Weather research & forecasting model and MERRA-2 data for wind energy evaluation at different altitudes in Bolivia

2021 ◽  
pp. 0309524X2110197
Author(s):  
Rober Mamani ◽  
Patrick Hendrick
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Electricity Generation ◽  
Wind Farms ◽  
Forecasting Model ◽  
High Wind ◽  
Wind Speeds ◽  
Energy Evaluation ◽  
Wind Flows ◽  
Different Altitudes

Wind energy is one of the most promising alternatives for a clean and ecological electricity generation. However, the implementation of efficient wind farms requires accurate data and measurements. This work analyses the MERRA-2 satellite datasets to compare and complement it with WRF simulations in different regions and altitudes in Bolivia, such as the Altiplano, Amazon and Chaco. A 41 years of hourly wind speed from MERRA-2 was used to analyze wind averages and characteristics over the year. WRF simulations for representative months were used to analyze wind shear and wind flows along Bolivia. The main results are related to wind speed index in different sites which varied between 0.90 and 1.09 and the periods of high wind speeds that is May—October in the Altiplano, and June—December in the Amazon and Chaco. However, the main findings are the differences between MERRA-2 data and WRF simulations that is linked to the topography of the sites in study.

Hybrid Forecasting Model Based Data Mining and Cuckoo Search: A Case Study of Wind Speed Time Series

2016 ◽  
Vol 9 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Xiangdong Xu ◽  
Xi Song ◽  
Qian Wang ◽  
Zhiyuan Liu ◽  
Jing Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Renewable Energy Sources ◽  
Eastern China ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Original Data ◽  
Vital Role ◽  
Forecasting Model ◽  
Hybrid Forecasting

Wind energy has been part of the fastest growing renewable energy sources that is clean and pollution-free, which has been increasingly gaining global attention, and wind speed forecasting plays a vital role in the wind energy field, however, it has been proven to be a challenging task owing to the effect of various meteorological factors. This paper proposes a hybrid forecasting model, which can effectively make a preprocess for the original data and improve forecasting accuracy, the developed model applies cuckoo search(CS) algorithm to optimize the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined on the wind farms of eastern China and the forecasting performance shows that the developed model is better than some traditional models.

scholarly journals Downscaling and Verification of Maximum Wind Speeds by Using Artificial Neural Networks for New European Wind Atlas and Wind Farm Data 

2021 ◽  
Author(s):  
Melek Akın ◽  
Ahmet Öztopal ◽  
Ahmet Duran Şahin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Wind Turbines ◽  
Electricity Generation ◽  
Wind Farm ◽  
Wind Farms ◽  
Maximum Wind ◽  
Wind Speeds ◽  
Artificial Neural

<p>As is known, wind is a renewable and non-polluting energy resource. In addition, there is no transportation problem in wind energy and it does not require very high technology for electricity generation. Wind turbines are used for electricity generation from kinetic energy of wind. In the point of power curves of these turbines, wind speed must be a certain band. Generally, they do not generate electricity cut-in wind speed that is between 0 and 4 m/s and cut out wind speed that is over 20-25 m/s. Over cut-out values cause breaking down of wind turbines, because high wind speeds create extra mechanical loads on them. Therefore, maximum/extreme winds and their estimation and prediction carry weight in terms of energy generation.</p><p>New European Wind Atlas (NEWA) is the project, within the scope of ERANET+ Program, and the attendants are Belgium, Denmark, Germany, Latvia, Portugal, Spain, Sweden, and Turkey. The aim of NEWA is to present a new wind atlas to the wind industry. In this project, the physical model used for obtaining wind speeds is a numerical weather prediction model named Weather Research and Forecasting (WRF).</p><p>One of the methods, which are developed by imitating of biological properties of living forms in a virtual environment, is Artificial Neural Networks (ANNs). Stimulations taken from the environment by using sense organs are transmitted to brain whereby neurons in a body and brain makes a decision towards these stimulations. That is the working form of ANNs. Moreover, ANNs can be thought as a black box, which processes given data and produces outputs against inputs. Furthermore, they are a method of Artificial Intelligence.</p><p>In this study, maximum wind speeds of 4 different wind farms in Turkey were estimated by using a downscaling method based on ANNs and wind data which were produced in grid points of NEWA Project. Besides that, 8 different levels (10, 50, 75, 100, 150, 200, 250, and 500 m) for each wind farm were considered. As a result of determining the best ANN architectures with sensitivity analysis, it was seen that Levenberg-Marquardt Backpropagation (trainlm) approach as a training algorithm and 9 neurons in each layer are common traits of best ANN architectures. In addition, 50 m for 2 wind farms and 10 m with 75 m for others were determined as an optimum downscaling levels. Moreover, according to downscaling results, correlation values were calculated around 0.80.</p><p><strong>Key Words: </strong>ANN, Downscaling, Maximum wind, NEWA, Turkey, Wind farm.</p>

About the Extension of Wind Turbine Power Curve in the High Wind Region

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Test Case ◽  
Power Curve ◽  
High Wind ◽  
High Wind Speed ◽  
Wind Speeds

The financial sustainability and the profitability of wind farms strongly depend on the efficiency of the conversion of wind kinetic energy. This motivates further research about the improvement of wind turbine power curve. If the site is characterized by a considerable occurrence of very high wind speeds, it can become particularly profitable to update the power curve management. This is commonly done by raising the cut-out velocity and the high wind speed cut-in regulating the hysteresis logic. Doing this, on one side, the wind turbine possibly undergoes strong vibration and loads. On the other side, the energy improvement is almost certain and the point is quantifying precisely its magnitude. In this work, the test case of an onshore wind farm in Italy is studied, featuring 17 2.3 MW wind turbines. Through the analysis of supervisory control and data acquisition (SCADA) data, the energy improvement from the extension of the power curve in the high wind speed region is simulated and measured. This could be useful for wind farm owners evaluating the realistic profitability of the installation of the power curve upgrade on their wind turbines. Furthermore, the present work is useful for the analysis of wind turbine behavior under extremely stressing load conditions.

Autonomous energy supply based on the wind-energy complex and hydrogen energy storage

2019 ◽  
pp. 12-26
Author(s):  
S. I. Nefedkin ◽  
A. O. Barsukov ◽  
M. I. Mozgova ◽  
M. S. Shichkov ◽  
M. A. Klimova
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Power Supply ◽  
Energy Supply ◽  
Heat Supply ◽  
Hot Water ◽  
Successful Implementation ◽  
High Wind ◽  
Wind Generators ◽  
Weak Wind

The paper proposes an alternative scheme of guaranteed electricity and heat supply of an energy-insulated facility with a high potential of wind energy without the use of imported or local fuel. The scheme represents a wind power complex containing the park of wind generators located at the points with high wind potential. The wind generators provide guaranteed power supply even in periods of weak wind. For heat supply of the consumer, all surplus of the electric power goes on thermoelectric heating of water in tanks of accumulators, and also on receiving hydrogen by a method of electrolysis of water. The current heat supply is carried out with the use of hot water storage tanks, and the heat supply during the heat shortage is carried out by burning the stored hydrogen in condensing hydrogen boilers. We have developed the algorithm of calculation and the program "Wind in energy" which allows calculating annual balance of energy and picking up necessary quantity of the equipment for implementation of the scheme proceeding from the annual schedule of thermal and electric loading, and also potential of wind energy in the chosen region. The calculation-substantiation of the scheme proposed in relation to the real energy-insulated object Ust-Kamchatsk (Kamchatka) is carried out. The equipment for the implementation of an alternative energy supply scheme without the use of imported fuel is selected and compared with the traditional energy supply scheme based on a diesel power plant and a boiler house operating on imported fuel. With the introduction of an alternative power supply scheme, the equipment of the traditional scheme that has exhausted its resource can be used for backup power supply. Using climate databases, a number of energy-insulated facilities in the North and East of Russia with high wind energy potential are considered and the conditions for the successful implementation of the energy supply scheme are analyzed. This requires not only a high average annual wind speed, but also a minimum number of days of weak wind. In addition, it is necessary that the profile of the wind speed distribution in the annual section coincides with the profile of the heat load consumption.

SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

2018 ◽  
pp. 28-50
Author(s):  
S. G. Ignatiev ◽  
S. V. Kiseleva
Keyword(s):  
Energy Storage ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Demand ◽  
Diesel Generator ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

scholarly journals Inter-annual variability of wind climates and wind turbine annual energy production

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Improving Lidar-Derived Turbulence Estimates for Wind Energy

2016 ◽  
Author(s):  
Jennifer F. Newman ◽  
Andrew Clifton
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Energy ◽  
Sonic Anemometer ◽  
Turbulence Models ◽  
Volume Averaging ◽  
Turbine Rotor ◽  
Error Reduction ◽  
Power Prediction ◽  
Wind Speeds

Abstract. Remote sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, commercially available lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The algorithm, L-TERRA, can be applied using only data from a stand-alone commercially available lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. L-TERRA was tested on data from three sites – two in flat terrain and one in semicomplex terrain. L-TERRA significantly reduced errors in lidar turbulence at all three sites, even when the machine-learning portion of the model was trained on one site and applied to a different site. Errors in turbulence were then related to errors in power through the use of a power prediction model for a simulated 1.5 MW turbine. L-TERRA also reduced errors in power significantly at all three sites, although moderate power errors remained for periods when the mean wind speed was close to the rated wind speed of the turbine and periods when variance contamination had a large effect on the lidar turbulence error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

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.

Assessment of wind energy potential and characteristics in Qatar for clean electricity generation

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Wind Energy Potential

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.

scholarly journals Catch Characteristics of Precipitation Gauges in High-Latitude Regions with High Winds

2006 ◽  
Vol 7 (5) ◽  
pp. 984-994 ◽  
Author(s):  
Konosuke Sugiura ◽  
Tetsuo Ohata ◽  
Daqing Yang
Keyword(s):  
Wind Speed ◽  
High Latitude ◽  
Cold Climate ◽  
Strong Wind ◽  
Blowing Snow ◽  
High Wind ◽  
Wind Speeds ◽  
Solid Precipitation ◽  
High Winds ◽  
Correction Equations

Abstract Intercomparison of solid precipitation measurement at Barrow, Alaska, has been carried out to examine the catch characteristics of various precipitation gauges in high-latitude regions with high winds and to evaluate the applicability of the WMO precipitation correction procedures. Five manual precipitation gauges (Canadian Nipher, Hellmann, Russian Tretyakov, U.S. 8-in., and Wyoming gauges) and a double fence intercomparison reference (DFIR) as an international reference standard have been installed. The data collected in the last three winters indicates that the amount of solid precipitation is characteristically low, and the zero-catch frequency of the nonshielded gauges is considerably high, 60%–80% of precipitation occurrences. The zero catch in high-latitude high-wind regions becomes a significant fraction of the total precipitation. At low wind speeds, the catch characteristics of the gauges are roughly similar to the DFIR, although it is noteworthy that the daily catch ratios decreased more rapidly with increasing wind speed compared to the WMO correction equations. The dependency of the daily catch ratios on air temperature was confirmed, and the rapid decrease in the daily catch ratios is due to small snow particles caused by the cold climate. The daily catch ratio of the Wyoming gauge clearly shows wind-induced losses. In addition, the daily catch ratios are considerably scattered under strong wind conditions due to the influence of blowing snow. This result suggests that it is not appropriate to extrapolate the WMO correction equations for the shielded gauges in high-latitude regions for high wind speed of over 6 m s−1.

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