On the wind resource in Algeria: Probability distributions evaluation

The Mean ◽

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.

Wind Speed using Improved Mixture Weibull Distribution

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f7896.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 1871-1874

Keyword(s):

Wind Speed ◽

Probability Distribution ◽

Weibull Distribution ◽

Wind Farms ◽

Resource Assessment ◽

Estimation Of Parameters ◽

Basic Properties ◽

Proposed Model ◽

Wind is random in nature both in space and in time. Several technologies are used in wind resource assessment (WRA).The appropriate probability distribution used to calculate the available wind speed at that particular location and the estimation of parameters is the essential part in installing wind farms. The improved mixture Weibull distribution is proposed model which is the mixture of two and three parameter Weibull distribution with parameters including scale, shape, location and weight component. The basic properties of the proposed model and estimation of parameters using various methods are discussed.

Comparison of three probability distributions and techno-economic analysis of wind energy production along the coastal belt of Pakistan

Energy Exploration & Exploitation ◽

10.1177/0144598720931587 ◽

2020 ◽

pp. 014459872093158 ◽

Cited By ~ 1

Author(s):

Muhammad Sumair ◽

Tauseef Aized ◽

Syed Asad Raza Gardezi ◽

Muhammad Mahmood Aslam Bhutta ◽

Syed Muhammad Sohail Rehman ◽

...

Keyword(s):

Wind Speed ◽

Energy Production ◽

Probability Distributions ◽

Resource Assessment ◽

Wind Data ◽

Coefficient Of Determination ◽

Resource Estimation ◽

Coastal Belt ◽

Continuous probability distributions have long been used to model the wind data. No single distribution can be declared accurate for all locations. Therefore, a comparison of different distributions before actual wind resource assessment should be carried out. Current work focuses on the application of three probability distributions, i.e. Weibull, Rayleigh, and lognormal for wind resource estimation at six sites along the coastal belt of Pakistan. Four years’ (2015–2018) wind data measured each 60-minutes at 50 m height for six locations were collected from Pakistan Meteorological Department. Comparison of these distributions was done based on coefficient of determination ( R2), root mean square error, and mean absolute percentage deviation. Comparison showed that Weibull distribution is the most accurate followed by lognormal and Rayleigh, respectively. Wind power density ( PD) was evaluated and it was found that Karachi has the highest wind speed and PD as 5.82 m/s and 162.69 W/m2, respectively, while Jiwani has the lowest wind speed and PD as 4.62 m/s and 76.76 W/m2, respectively. Furthermore, feasibility of annual energy production (AEP) was determined using six turbines. It was found that Vestas V42 shows the worst performance while Bonus 1300/62 is the best with respect to annual energy production and Bonus 600/44 is the most economical. Finally, sensitivity analysis was carried out.

Bayesian Method for Estimating Weibull Parameters for Wind Resource Assessment in the Equatorial Region: a Comparison Between Two-parameter and Three-parameter Weibull Distributions

10.21203/rs.3.rs-504670/v1 ◽

2021 ◽

Author(s):

M. G. M. Khan ◽

M. Rafiuddin Ahmed

Keyword(s):

Weibull Distribution ◽

Bayesian Approach ◽

Resource Assessment ◽

Equatorial Region ◽

Weibull Parameters ◽

Wind Speeds ◽

Low Wind Speeds ◽

Wind Resource ◽

Two Parameter

Abstract The two-parameter Weibull distribution has garnered much attention in the assessment of wind energy potential. The estimation of the shape and scale parameters of the distribution has brought forth a successful tool for the wind energy industry. However, it may be inappropriate to use the two-parameter Weibull distribution to accurately characterize wind speed at every location, especially at sites where the frequency of low speed is high, such as the Equatorial region. In this work, for the robustness in wind resource assessment, we first propose a Bayesian approach in estimating Weibull parameters. Secondly, we compare the techniques of wind resource assessment using both two and three-parameter Weibull distributions for different sites in the Equatorial region. The Bayesian inference approach is adopted using Markov Chain Monte Carlo (MCMC) algorithms. Simulation studies conducted in this research confirms that the Bayesian approach seems to be a new robust alternative technique for accurate estimation of Weibull parameters. An appropriate Weibull distribution and the application of the Bayesian approach in estimating distribution parameters were determined using data from six sites in the Equatorial region from 1° N of Equator to 19° South of Equator. Results revealed that a three-parameter Weibull distribution is a better fit for wind data having a greater percentage of low wind speeds (0-1 m/s) and low skewness. However, wind data with a smaller percentage of low wind speeds and high skewness showed better results using a two-parameter Weibull distribution. The results also demonstrate that the proposed Bayesian approach to estimate Weibull parameters is extremely useful in the analysis of wind power potential, as it provides more accurate results while characterizing lower wind speeds.

Wind Resource Assessment for the Kingdom of Bahrain

Wind Engineering ◽

10.1260/030952408786411976 ◽

2008 ◽

Vol 32 (5) ◽

pp. 439-448 ◽

Cited By ~ 14

Author(s):

Hanan Al Buflasa ◽

David Infield ◽

Simon Watson ◽

Murray Thomson

Keyword(s):

Wind Speed ◽

Energy Production ◽

Resource Assessment ◽

Measured Data ◽

Shape Parameters ◽

Main Island ◽

Mean Wind Speed ◽

Wind Resource ◽

Hourly Basis

The geographical distribution of wind speed (the wind atlas) for the kingdom of Bahrain is presented, based on measured data and on calculations undertaken using WAsP,. The data used were recorded by the Meteorological Directorate at a weather station situated at Bahrain International Airport, taken on an hourly basis for a period of time extended for ten years. These data indicate an annual mean wind speed of 4.6 m/s at 10 m height and mean Weibull scale and shape parameters C and k of 5.2 m/s and 1.9 respectively. At a typical wind turbine hub height of sixty metres, these values are extrapolated to 6.9 m/s, 7.8 m/s and 1.8 respectively, which suggests that the area has a good wind resource. The wind atlas shows that several locations in the less populated central and southern regions of the main island of the archipelago of Bahrain are potentially suitable for wind energy production.

Horizontal Extrapolation of Wind Speed Distribution Using Neural Network for Wind Resource Assessment

International Journal of Science and Research (IJSR) ◽

10.21275/art20178810 ◽

2017 ◽

Vol 6 (12) ◽

pp. 1498-1504

Keyword(s):

Neural Network ◽

Wind Speed ◽

Resource Assessment ◽

Speed Distribution ◽

Wind Speed Distribution ◽

Year-to-year correlation, record length, and overconfidence in wind resource assessment

Wind Energy Science ◽

10.5194/wes-1-115-2016 ◽

2016 ◽

Vol 1 (2) ◽

pp. 115-128 ◽

Cited By ~ 3

Author(s):

Nicola Bodini ◽

Julie K. Lundquist ◽

Dino Zardi ◽

Mark Handschy

Keyword(s):

Interannual Variability ◽

Resource Assessment ◽

Energy Estimates ◽

Data Set ◽

Wind Speeds ◽

Wide Range ◽

Serial Correlations ◽

Resource Levels ◽

Abstract. Interannual variability of wind speeds presents a fundamental source of uncertainty in preconstruction energy estimates. Our analysis of one of the longest and geographically most widespread extant sets of instrumental wind-speed observations (62-year records from 60 stations in Canada) shows that deviations from mean resource levels persist over many decades, substantially increasing uncertainty. As a result of this persistence, the performance of each site's last 20 years diverges more widely than expected from the P50 level estimated from its first 42 years: half the sites have either fewer than 5 or more than 15 years exceeding the P50 estimate. In contrast to this 10-year-wide interquartile range, a 4-year-wide range (2.5 times narrower) was found for "control" records where statistical independence was enforced by randomly permuting each station's historical values. Similarly, for sites with capacity factor of 0.35 and interannual variability of 6  %, one would expect 9 years in 10 to fall in the range 0.32–0.38; we find the actual 90  % range to be 0.27–0.43, or three times wider. The previously un-quantified effect of serial correlations favors a shift in resource-assessment thinking from a climatology-focused approach to a persistence-focused approach: for this data set, no improvement in P50 error is gained by using records longer than 4–5 years, and use of records longer than 20 years actually degrades accuracy.

A model for vertical wind speed data extrapolation for improving wind resource assessment using WAsP

Renewable Energy ◽

10.1016/j.renene.2011.11.016 ◽

2012 ◽

Vol 41 ◽

pp. 407-411 ◽

Cited By ~ 25

Author(s):

Željko Đurišić ◽

Jovan Mikulović

Keyword(s):

Wind Speed ◽

Resource Assessment ◽

Vertical Wind ◽

Wind Speed Data ◽

Vertical Wind Speed ◽

Data Extrapolation ◽

An Overview of Wind Resource Assessments With Special Reference to The Emirate of Ajman, UAE

Renewable Energy and Environmental Sustainability ◽

10.1051/rees/2021033 ◽

2021 ◽

Vol 6 ◽

pp. 32

Author(s):

Kais Muhammed Fasel ◽

Abdul Salam K. Darwish ◽

Peter Farrell ◽

Hussein Kazem

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Clean Energy ◽

Resource Assessment ◽

Site Specific ◽

Onshore Wind ◽

Wind Potential ◽

The Government ◽

The continuous increase in clean energy demand and reduced CO2 emissions in the UAE and specifically the Emirate of Ajman has put an extreme challenge to the Government. Ajman is one of the seven emirates constituting the United Arab Emirates (UAE). Ajman is located along the Arabian Gulf on its West and bordered by the Emirate of Sharjah on its North, South, and East. The government is taking huge steps in including sustainability principles and clean energy in all of its developments. Successful implementation of green architecture law decree No 10 of 2018 effectively is a sign of such an initiative. Renewable energy sources in this country have had two folds of interest in solar and wind. Recent research works supported the feasibility of using wind energy as an alternative clean source of energy. Site-specific and accurate wind speed information is the first step in the process of bankable wind potential and wind Atlas. This study has compared how wind speed and its distribution varies for similar offshore and onshore locations between two different mesoscale data sources. Also, discussed the main environmental characteristics of Ajman that would influence the implementation of a major wind energy project. In addition, the study made a brief critical overview of the major studies undertaken in the Middle East and North Africa (MENA) region on wind resource assessment. Finally, based on the results, the study makes conclusions, recommendations and a way forward for a bankable wind resources assessment in the Emirate of Ajman. This paper would alert the wind energy industry about the consequence of not considering the best error corrected site specific suitable wind resource data along with other environmental characteristics. The study results show that for offshore, there is 2.9 m/s and for Onshore 4.9 m/s variations in wind speed at the same location between ECMWF Reanalysis (ERA-5) and NASA Satellite data. Hence It is concluded that error corrected site-specific wind resource assessment is mandatory for assessing the available bankable wind potential since there are considerable variations in wind speed distributions between mesoscale data sets for similar locations. The study also identifies that the Emirate of Ajman has limited space for onshore wind farms; hence the offshore site seems to have good potential that can be utilised for energy generation. However, individual wind turbines can be installed for exploiting the available site-specific onshore wind energy. Finally, the study recommends a way forward for a comprehensive wind resource assessment to help the Emirate of Ajman form a sustainable wind power generation policy.

Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China

Remote Sensing ◽

10.3390/rs12060973 ◽

2020 ◽

Vol 12 (6) ◽

pp. 973

Author(s):

Wenqing Xu ◽

Like Ning ◽

Yong Luo

Keyword(s):

Wind Speed ◽

Data Assimilation ◽

Satellite Data ◽

Surface Wind ◽

Wrf Model ◽

Wind Farms ◽

Resource Assessment ◽

Near Surface ◽

With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms.