Wind Power Density Distribution Research Articles

Wind energy potential assessment based on wind speed, its direction and power data

Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters of null or low wind speed and multimodal wind speed data, based on expectation–maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as the models of wind direction and the correlation circular variable between wind speed and direction, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-min field monitoring wind data and compared with the other estimation methods and judged by the values of R2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.

Wind energy land distribution in Morocco in 2021–2050 according to RCM simulation of CORDEX-Africa project

According to the Ministry of Energy, more than 93% of Morocco’s energy resources are imported. In 2008, the Moroccan Government has developed the National Energy Strategy, which includes the Moroccan Integrated Wind Energy Program. Morocco is rich in wind energy resources. Morocco launched the renewable energy development program, which aims to achieve a total installed capacity 2000 MW from wind power by 2020. The aim of this study is to determine the characteristics of the distribution of wind power density in Morocco in 2021–2050 and determine the areas with favorable conditions for the development of wind energy in the near future. The article presents the calculation results of wind power density for the period 2021–2050 at 50 m height. As a base value of the wind speed was used value of wind speed calculated by the ensemble of the regional climate models (RСM) of the CORDEX-Africa project. The RCM modeling was carried out for the region of Africa, in a rectangular coordinate system with a spatial resolution of ~44 km. Model calculation was performed taking into account the greenhouse gas concentration trajectory of RCP 4.5. Eleven climate models were used for the calculation. The results of the regional climate modeling of the CORDEX-Africa project showed that in Morocco in 2021–2050, in terms of wind power density at 50 m height, the fair and good wind power class would be the Atlantic coast area between Cape Beddouza and Cape Ghir, where wind power farms in Jbel Lahdid, Essaouira, are located. The richest wind resources in the coming decades are projected on the Atlantic coast to the south of the city of Tarfaya, where the Akhfenir, Tiskrad, and Boujdour wind power farms are located. The Tanger-Tetouan-Al Hoceima region, where the Tanger and Koudia wind farms are located, will be characterized by fair wind power class during the period from February to April. The coast of the Atlantic Ocean, where the Jbel Lahdid, Essaouira, wind farms are located, will have a good and above wind power class over the period from April to September, and the coastal areas of the Laayoune-Sakia El Hamra region can be described by the RCM as a good and above wind power class throughout the year.

An investigation of wind power density distribution at location with low and high wind speeds using statistical model

The study represents wind characteristics and power density in the locations with different wind speed conditions. The wind speed data measured at meteorological stations were used for statistical analysis from two locations with high and low wind speeds in Lithuania. It was found that many of the probability density functions of calculation methods allows you to get a fairly reliable results. However, depending on the geographical situation of the area, the height and other factors affect the wind power density indicates that some of the methods are not acceptable. The Weibull shape k and scale c parameters were calculated by using four methods and after the wind characteristics and power density were estimated. Also, the wind parameters extrapolation with the height was carried out. The mean square error, coefficient of determination, chi-square test and relative error were calculated for validation of goodness fit of Weibull parameters. The empirical model was developed for evaluation of monthly mean wind power density based on the measured wind speeds. The proposed simulation model for assessing the wind energy density could be successfully used for the finding the suitable sites for the development of wind energy in the selected locations.

Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: A review

A knowledge of wind speed and wind power probability distributions is important for several reasons such as assessing wind resources, planning wind farms and minimizing the uncertainties for wind power development. In this study, application of the two parameter Birnbaum-Saunders (BS) distribution is introduced and reviewed for characterizing the wind speed and wind power density distributions. For this work, a long-term time series of measured wind speed data for ten stations distributed in the Ontario province of Canada has been utilized for a case study. The suitability of the BS distribution was evaluated against nine earlier used one-component distributions. The results showed that BS distribution performed very favorably at all selected stations. It outperformed the other distributions at six stations while in the remaining four stations ranked either third or fourth. It was also found that, although the Weibull distribution was the second best function to estimate the wind power density distribution, it was not able to give adequate description of the frequency of wind speed data at all sites. In summary, the study results demonstrated the generalization capability, precision, and effectiveness of the BS distribution for characterizing wind speed and wind power distribution.

Assessment of Commercial Wind Profiles for Bangladesh in Hotspots Determined by the UNEP

This paper predicts pragmatic turbine level profiles of wind power, the fastest growing source of renewable energy in the world, for zonal areas in the South Asian country of Bangladesh selected on the basis of global readings provided by the United Nations Environmental Programme (UNEP). Without focusing exclusively on the coastal belt according to tradition, hotspots (regions with high wind potential) uniformly located around the country are taken into consideration. Nine observation stations from three such hotspots in the country are documented with respect to statistics of wind speed, vertical power law coefficients, wind power density distribution at different heights, extraction of energy factor, prominent direction distribution of wind, and approximation of weibull density functions over a ten year period using measured meteorological raw data. Profiles are projected for commercial turbine heights (~40-60m) and the available internationally accepted power classes for wind are identified at the stations to assess the true wind potential of Bangladesh.

Analysis of some flexible families of distributions for estimation of wind speed distributions

The wind speed distribution is the basis for the assessment of wind energy potential needed for the design of wind farms. Therefore, the proper specification of the wind speed distribution is of special importance. In this study, we propose for the first time two flexible families of distributions, the skewed generalized error and skewed t, for the description of the wind speed distribution. These families both are very flexible enough to accommodate the shape of the wind speed data and include some well-known distributions as special cases. Also we evaluate the performance of these flexible families relative to the widely-used Weibull distribution by using wind speed data measured in various regions of Turkey. The results indicate that these flexible families of distributions provide substantial improvement over the Weibull distribution in estimating wind speed distribution and wind power density distribution. Thus, the skewed generalized error and skewed t distributions can be alternatively used for assessment of wind energy potential.

전산유동해석을 통한 산악 지역의 고해상도 풍력자원 특성에 관한 수치연구

전산유동 수치모형을 이용하여 다양한 대기안정도 상태하에서 부산광역시내 승학산과 구덕산의 초고해상도 풍력자원을 평가하였다. 연구에 사용된 수치모형은 중규모와 미규모 기상현상의 재현에 널리 사용되는 전산유동 수치모형인 A2C이다. 대기안정도가 강할 때, 위치에너지의 크기가 상대적으로 강해지기 때문에 산을 넘어가는 경향이 나타난다. 반면 대기안정도가 약해지면서 산악후면의 후류 발생이 증대되며, 난류에너지가 증가한다. 그리고 연평균 풍력밀도, 난류운동에너지, 연직 바람전단력 분석을 통하여 구덕산 정상의 남쪽 부근이 다른 구역보다 가용 풍력자원이 풍부함을 확인하였다. The purpose of this paper is to evaluate the wind energy resources with high spatial resolution in Sunghak and Guduck mountains in Busan Metropolitan area under the various atmospheric stabilities. The numerical model used in this research is A2C (Atmosphere to CFD), mainly applied to assess the regional scale and microscale meteorological phenformin. Wind under the strong atmospheric stability moves around mountain side smoothly due to the strong potential energy. On the other hand, the cavity region on the lee side of mountain tends to be created and expanded as the atmospheric stability decrease. Annually the average distribution of wind power density, turbulence kinetic energy, and vertical wind shear help to explain quantitatively that wind resource near the northern side of Guduck mountain top is more suitable to establish wind energy complex than that in any other regions in the target area.

An Improved Method for Estimating the Wind Power Density Distribution Function

Abstract The wind power density (WPD) distribution curve is essential for wind power assessment and wind turbine engineering. The usual practice of estimating this curve from wind speed data is to first estimate the wind speed probability density function (PDF) using a nonparametric or parametric method. The density function is then multiplied by one-half the wind speed cubed times the air density. Unfortunately, this means that minor errors in the estimation of the wind speed PDF can result in large errors in the WPD distribution curve because the cubic term in the WPD function magnifies the error. To avoid this problem, this paper presents a new method of estimating the WPD distribution curve through a direct estimation of the curve using a Gauss–Hermite expansion. It is demonstrated that the proposed method provides a much more reliable estimate of the WPD distribution curve.

종관 바람 관측 자료를 이용한 북한 지역의 풍력자원 분석

북한 지역의 27개 기상관측지점의 30년 바람 자료를 이용하여 고도 50 m에서의 풍력밀도를 분석하였다. 27개 지점의 연평균 풍력밀도는 58.6W/<TEX>$m^2$</TEX>로 1등급에 해당하는 풍력 자원이었다. 계절에 따른 평균 풍력밀도는 겨울보다 봄에 더 높았으며, 여름에는 봄의 50% 정도의 풍력밀도를 나타냈다. 풍력밀도의 일변화를 보면 거의 모든 관측 지점에서 오후에 비교적 높은 풍력밀도와 오전 3-6시 경에 낮은 풍력밀도를 보였으며, 일변화의 진폭은 봄에 가장 컸다. 특히 내륙 중심부인 개마고원 지역과 함경북도 동북부, 평안도 남부 해안, 황해도 해안 근처에서 비교적 높은 값을 나타냈다. 장진에서의 연평균 풍력밀도는 3등급인 151.2 W/<TEX>$m^2$</TEX>를 나타냈으며, 용연은 2등급인 102.4 W/<TEX>$m^2$</TEX>의 값을 보였다. Wind power density distribution over the North Korea territory was investigated by using 30-year wind observations at 27 meteorological stations. The mean annual wind power density over North Korea turned out to be 58.6W/<TEX>$m^2$</TEX>, which corresponds to the wind power class of 1. The wind power density shows a seasonal variation, having the highest density in spring and the lowest in summer. In particular, the wind power density in summer is about a half of that in spring. The diurnal variation of the wind power density shows that the highest and lowest densities occur in the afternoon and between 3 and 6 am in local time, respectively. The most potential wind energy generation regions are the Gaema Plateau in the central region, the northeast part of Hamgyeongbuk-do, the south coast of Pyongan-do and the west coast of Hwanghae-do. The mean annual wind power density in Changjin is 151.2W/<TEX>$m^2$</TEX>, which is equivalent to the class of 3. In Ryongyon, the annual mean wind power density is 102.4W/<TEX>$m^2$</TEX>, which belongs to the class of 2.

A continuous bivariate model for wind power density and wind turbine energy output estimations

The wind power probability density function is useful in both the design process of a wind turbine and in the evaluation process of the wind resource available at a potential site. The continuous probability models used in the scientific literature to estimate the wind power density distribution function and wind turbine energy output assume that air density is independent of the wind speed. A constant annual value for air density of 1.225 kg m −3 , corresponding to standard conditions (sea level, 15 °C), is generally used. A bivariate probability model (BPM) is presented in this paper for wind power density and wind turbine energy output estimations. This model takes into account the time variability of air density and wind speed, as well as the correlation existing between both variables. Contingency type bivariate distributions with specified marginal distributions have been used for this purpose. The proposed model is applied in this paper to meteorological data (temperature, pressure, relative humidity, wind speed) recorded over a one year period at a weather station located at the facilities of the Technological Institute of the Canary Islands (Spain). The conclusion reached is that the BPM presented in this paper is more realistic than the univariate probability models (UPMs) normally used in the scientific literature. In the particular case under study, and for all the situations analysed, the BPM has provided values for the annual mean wind power density and annual energy output of a wind turbine that fit the sample data better than the UPMs. However, as a result of the climatological characteristics of the area where the analysis was performed, the results do not differ notably from those obtained through the use of a UPM and the mean air density of the area.

Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions

The typical two-parameter Weibull is a flexible distribution that is useful for describing unimodal frequency distributions of wind speeds at many sites. A two-component mixture Weibull distribution (WW-probability distribution function (pdf)) is even more useful because it is additionally able to represent heterogenous wind regimes in which there is evidence of bimodality or bitangentiality or, simply, unimodality. An analysis is made in this paper of three of the most frequently used methods in the estimation of the five parameters of the WW-pdf and the numerical methods employed are described. Hourly mean wind speed data recorded at four weather stations located in the island of Gran Canaria (Spain) are used to analyse the estimation methods. Prior identification of the sample components of the mixture is not required. The suitability of the distributions is judged from the various tests-of-fit commonly used in the specialised literature on wind energy. A comparison is also made of the ability to describe the experimental wind power density distribution. The general conclusion is that if the sample data are independent then maximum likelihood (ML) estimators should be used due to their large sampling efficiency. However, they require elaborate calculation techniques. The least-square (LS) method provides a robust and computationally efficient alternative to the techniques currently in use. The method of moments has the disadvantage that it does not always supply a feasible result and lacks the desirable optimality properties of ML and LS estimators.