Estimation Of Wind Energy Potential Research Articles

Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

Power Prediction of Wind Turbine Based on The Presumed Shape of Power Curve

An accurate model of power plays a crucial role in turbine energy assessment, wind turbine condition monitoring, estimation of wind energy potential, warranty formulations, power forecasting, wind turbine selection, optimization of the operational cost and expansion of windfarm. To achieve all these, algorithms of linear and cubic law models are used to predict the output power of BWC Excel 10 wind turbine. The comparative results show that the considered models can approximate and satisfactorily predicts the output power of wind turbines when compared with fundamental equation of wind turbine that depends on stringent factors like air density, turbine blade parameters, mechanical and control issues etc to yield similar results.

Tail-Weighted Wind Speed Distribution by Mixture Model with Constrained Maximum Likelihood

In order to reduce the estimation uncertainty for the wind energy output, the accurate assessment of the wind speed distribution plays an important role. Due to the influence of the complex terrain, the wind speed data in mountainous area may show the heavy tail. The tail has an important effect on the annual energy production of the wind turbine because of the power output being the cubic of the wind speed. Hence the heavy tail should be highlighted before choosing the proper distributions. To characterize the heavy tail, the box plot, QQ plot and conditional mean exceedance are used. In addition, the wind climate in the mountainous area is very complicated and typically mixed by local storms and large-scale wind flows. The distribution of the wind speed is prone to exhibit bimodal or even multi-modal nature. The conventional unimodal distributions such as Weibull may not well portray these distributions. The mixture distribution has more flexibilities to handle this challenge. Both generalized extreme value distribution and generalized Pareto distribution are considered to formulate the mixture distribution in modeling the tail. The maximum likelihood method and the constrained maximum likelihood method are used to estimate distribution parameters. The comprehensive evaluation criteria are adopted to determine the most appropriate distribution of four sets of wind speed in the mountainous area of Yunnan, China. Graphical tools, such as histogram density curve and QQ plot, are also used for evaluation. Results show that heavy tail distribution should be considered for the wind data in mountainous areas and the proposed mixture model with the constrained maximum likelihood method for the parameter estimation will be superior for the wind energy potential estimation.

The use of Sentinel-1 OCN products for preliminary deep offshore wind energy potential estimation: A case study on Ionian sea

The exploitation of wind energy potential has been strongly limited in the Mediterranean area due to its critical bathymetry. However, the spread of new deep offshore technologies such as floating turbines could drastically change the actual scenario, allowing wind energy to be exploited even in areas with critical bathymetric conditions. In such framework, remote sensing technologies could significantly help the development of offshore wind farms and may represent a valid resource for better planning wind energy solutions. In this article, a satellite based methodology for assessing the wind energy potential of offshore areas is presented and applied on a study case in the Ionian sea to identify the most effective areas for energy harvesting. The study is based on the use of the Sentinel-1 OCeaN (OCN) products, which are Level-2 sea-based data products provided by Copernicus (the principle European Union’s earth observation programme), containing wind retrieval information. Despite their proven accuracy and their ease of use, OCN products have been rarely used in recent scientific production for energetic purposes. An initial investigation on wind speed characteristics has been carried out using as reference data-set the one provided by the Global Wind Atlas (GWA). A sub-area with maximum coastal wind speed gradient has been identified in the Region of Interest (ROI). Afterwards, 54 Sentinel-1 images have been processed. Data have been aggregated on a monthly domain (February 2020 - January 2021) to produce 12 wind speed maps of the ROI sub-area. A productivity analysis has been carried out in three specific sites. Results show: (i) the effectiveness of an OCN products based methodology for wind energy solutions planning, (ii) the reliability of the monthly based data aggregation (proven by a spatial dispersion analysis) and (iii) the specific wind energy production potentialities of the chosen ROI, where a single wind turbine installation could ensure an annual power production up to 19.06 GWh.

Wind energy potential estimation using neural network and SVR approaches

The distribution of wind speed and the optimal assessment of wind energy potential are very important factors when selecting a suitable site for a wind power plant. In wind farm design projects for the supply of electrical energy, designers use the Weibull distribution law to analyse the characteristics and variations of wind speed in order to evaluate the wind potential. In our study we used two approaches, namely, the Multilayer Perceptron (MLP) approach and the Support Vector Machine (SVR) approach to determine a distribution law of wind speeds and to optimally evaluate the wind potential. These two approaches were compared to two well-known numerical methods which are the Justus Empirical Method (EMJ) and the Maximum Likelihood Method (MLM). The results show that the neural network approach produces a better fit of the distribution curve with an Root Mean Square Error (RMSE) of 0.00005016 at Lomé, 0.000040289 at Cotonou site and a more interesting estimate of the wind potential. After that SVR show a better result too with an RMSE of 0.0095618 at the Lomé site and 0.0053549 at the Cotonou site.

A New Approach to Assess Wind Potential

<p>Weibull Cumulative Distribution Function (C.D.F.) has been employed to assess and compare wind potentials of two wind stations Europlatform and Stavenisse of The Netherland. Weibull distribution has been used for accurate estimation of wind energy potential for a long time. The Weibull distribution with two parameters is suitable for modeling wind data if wind distribution is unimodal. Whereas wind distribution is generally unimodal, random weather changes can make the distribution bimodal. It is always desirable to find a method that accurately represents actual statistical data. Some well-known statistical methods are Method of Moment (MoM), Linear Least Square Method (LLSM), Maximum Likelihood Method (M.L.M.), Modified Maximum Likelihood Method (MMLM), Energy Pattern Factor Method (EPFM), and Empirical Method (E.M.), etc. All these methods employ Probability Density Function (PDF) of Weibull distribution, except LLSM, which uses Cumulative Distribution Function (C.D.F.). In this communication, we are presenting a newly proposed method of evaluating Weibull parameters. Unlike most methods, this new method employs a cumulative distribution function. A MATLAB® GUI-based simulation is developed to estimate Weibull parameters using the C.D.F. approach. It is found that the Mean Square Error (M.S.E.) is the lowest when using the new method. The new method, therefore, estimates wind power density with reasonable accuracy. Wind Power (W.P.) is estimated by considering four different Wind Turbine (W.T.) models for two sites, and maximum W.P. is found using Evance R9000.</p>

Estimation of wind energy potential and comparison of six Weibull parameters estimation methods for two potential locations in Nepal

Estimation of wind energy potential and comparison of six Weibull parameters estimation methods for two potential locations in Nepal

Modeling directional distributions of wind data in the United Arab Emirates at different elevations

Modeling wind speed and direction are crucial in several applications such as the estimation of wind energy potential and the study of the long-term effects on engineering structures. While there have been several studies on modeling wind speed, studies on modeling wind direction are limited. In this work, we use a mixture of von Mises distributions to model wind direction. Finite mixtures of von Mises (FMVM) distributions are used to model wind directions at two sites in the United Arab Emirates. The parameters of the FMVM distribution are estimated using the least square method. The results of the research show that the FMVM is the best suited distribution model to fit wind direction at these two sites, compared to other distributions commonly used to model wind direction.

A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation

In this paper, we propose a Four-Dimensional Variational (4D-Var) data assimilation framework for wind energy potential estimation. The framework is defined as follows: we choose a numerical model which can provide forecasts of wind speeds then, an ensemble of model realizations is employed to build control spaces at observation steps via a modified Cholesky decomposition. These control spaces are utilized to estimate initial analysis increments and to avoid the intrinsic use of adjoint models in the 4D-Var context. The initial analysis increments are mapped back onto the model domain from which we obtain an estimate of the initial analysis ensemble. This ensemble is propagated in time to approximate the optimal analysis trajectory. Wind components are post-processed to get wind speeds and to estimate wind energy capacities. A matrix-free analysis step is derived from avoiding the direct inversion of covariance matrices during assimilation cycles. Numerical simulations are employed to illustrate how our proposed framework can be employed in operational scenarios. A catalogue of twelve Wind Turbine Generators (WTGs) is utilized during the experiments. The results reveal that our proposed framework can properly estimate wind energy potential capacities for all wind turbines within reasonable accuracies (in terms of Root-Mean-Square-Error) and even more, these estimations are better than those of traditional 4D-Var ensemble-based methods. Moreover, large variability (variance of standard deviations) of errors are evidenced in forecasts of wind turbines with the largest rate-capacity while homogeneous variability can be seen in wind turbines with the lowest rate-capacity.

Estimation of Wind Energy Potential for Two Locations in North-East Region of Nigeria

This paper presents an estimation of wind power potential of North East, Nigeria (Bauchi and Maiduguri) on the basis of monthly wind speed data at 10m height from the ground. The data for the locations were collected from Nigeria metrological station, Abuja for the period of (2013-2017). Mean monthly values were used in calculation of Weibull distribution parameters c (scale factor ms-1) and k (shape factor). The Weibull results shows that for Bauchi, the shape factor ranges from 2.86 – 5.96 and scale factor ranges from 2.32ms-1 – 2.54ms-1 while Maiduguri the shape factor ranges from 2.66 – 5.52 and values of scale factor ranges from 4.74ms-1 – 5.89ms-1. It is evident that the maximum average monthly value of wind speed in Bauchi occurs in year 2017 with value of 3.8ms-1 in the month of May while the maximum average wind speed in Maiduguri occurs in year 2013 with value of 8.5ms-1 in the month of December. The probability distribution function f(V) of wind speed, together with the duration function T(V) was evaluated for the period under investigation. From the statistical analysis of distributions, the Weibull distribution was found to have better fittings in the probability distribution functions f(V) and T(V). The value of power density was computed to be 33.47W/m2 (class I) & 374.62W/m2 (class II) and energy density was also computed to be 24.9 kWh/m2 & 278kWh/m2 for both Bauchi and Maiduguri respectively.

Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

Machine learning and geographic information systems for large-scale wind energy potential estimation in rural areas

Clean, safe, affordable and available in the long-term, wind is one of the most promising sources of renewable energy. Its optimized and profitable use, however, requires an estimation of the potential in locations of interest, given its very volatile behavior in various settings. In the present study, we propose a methodology using a combination of Machine Learning (Random Forests), Geographic Information Systems and wind parametric models to estimate the large-scale theoretical wind speed potential in rural areas over the entire Switzerland. The monthly wind speed over rural areas is estimated based on wind speed measurements and several meteorological, topographic, and wind-specific features available accross the country. Wind speed values and their associated uncertainty are computed at the scale of 200 x 200 [m2] pixels covering the territory, at a typical height for rural commercial wind turbine installation, that is, z=100m. The developed methodology, is, however, applicable to any large region, given the availability of data of interest. The results show that in the case of Switzerland, wind turbines could approximately represent an non-negligible installed power capacity of, for each pixel and for each turbine installation, on average 80 kW in Swiss rural areas, and up to 1600 kW in most suitable pixels.

Research on Facilities of Power Supply of Small Power Capability Consumers of Bukhara Region by using Wind and Solar Energy

This article analyzes the climatic features of the Bukhara region – the southern-eastern part of Uzbekistan. The wind and solar potential of the region and the prospects of its utilization were evaluated. In recent years, wind and solar energy has been analyzed worldwide. The potential for the use of solar radiation in the region was analyzed. Accordingly, when the solar photovoltaic batteries in the region are used (coefficient of efficiency-18%), the technical potential of the region is 41 TWh/year. Wind speeds and wind energy potential data were collected over eight years (2011-2018) and were calculated using the monthly wind speed data measured every 30 minutes at a height of 10m. During the estimation of wind energy potential we were used two parameters Weibull and Rayleigh distribution functions. The density power and energy values of the wind flow at various heights were evaluated using the extrapolation method. In accordance with that, on height of 10m it makes 41.19 W/m2 and 361.48 kWh/m2, on height of 50m 117.23 W/m2 1026.96 kWh/m2, and on height of 100m 192.76 W/m2, 1688.59 kWh/m2. The wind energy gross, technical and economic potential of the region has been evaluated. In addition, the potential and prospects of using hybrid (wind and solar) power stations for the supply of uninterrupted and reliable power supply to consumers in the region were analyzed

Evaluation of Wind Power for Electrical Energy Generation in the Mediterranean Coast of Palestine for 14 Years

The generation, distributionand transmission of electricity in Palestine have recently emerged as major issues. This study comprehensively assesses the production of wind energy and the estimation of wind energy potential in Palestine’s south coastal plain. The goal is to evaluate Palestine’s wind energy production by studying wind data and calculating energy and power. This study analyses two actual time series datasets. Findings are elaborated to determine the wind energy conversion per 1 m2. The wind speed data from January 1996 to December 2006 in Gaza and from January 2012 to December 2015 in Ashqelon are selected as the data sample. This study is crucial given the need for clean and renewable energy, the power shortage in the Gaza Strip and the limited number of wind energy studies conducted in the south coastal plain of Palestine, especially Gaza Strip. This study estimates the wind energy potential of the Gaza Strip to determine the wind potential. The annual mean wind speed and power are 4.11 ms-1 and 903.4 Wm-2, respectively. Moreover, the study clarifies the electrical energy generation in the Gaza Strip using small-scale turbines and offers a feasible alternative to existing schemes.

Wind Power Curve Modeling With Asymmetric Error Distribution

Wind power curves play important roles in developing and utilizing wind energy, including wind power forecasting, wind turbine condition monitoring, wind energy potential estimation, and wind turbine selection. In this paper, we show that the error distribution in wind power curve modeling is asymmetric and complex, rather than a Gaussian distribution. Considering this error characteristic, we develop two novel asymmetric spline regression models, MoAG-ASR and MoAEP-ASR, in which the error terms are assumed to obey mixture of asymmetric Gaussian distributions (MoAG) and mixture of asymmetric exponential power distribution (MoAEP), respectively. We compare the proposed models with 16 popular wind power curve fitting models, and find that the proposed models rank higher than the others. Moreover, besides the asymmetry, two statistics (skewness and kurtosis) also show that the error distribution in wind power curve modeling is thick-tailed and leptokurtic. However, MoAG and MoAEP can precisely fit such complex error distribution. Thus, MoAG-ASR and MoAEP-ASR are effective tools for wind power curve modeling.

Wind energy potential estimation with prediction of wind speed distribution

Integration of renewable distributed generation technology with radial power distribution system is rapidly growing. Among the number of available renewable energy sources, the role of wind energy in the power sector is very important. In this work, authors have estimated the wind energy potential at Banaras Hindu University (BHU), India. Estimation of wind energy potential depends on statistical analysis of wind speed distribution, which will give us the best-fitted probability density function of the desired location. For statistical analysis of wind speed distribution, authors have taken hourly data of BHU centre from the source of National Renewable Energy Laboratory (NREL), USA for the 12-month period. A statistical R-programming language tool has been used for the computational work. In this work, authors have considered Weibull, Gamma and Lognormal probability distributions for getting best-fitted distribution. After the analysis, result shows for the given wind speed data of BHU area, Weibull distribution is the best-fitted one.

Wind energy potential estimation with prediction of wind speed distribution

Wind energy potential estimation with prediction of wind speed distribution

Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

In this article, offshore wind energy potential is measured around the Iberian Mediterranean coast and the Balearic Islands using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson’s correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation’s spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation.

Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS

This paper, focused on offshore wind resource assessment of the Persian Gulf using uncertainty method. In this method, for better estimation of wind energy potential, the uncertainties due to wind speed, sea surface roughness, air density and wind turbine power production were considered. For this purpose, first, a 25-year (1984–2008) ECMWF (European Center for Medium-Range Weather Forecasts) data from over 550 points in the region were collected. Then by analyzing the data, the probability of density of each parameter was established. Next; a Monte Carlo simulation was used for long-term simulation of wind field and energy production. The results showed that the Persian Gulf is capable of generating over 2980 GWh per year which is 5.3% lower than results of the conventional analysis. A detailed GIS (Geographic Information System) analysis depicted that middle and southern parts of the region have a high level of wind energy potential. Also, an economic analysis showed that by 15% interest rate, offshore wind farms cannot compete the global electricity production rates; however, by decreasing the interest rate to 5%, the southern, southwestern, and a part of northern shoreline are capable of generating offshore wind electricity in the range of global offshore electricity production costs.

Comparison of wind tunnel and on site measurements for urban wind energy estimation of potential yield

There is a need to compare wind tunnel results with field measurement wind data in order to examine the validity of wind tunnel data in providing realistic estimates of urban wind energy potential and assess the probable errors involved. The paper refers to and discusses two Montreal building cases with different upstream roughness homogeneity. In the first case, field wind speed measurements are used to calculate the wind energy potential for a building with upstream rather homogeneous suburban type of terrain. In the second case, the building upstream terrain is very rough and highly nonhomogeneous. The calculated wind energy potential based on the field measurements was compared with the estimated value based on respective boundary layer wind tunnel data. In the first case, where the upstream terrain is homogeneous, the difference between the estimation of wind energy potential and the calculation using the field measurements is less than 5%. However, in the second case with the nonhomogeneous upstream terrain conditions, the difference between the estimation of the wind energy potential and the calculation using the field measurements is increased by up to 20%.