scholarly journals Wind Energy Assessment during High-Impact Winter Storms in Southwestern Europe

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 509
Author(s):  
Ana Gonçalves ◽  
Margarida L. R. Liberato ◽  
Raquel Nieto
Keyword(s):  
Wind Energy ◽  
Power Law ◽  
Energy Production ◽  
Weather Conditions ◽  
High Impact ◽  
Energy Potential ◽  
Energy Assessment ◽  
Logarithmic Law ◽  
Strong Winds ◽  
Wind Resource

The electricity produced through renewable resources is dependent on the variability of weather conditions and, thus, on the availability of the resource, as is the case with wind energy. This study aims to assess the wind resource available and the wind energy potential (WEP) during the December months for the three years 2017, 2018, and 2019, in southwestern Europe, when several high-impact storms affected the region. Additionally, a comparison of Prandtl’s logarithmic law and Power-law equations for extrapolation of the vertical wind profile is performed for onshore conditions, to evaluate the differences in terms of energy production, with the use of different equations. To assess the effect of the strong winds associated with the storms, 10 m wind components are used, with a 6-hourly temporal resolution, for the December months over the southwestern Europe region (30° N–65° N; 40° W–25° E). Results are compared to the climatology (1981–2010) and show an increase of wind intensity of 1.86 m·s−1 in southwestern Europe during December 2019, and a decrease up to 2.72 m·s−1 in December 2018. WEP is calculated for the selected wind turbine, 4 MW E-126 EP3—ENERCON, as well as the values following the wind resource record, that is, (i) higher values in December 2019 in the offshore and onshore regions, reaching 35 MWh and 20 MWh per day, respectively, and (ii) lower values in December 2018, with 35 MWh and 15 MWh per day for offshore and onshore. Differences in WEP when using the two equations for extrapolation of wind vertical profile reached 60% (40%) in offshore (onshore) regions, except for the Alps, where differences of up to 80% were reached. An additional analysis was made to understand the influence of the coefficients of soil roughness and friction used in each equation (Prandtl’s logarithmic law and Power-law), for the different conditions of onshore and offshore. Finally, it is notable that the highest values of wind energy production occurred on the stormy days affecting southwestern Europe. Therefore, we conclude that these high-impact storms had a positive effect on the wind energy production in this region.

Wind Energy Assessment in Southwestern Europe in December months 2017 to 2020

2021 ◽  
Author(s):  
Ana Gonçalves ◽  
Margarida L.R. Liberato ◽  
Raquel Nieto
Keyword(s):  
Wind Energy ◽  
Iberian Peninsula ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Energy Potential ◽  
Hourly Data ◽  
Wind Energy Potential ◽  
Strong Winds ◽  
Wind Intensity

<p>Renewable resources are dependent on the variability of weather conditions and thus on the availability of the resource as it is the case of wind energy. The huge expansion of the worldwide wind power capacity to produce electricity makes this technology vulnerable to extreme weather conditions such as those associated with extratropical cyclones and extreme weather events (Gonçalves <em>et al</em>., 2020). This work aims to assess the wind resources available and the wind energy potential (WEP) during recent December months (the years 2017 to 2020) in the southwestern Europe. These winter months were characterized by high impact storms with strong winds associated which caused extensive damage. In this region, a total of 10 intense named storms occurred in December: 2017 (Ana, Bruno, and Carmen); 2018 (Etienne and Flora); 2019 (Daniel, Elsa, and Fabien); 2020 (Dora and Ernest) (IPMA; AEMet; Météo France; 2021). To understand the effect of the strong winds associated with the passage of the storms during these months, the ERA5 Reanalysis 10m wind components (10-meter U and V wind components) are retrieved from the European Centre for Medium Range Weather Forecasts (ECMWF) (Hersbach <em>et al</em>., 2019).  The fields were extracted at 00, 06, 12 and 18 UTC (6-hourly data), for the 2017, 2018, 2019 and 2020 December months over a geographical sector that covers the southwestern Europe region (30°N–65°N; 40°W–25°E) and compared to climatological values for the 1981-2010 period. Moreover, the wind energy potential was calculated for the respective December months and the values compared and associated with the values of renewable energy reports available for the Iberian Peninsula and the countries of southwestern Europe. Obtained results show an increase of wind intensity of up to 2 m.s<sup>-1</sup> in southwestern Europe during December 2017 and 2019 and a decrease of 2 m.s<sup>-1</sup> in December 2018, when compared with the respective climatology for the 1981-2010 period. In December 2020, a significant increase of wind intensity reaching up to 2.8 m.s<sup>-1</sup> in the Bay of Biscay region, affecting the Iberian Peninsula and the west coast of France. The increase in wind resource resulted in an increase in wind potential in the months under study. These values are in line with the values of wind energy produced during the months analyzed for the EU-28 countries. Finally, it is shown that the highest values of wind production occurred during the days when the storms hit southwestern Europe.</p><p><strong>Acknowledgements</strong></p><p>This work is supported by Fundação para a Ciência e a Tecnologia – FCT through the projects PTDC/CTA-MET/29233/2017 (WEx-Atlantic) and UID/GEO/50019/2020. Partial support was also obtained from the Xunta de Galicia under the Project ED431C 2017/64-GRC Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas (Grupos de Referencia Competitiva) and Consellería de Educación e Ordenación Universitaria, cofunding from the ERDF, in the framework of the Operational Program Galicia 2014–2020.</p><p> </p><p><strong>References</strong></p><p>Agencia Estatal Meteorología, 2021. Online: http://www.aemet.es/es/conocermas/borrascas </p><p>Gonçalves et al., 2020. ECAS 2020, doi:10.3390/ecas2020-08132</p><p>Hersbach et al.,  2019. ECMWF Newsletter, Vol 159, pp. 17–24, doi: 10.21957/vf291hehd7</p><p>Instituto Português do Mar e da Atmosfera, 2021. Online: https://www.ipma.pt/en/index.html </p><p>Météo France, 2021. Online: http://www.meteofrance.com/accueil </p>

Wind Energy Assessment of Michigan City, United States

2018 ◽  
Author(s):  
Michael K. Okorie ◽  
Uzumma O. Ozeh ◽  
Xiuling Wang
Keyword(s):  
United States ◽  
Wind Speed ◽  
Wind Energy ◽  
Energy Production ◽  
Alternative Energy ◽  
Maximum Energy ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Energy Assessment ◽  
Solar Powered

There is a growing need for an environmentally friendly source of energy that can replace the conventional fossil fuel energy. This is because the effects of global warming is becoming very obvious, as evidenced by the severe flooding that occurred in the U.S. in 20171. Two notable solutions to this dilemma are wind and solar energy. Solar powered devices derive their energy from the sun, hence, the amount of energy is severely limited during the cold months of the year when solar intensity is typically low. Wind energy, on the other hand is most prevalent during this cold months when the wind speed is typically higher. The aim of this research is therefore to conduct a comprehensive assessment of wind energy potential in Michigan City, Indiana, United States. This information will allow homeowners and investors with interest in alternative energy to make critical decisions in this regard. The study was conducted using wind speed data collected over a five-year period from 2012 to 2016. In this work, we have also determined the best method for evaluating the Weibull parameters (shape and scale factors) for wind data analysis. The site average wind speed ranged from 4m/s to 9m/s with a peak in the winter months and minimum in the summer months. The wind speed with the maximum energy at the hub height varied between 5.84 m/s in August 2016 to 12.79 m/s in October 2012 with annual average speeds between 8.85 and 9.35 m/s and a five-year average of 9.13 m/s. The prevailing wind speed was within the range of 4–8m/s and strongest on the Southern part of the site especially in the South Southeastern direction. Consequently, siting a wind turbine on the Southern part of the City would generate more energy than on any other direction. Among the turbines analyzed, ITALTECH 250 will yield the maximize energy production with a capacity factor of 0.385 and average annual energy production of 840 MWh/yr. The results presented in this work proves the great potential for investments in wind energy in Michigan City.

scholarly journals The Wind Energy Potential of Kurdistan, Iran

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Farzad Arefi ◽  
Jamal Moshtagh ◽  
Mohammad Moradi
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbines ◽  
Wind Farms ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Energy Assessment ◽  
Technical Specifications ◽  
Weather Stations ◽  
Calm Wind

In the current work by using statistical methods and available software, the wind energy assessment of prone regions for installation of wind turbines in, Qorveh, has been investigated. Information was obtained from weather stations of Baneh, Bijar, Zarina, Saqez, Sanandaj, Qorveh, and Marivan. The monthly average and maximum of wind speed were investigated between the years 2000–2010 and the related curves were drawn. The Golobad curve (direction and percentage of dominant wind and calm wind as monthly rate) between the years 1997–2000 was analyzed and drawn with plot software. The ten-minute speed (at 10, 30, and 60 m height) and direction (at 37.5 and 10 m height) wind data were collected from weather stations of Iranian new energy organization. The wind speed distribution during one year was evaluated by using Weibull probability density function (two-parametrical), and the Weibull curve histograms were drawn by MATLAB software. According to the average wind speed of stations and technical specifications of the types of turbines, the suitable wind turbine for the station was selected. Finally, the Divandareh and Qorveh sites with favorable potential were considered for installation of wind turbines and construction of wind farms.

Wind resource assessment and wind farm modeling in Mossobo-Harena area, North Ethiopia

2020 ◽  
pp. 0309524X2092540
Author(s):  
Addisu Dagne Zegeye
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Power Density ◽  
Energy Production ◽  
Wind Farm ◽  
Ground Level ◽  
Energy Potential ◽  
Mean Power ◽  
Wind Energy Potential

Although Ethiopia does not have significant fossil fuel resource, it is endowed with a huge amount of renewable energy resources such as hydro, wind, geothermal, and solar power. However, only a small portion of these resources has been utilized so far and less than 30% of the nation’s population has access to electricity. The wind energy potential of the country is estimated to be up to 10 GW. Yet less than 5% of this potential is developed so far. One of the reasons for this low utilization of wind energy in Ethiopia is the absence of a reliable and accurate wind atlas and resource maps. Development of reliable and accurate wind atlas and resource maps helps to identify candidate sites for wind energy applications and facilitates the planning and implementation of wind energy projects. The main purpose of this research is to assess the wind energy potential and model wind farm in the Mossobo-Harena site of North Ethiopia. In this research, wind data collected for 2 years from Mossobo-Harena site meteorological station were analyzed using different statistical software to evaluate the wind energy potential of the area. Average wind speed and power density, distribution of the wind, prevailing direction, turbulence intensity, and wind shear profile of the site were determined. Wind Atlas Analysis and Application Program was used to generate the generalized wind climate of the area and develop resource maps. Wind farm layout and preliminary turbine micro-sitting were done by taking various factors into consideration. The IEC wind turbine class of the site was determined and an appropriate wind turbine for the study area wind climate was selected and the net annual energy production and capacity factor of the wind farm were determined. The measured data analysis conducted indicates that the mean wind speed at 10 and 40 m above the ground level is 5.12 and 6.41 m/s, respectively, at measuring site. The measuring site’s mean power density was determined to be 138.55 and 276.52 W/m2 at 10 and 40 m above the ground level, respectively. The prevailing wind direction in the site is from east to south east where about 60% of the wind was recorded. The resource grid maps developed by Wind Atlas Analysis and Application Program on a 10 km × 10 km area at 50 m above the ground level indicate that the selected study area has a mean wind speed of 5.58 m/s and a mean power density of 146 W/m2. The average turbulence intensity of the site was found to be 0.136 at 40 m which indicates that the site has a moderate turbulence level. According to the resource assessment done, the area is classified as a wind Class IIIB site. A 2-MW rated power ENERCON E-82 E2 wind turbine which is an IEC Class IIB turbine with 82 m rotor diameter and 98 m hub height was selected for estimation of annual energy production on the proposed wind farm. 88 ENERCON E-82 E2 wind turbines were properly sited in the wind farm with recommended spacing between the turbines so as to reduce the wake loss. The rated power of the wind farm is 180.4 MW and the net annual energy production and capacity factor of the proposed wind farm were determined to be 434.315 GWh and 27.48% after considering various losses in the wind farm.

Assessment of climate change impacts on wind resource characteristics and wind energy potential in Greece

2019 ◽  
Vol 11 (6) ◽  
pp. 066502 ◽  
Author(s):  
Theodoros Katopodis ◽  
Diamando Vlachogiannis ◽  
Nadia Politi ◽  
Nikolaos Gounaris ◽  
Stelios Karozis ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Energy ◽  
Climate Change Impacts ◽  
Energy Potential ◽  
Wind Energy Potential ◽  
Resource Characteristics ◽  
Wind Resource

Wind Energy Potential Assessment and Wind Turbine Performance Investigation in the Cotonou Coast (Benin Republic)

2019 ◽  
Vol 45 ◽  
pp. 89-98
Author(s):  
Maurel Aza-Gnandji ◽  
François Xavier Fifatin ◽  
Frédéric Dubas ◽  
Christophe Espanet ◽  
Antoine Vianou
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Maximum Energy ◽  
Capacity Factor ◽  
Energy Potential ◽  
Turbine Performance ◽  
Best Fitting ◽  
Wind Energy Potential ◽  
Wind Resource ◽  
Benin Republic

This paper presents a study of the monthly variability of wind energy potential at several heights and an investigation of the best fitting commercial wind turbine in the Cotonou coast (Benin Republic). The monthly Weibull parameters are calculated at 10 m and extrapolated at 30 and 50 m heights. The monthly Weibull wind power density and the wind speed carrying maximum energy are calculated at 10, 30 and 50 m. We showed that wind resource in the Cotonou coast is favorable for wind energy production at 30 and 50 m heights. The capacity factor of selected commercial wind turbines is calculated to investigate the best fitting wind turbine in the Cotonou coast. It turns out that Polaris 19-50 is the best fitting wind turbine in the selected turbines with a mean capacity factor of 0.49.

Determination of Yearly Wind Energy Potential and Extraction of Wind Energy Using Wind Turbine for Coastal Cities of Baluchistan, Pakistan

2019 ◽  
Vol 10 (3) ◽  
pp. 56-63
Author(s):  
Muhammad Shoaib ◽  
Imran Siddiqui ◽  
Saif Ur Rehman
Keyword(s):  
Wind Energy ◽  
Goodness Of Fit ◽  
Estimation Methods ◽  
Energy Potential ◽  
Goodness Of Fit Test ◽  
Wind Speeds ◽  
Energy Assessment ◽  
Weibull Pdf ◽  
Parameter Estimation Methods ◽  
Two Parameters

04 March, 2019 Accepted: 24 April, 2019Abstract: Wind energy assessment of Ormara, Gwadar and Lasbela wind sites which are located in provinceBaluchistan is presented. The daily averaged wind speed data for the three sites is recorded for a period of four yearsfrom 2010-2013 at mast heights 7 m, 9.6 m and 23 m. Measured wind data are extrapolated to heights 60 m (Ormara),80 m (Gwadar) and 60 m (Lasbela). Yearly averaged wind speeds are modeled using a two parameters Weibullfunction whose shape (k) and scale (c) parameters are computed using seven well known numerical iterative methods.Reliability of the fitting process is assessed by employing three goodness-of-fit test statistics, namely, RMSE, R2 and χ2tests. Tests indicate that MLE, MLM and EPFM outperformed other Weibull parameter estimation methods for a betterfit behavior. Yearly Weibull pdf and cdf are obtained and Weibull wind characteristics are determined. Wind turbinesEcotecnia 60/1.67 MW and Nordex S77 1500 kW are used to extract wind energy on yearly basis. Estimated yearlyWeibull power densities are in the range 623.00 - 700.13 W/m2, 276.04 – 307.55 W/m2 and 66.85 – 75.93 W/m2 forOrmara, Gwadar and Lasbela respectively. Extracted wind energy values for Ormara and Gwadar using wind turbinesare reported as ca. 8623 kWh and ca. 4622 kWh, respectively.

scholarly journals Uncertainty Analysis in MCP-Based Wind Resource Assessment and Energy Production Estimation

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Matthew A. Lackner ◽  
Anthony L. Rogers ◽  
James F. Manwell
Keyword(s):  
Wind Energy ◽  
Energy Production ◽  
Closed Form Solution ◽  
Resource Assessment ◽  
Form Solution ◽  
Mathematical Framework ◽  
Site Assessment ◽  
Wind Resource Assessment ◽  
Uncertainty Sources ◽  
Wind Resource

This paper presents a mathematical framework to properly account for uncertainty in wind resource assessment and wind energy production estimation. A meteorological tower based wind measurement campaign is considered exclusively, in which measure-correlate-predict is used to estimate the long-term wind resource. The evaluation of a wind resource and the subsequent estimation of the annual energy production (AEP) is a highly uncertain process. Uncertainty arises at all points in the process, from measuring the wind speed to the uncertainty in a power curve. A proper assessment of uncertainty is critical for judging the feasibility and risk of a potential wind energy development. The approach in this paper provides a framework for an accurate and objective accounting of uncertainty and, therefore, better decision making when assessing a potential wind energy site. It does not investigate the values of individual uncertainty sources. Three major aspects of site assessment uncertainty are presented here. First, a method is presented for combining uncertainty that arises in assessing the wind resource. Second, methods for handling uncertainty sources in wind turbine power output and energy losses are presented. Third, a new method for estimating the overall AEP uncertainty when using a Weibull distribution is presented. While it is commonly assumed that the uncertainty in the wind resource should be scaled by a factor between 2 and 3 to yield the uncertainty in the AEP, this work demonstrates that this assumption is an oversimplification and also presents a closed form solution for the sensitivity factors of the Weibull parameters.

scholarly journals Assessment of Wind Energy Potential In Zwara, Libya

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Alhassan A. Teyabeen ◽  
Fathi R. Akkari ◽  
Ali E. Jwaid ◽  
Ashraf Zaghwan ◽  
Rehab Abodelah
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Ground Level ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Wind Power Density ◽  
Wind Energy Potential ◽  
Wind Resource

To assess the wind energy potential at any site, the wind power density should be estimated; it evaluates the wind resource and indicates the amount of available wind energy. The purpose of this study is to estimate the monthly and annual wind power density based on the Weibull distribution using wind speed data collected in Zwara, Libya during 2007. The wind date are measured at the three hub heights of 10m, 30m, and 50m above ground level, and recorded every 10 minutes. The analysis showed that the annual average wind speed are 4.51, 5.86, 6.26 m/s for the respective mentioned heights. The average annual wind power densities at the mentioned heights were 113.71, 204.19, 243.48 , respectively.

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