scholarly journals Assessing variability of wind speed: comparison and validation of 27 methodologies

2018 ◽  
Vol 3 (2) ◽  
pp. 845-868 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
M. Jason Fields ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Energy Generation ◽  
Resource Assessment ◽  
Assessment Process ◽  
Wind Speeds ◽  
Statistical Confidence ◽  
Mean Wind Speed

Abstract. Because wind resources vary from year to year, the intermonthly and interannual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process, thereby creating challenges for wind farm operators and owners. We present a critical assessment of several common approaches for calculating variability by applying each of the methods to the same 37-year monthly wind-speed and energy-production time series to highlight the differences between these methods. We then assess the accuracy of the variability calculations by correlating the wind-speed variability estimates to the variabilities of actual wind farm energy production. We recommend the robust coefficient of variation (RCoV) for systematically estimating variability, and we underscore its advantages as well as the importance of using a statistically robust and resistant method. Using normalized spread metrics, including RCoV, high variability of monthly mean wind speeds at a location effectively denotes strong fluctuations of monthly total energy generation, and vice versa. Meanwhile, the wind-speed IAVs computed with annual-mean data fail to adequately represent energy-production IAVs of wind farms. Finally, we find that estimates of energy-generation variability require 10±3 years of monthly mean wind-speed records to achieve a 90 % statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.

scholarly journals Assessing Variability of Wind Speed: Comparison and Validation of 27 Methodologies

2018 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
M. Jason Fields ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Resource Assessment ◽  
Assessment Process ◽  
Wind Speeds ◽  
Statistical Confidence ◽  
Monthly Total ◽  
Mean Wind Speed

Abstract. Because wind resources vary from year to year, the inter-monthly and inter-annual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process thereby causing challenges to wind-farm operators and owners. We present a critical assessment of several common approaches for calculating variability by applying each of the methods to the same 37-year monthly wind-speed and energy-production time series to highlight the differences between these methods. We then assess the accuracy of the variability calculations by correlating the wind-speed variability estimates to the variabilities of actual wind-farm energy production. We recommend the Robust Coefficient of Variation (RCoV) for systematically estimating variability, and we underscore its advantages as well as the importance of using a statistically robust and resistant method. Using normalized spread metrics, including RCoV, high variability of monthly mean wind speeds at a location effectively denotes strong fluctuations of monthly total energy generations, and vice versa. Meanwhile, the wind-speed IAVs computed with annual-mean data fail to adequately represent energy-production IAVs of wind farms. Finally, we find that estimates of energy-generation variability require 10 ± 3 years of monthly mean wind-speed records to achieve 90 % statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.

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.

Monte Carlo methods to include the effect of asymmetrical uncertainty sources in wind farm yield assessment

2018 ◽  
Vol 42 (6) ◽  
pp. 624-632 ◽  
Author(s):  
Alexander Gleim ◽  
Rolf-Erik Keck ◽  
John Amund Lund
Keyword(s):  
Monte Carlo ◽  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Sensitivity Study ◽  
Uncertainty Sources ◽  
Industry Standard ◽  
Wind Speed Distribution ◽  
Mean Wind Speed ◽  
A Site

This article presents a method for incorporating the effect on expected annual energy production of a wind farm caused by asymmetric uncertainty distributions of the applied losses and the nonlinear response in turbine production. The necessity for such a correction is best illustrated by considering the effect of uncertainty in the oncoming wind speed distribution on the production of a wind turbine. Due to the shape of the power curve, variations in wind speed will result in a skewed response in annual energy production. For a site where the mean wind speed is higher than 50% of the rated wind speed of the turbine (in practice all sites with sufficiently high wind speed to motivate the establishment of a wind farm), a reduction in mean wind will cause a larger reduction in annual energy production than a corresponding increase in mean wind would increase the annual energy production. Consequently, the expected annual energy production response when considering the uncertainty of the wind will be lower than the expected annual energy production based on the most probable incoming wind. This difference is due to a statistical bias in the industry standard methods to calculate expected annual energy production of a wind farm, as implemented in tools in common use in the industry. A method based on a general Monte Carlo approach is proposed to calculate and correct for this bias. A sensitivity study shows that the bias due to wind speed uncertainty and nonlinear turbine response will be on the order of 0.5% – 1.5% of expected annual energy production. Furthermore, the effect on expected annual energy production due to asymmetrical distributions of site specific losses, for example, loss of production due to ice, can constitute additional losses of several percent.

Wind Resource Assessment for the Kingdom of Bahrain

2008 ◽  
Vol 32 (5) ◽  
pp. 439-448 ◽  
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 ◽  
Wind Resource Assessment ◽  
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.

Effect of Mean Wind Speed Variations on Power Generation of Wind Farm

2012 ◽  
Vol 215-216 ◽  
pp. 1298-1307
Author(s):  
Chen Guo ◽  
Yan He
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Farm ◽  
High Reliability ◽  
Wind Farms ◽  
Measured Data ◽  
Data Sets ◽  
Mean Wind Speed ◽  
Relationship Of ◽  
The Relationship

Through two methods, wind speed data sets sequence, the elements of which increased in Mean Wind Speed (MWS) orderly, are introduced first, and a numerical integration method depending on Weibull fitting result and power curve data to calculate Power Generation (PG) is proposed in this paper. Then, with measured data of 3 wind farms, PG with different heights are calculated and contrastive studies are made, employing the proposed data sets processing and PG calculating methods. Research results indicate that the PG calculating method has high reliability, and Equivalent Available Duration (EAD) increases about 50-60h when MWS increased by 0.1m/s. The results provide important basis for studies on the relationship of PG variation and measured data correction methods.

scholarly journals Wind speed analysis based on the logarithmic wind shear model: a case study for some brazilian cities

2020 ◽  
Vol 9 (7) ◽  
pp. e298973984
Author(s):  
Anny Key de Souza Mendonça ◽  
Antonio Cezar Bornia
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Shear ◽  
Wind Farms ◽  
Shear Model ◽  
The Public ◽  
Wind Speeds ◽  
Generating Capacity ◽  
Mean Wind Speed ◽  
The Mean

The wind power’ share in electricity generating capacity has increased significantly in recent years. Due to the variability in wind power generation, given the variations in wind speed and considering the increase in wind participation in the Brazilian energy matrix, a fact that reinforces the relevance of the source, this article aims to present the methods used to analyze the wind speed more used in the literature and to analyze the wind speed in several Brazilian cities. The logarithmic wind shear model was used to analyze mean wind speeds based on historical data of twelve Brazilian cities available to the public on the ESRL database for a period of eight years 2010 to 2018. The study showed that in localities such as Uruguaiana/RS, Campo Grande/MS, Uberlândia/MG, São Luiz/MA and Corumba/MS, mean wind speeds are strong in all altitudes of reference, with a gain of ± 2m/s of wind speed as the operational altitude increases. The logarithmic wind gain in high altitudes or low altitudes can be seen in z = 100 meters, where the mean wind speed found was Wn ≈ 8 m/s in Uruguaiana/RS and Campo Grande/MS, whereas in Manaus it was Wn ≈ 5 m/s. In Porto Alegre (RS), Florianópolis (SC), Curitiba/PR and Brasília/DF, the mean wind speed in altitudes ≥ 250 m becomes significant, allowing the implementation of wind farms if the technology proves to be economically feasible.

scholarly journals Interannual variability of wind climates and wind turbine annual energy production

2018 ◽  
Vol 3 (2) ◽  
pp. 651-665 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Interannual Variability ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wind Speeds ◽  
Eastern Usa

Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV 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 min 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 or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in 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, the 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 preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Higher Wind: Highlighted Expansion Opportunities to Repower Wind Energy

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7716
Author(s):  
Francisco Haces-Fernandez
Keyword(s):  
Wind Energy ◽  
Wind Farm ◽  
Wind Farms ◽  
Energy Generation ◽  
The United States ◽  
Wind Speeds ◽  
Wind Energy Generation ◽  
Novel Method ◽  
Renewable Energy Generation ◽  
Near Future

Decarbonizing the world economy, before the most damaging effects of climate change become irreversible, requires substantially increasing renewable energy generation in the near future. However, this may be challenging in mature wind energy markets, where many advantageous wind locations are already engaged by older wind farms, potentially generating suboptimal wind harvesting. This research developed a novel method to systematically analyze diverse factors to determine the level of maturity of wind markets and evaluate the adequacy of wind farm repowering at regional and individual levels. The approach was applied to wind markets in the United States (U.S.), in which several states were identified as having diverse levels of maturity. Results obtained from case studies in Texas indicated a consequential number of wind farms that have reached their twenty-year end-of-life term and earlier obsolescence levels. The proposed approach aided in determining wind farms that may benefit from total or partial repowering. The method indicated that total repowering of selected installations would significantly increase overall wind energy generation, considering that these older installations have access to some of the best wind speeds, infrastructure and areas to grow. The proposed method can be applied to different world wind markets.

scholarly journals Improvement of Control Strategy for Pmsg Based Wecs under Strong Wind Conditions with Anfis Controller

2019 ◽  
Vol 8 (4) ◽  
pp. 11184-11189
Keyword(s):  
Wind Speed ◽  
Real Time ◽  
Control Strategy ◽  
Wind Farm ◽  
Wind Farms ◽  
Pi Controller ◽  
Strong Wind ◽  
Varying Parameters ◽  
Wind Speeds ◽  
Time Parameters

Wind farms can only operate and generate power under certain limit of wind speeds if the wind speed is observed beyond the limit then the operation of the wind farm reaches the cutoff region , so in order to operate the wind farm even under strong wind conditions which exceed the limit a control strategy is involved, due to the inability of the PI controller to operate under varying parameters the generated power is affected so in order to adapt a controller that can operate under varying real time parameters a ANFIS controller is adapted in this paper where generated power and d-q axis frame currents are analyzed for linearly rising wind conditions and typhoon landed conditions .

scholarly journals Mean Wind Speed Comparison and Wind Farm Energy Prediction at Chang-Hua Offshore (Taiwan)

2016 ◽  
Author(s):  
Meharkumar Barapati ◽  
Jiun-Jih Miau ◽  
Pei-Chi Chang
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Stable Condition ◽  
Wind Farms ◽  
Green Energy ◽  
Offshore Wind ◽  
Wind Data ◽  
Electricity Production ◽  
Average Roughness ◽  
Mean Wind Speed

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

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