This is a great advantage in the wind energy source determination in a location since the available energy depends on the mean cubic wind speed.

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Section: Single and Mixture Distributionsmentioning

confidence: 99%

Section: Single and Mixture Distributionsmentioning

confidence: 99%

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

2019

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“…The development of wind power has been accelerating in recent years to cope with the growing energy consumption as well as the environmental deterioration [1]. Two common ways are available to integrate wind power to the grid.…”

Section: Introductionmentioning

confidence: 99%

Operation Flexibility Evaluation and Its Application to Optimal Planning of Bundled Wind-Thermal-Storage Generation System

Yang

Zhang

et al. 2018

Electronics

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The growing penetration of wind power in a power system brings great challenges to system operation flexibility. For generation planning in presence of high wind power penetration, it is essential to take the operation flexibility of the system into account. Firstly, this paper developed the system operation flexibility metrics through considering the flexibility contribution of thermal generating units (TGUs) by operational state transition. Secondly, a planning model for the bundled wind-thermal-storage generation system (BWTSGS) that considers the operation flexibility constraints is proposed. The planning model is used to determine the power and energy rating of an energy storage system (ESS) as well as the type and number of TGUs. A daily scheduling simulation model of a BWTSGS is proposed to calculate the operation cost for the planning model and consider the sequential operation characteristics of the BWTSGS. Further, in order to accelerate the computation, a wind power sequential clustering technique based on the discrete Fourier transform (DFT) method is developed for improving the computational efficiency. Case studies have been conducted on a 1000-MW wind farm to demonstrate the validity and effectiveness of the proposed model.

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“…Within this framework, wind energy is awarded a crucial role. Wind farms can mostly be found in the northern part of Germany near the coast or offshore, where wind resource is abundant [4]. The expansion of onshore wind turbines, however, is experiencing a significant upswing as well.…”

Section: Introductionmentioning

confidence: 99%

On the Annual Cycle of Meteorological and Geographical Potential of Wind Energy: A Case Study from Southwest Germany

2017

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Wind energy in Germany has experienced high growth rates over the last few years. The set political target in the German federal state of Baden-Wuerttemberg is to raise the share of wind energy in the overall electricity supply to 10% by 2020. To achieve this goal, detailed information on wind energy potential in Baden-Wuerttemberg is necessary. This study assesses the geographical wind energy potential (GP) in Baden-Wuerttemberg giving a guideline to identify suitable locations for wind energy utilization. The focus of this investigation lies in assessing GP for the mean annual meteorological wind energy potential (MP) as well as for the mean MP in December and August providing information on the seasonal behavior of wind power availability. A GIS-based approach is employed to identify sites without geographical restrictions and with sufficient MP at hub heights of 100 m, 140 m, and 200 m. The study finds that (1) the number of possible sites for wind energy utilization is strongly limited by geographical restrictions, (2) GP is highly dependent on MP and, therefore, (3) GP varies highly throughout a year since MP depends on the seasonal pattern of wind speed in Central Europe, showing high values in winter and low values in summer.

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Introducing a system of wind speed distributions for modeling properties of wind speed regimes around the world

Cited by 67 publications

References 38 publications

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

Operation Flexibility Evaluation and Its Application to Optimal Planning of Bundled Wind-Thermal-Storage Generation System

On the Annual Cycle of Meteorological and Geographical Potential of Wind Energy: A Case Study from Southwest Germany

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