scholarly journals Evaluation of wind speed estimates in reanalyses for wind energy applications

2021 ◽  
Vol 18 ◽  
pp. 115-126
Author(s):  
Sebastian Brune ◽  
Jan D. Keller ◽  
Sabrina Wahl
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Surface Characteristics ◽  
Horizontal Resolution ◽  
Hilly Terrain ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Energy Applications ◽  
Spatio Temporal ◽  
Regional Reanalysis

Abstract. A correct spatio-temporal representation of retrospective wind speed estimates is of large interest for the wind energy sector. In this respect, reanalyses provide an invaluable source of information. However, the quality of the various reanalysis estimates for wind speed are difficult to assess. Therefore, this study compares wind measurements at hub heights from 14 locations in Central Europe with two global (ERA5, MERRA-2) and one regional reanalysis (COSMO-REA6). Employing metrics such as bias, RMSE and correlation, we evaluate the performance of the reanalyses with respect to (a) the local surface characteristics (offshore, flat onshore, hilly onshore), (b) various height levels (60 to 200 m) and (c) the diurnal cycle. As expected, we find that the reanalyses show the smallest errors to observations at offshore sites. Over land, MERRA-2 generally overestimates wind speeds, while COSMO-REA6 and ERA5 represent the average wind speed more realistically. At sites with flat terrain, ERA5 correlates better with observations than COSMO-REA6. In contrast, COSMO-REA6 performs slightly better over hilly terrain, which can be explained by the higher horizontal resolution. In terms of diurnal variation, ERA5 outperforms both other reanalyses. While the overestimation of MERRA-2 is consistent throughout the day, COSMO-REA6 significantly underestimates wind speed at night over flat and hilly terrain due to a misrepresentation of nightly low level jets and mountain and valley breezes. Regarding the representation of downtime of wind turbines due to low/high wind speeds, we find that MERRA-2 is consistently underperforming with respect to the other reanalyses. Here COSMO-REA6 performs better over the ocean, while ERA5 shows the best results over land.

scholarly journals An intercomparison of mesoscale models at simple sites for wind energy applications

2016 ◽  
Author(s):  
Bjarke Tobias Olsen ◽  
Andrea Noemi Hahmann ◽  
Anna Maria Sempreviva ◽  
Jake Badger ◽  
Hans Ejsing Jørgensen
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Best Practice ◽  
Weather Prediction ◽  
Average Wind Speed ◽  
Small Shift ◽  
Mesoscale Models ◽  
Wind Speeds ◽  
Energy Applications ◽  
Mean Wind Speed

Abstract. An intercomparison of model results from 25 different Numerical Weather Prediction (NWP) models is presented for the year 2011 at six sites in Northern Europe characterized by simple terrain. The model results and a detailed description of each model was submitted by 18 different modeling groups to a open call for data, and serves as a rare quantitative overview of the model uncertainties associated with state-of-the-art mesoscale models used for wind energy applications today. At three of the sites the model intercomparison was verified with observations from nearby meteorological masts. The intercomparison was based on statistical properties of the wind for a number of heights at each site. The results show better performance of the models and a smaller inter-model spread offshore and aloft (2–4 % mean wind speed bias above 40 meters), and greater errors and more spread for inland sites and closer to the surface (up to 7–9 % wind speed bias). For the distributions of wind speed, wind direction, and wind shear only small deviations exist between the observations and the average of the models, but a small shift of the average wind speed distribution towards high wind speeds at Cabauw, and an underrepresentation of strong shear cases was observed. Although the model setup options were studied to determine a 'best practice', no significant indicator was found.

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 The  regional reanalysis COSMO-REA6 and its transdisciplinary applications

2021 ◽  
Author(s):  
Andreas Hense ◽  
Keyword(s):  
Horizontal Resolution ◽  
Climatic Variables ◽  
Specific Humidity ◽  
Limited Area ◽  
European Continent ◽  
Scaling Properties ◽  
Energy Applications ◽  
Temperature And Precipitation ◽  
Spatio Temporal ◽  
Regional Reanalysis

<p>In recent years, the reanalysis efforts have been focused on increasing the spatio-temporal resolution to better capture the variability of the atmospheric state between the mesoβ up to synoptic scales. An approach to gain a substantial increase in horizontal resolution are regional reanalyses which utilize a limited area model instead of a global one and thus focus on a specific region of the world. For the European continent several regional reanalyses are now available, e.g., HARMONIE (11km) or COSMO-REA6 (6km).</p><p>This work presents ongoing work of a variety of applications and evaluations made possible through the hourly 6km data of the COSMO-REA6 reanalysis product. <br>Snow and precipitation analysis and events are going to represent better the smaller scales. Here we will show the representation of the scaling properties of the precipitation extremes and an evaluation of the 2005 extreme early winter snow event which happened in Western Germany.  With this reanalysis we also investigated the potential of season identification, finding that gridpoint-wise vertical temperature and wind information provides efficient ways to classify probabilistically a single day into a specific season.  Moreover, the calculation of the bio-climatic variables from COSMO-REA6 allowed the comparison with the Worldclim 2.0 dataset. The main advantage of the newly derived bio-climatic variables is the internal consistency between the temperature and precipitation-related variables. Furthermore, from the increment analysis, we investigate the soil moisture biases and how they relate to the missing representation of irrigation processes in the COSMO model. Results show that there is a similarity in the specific humidity increments and both the irrigation annual cycle and spatial distribution of irrigated areas. With physically consistent spatio-temporal fields it is possible to investigate human discomfort measurements and their relation to temperature indexes. Lastly, extended use of the COSMO-REA6 data was made for renewable energy applications both for wind turbines and photovoltaic cells. The examples given in this presentation will highlight the general potential of high-resolution reanalyses to be part of transdisciplinary problems related to climate and weather variability.  </p>

scholarly journals Analysis of Performance of the Wind-Driven Pulverizing Aerator Based on Average Wind Speeds in the Conditions of Góreckie Lake

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2796
Author(s):  
Andrzej Osuch ◽  
Ewa Osuch ◽  
Stanisław Podsiadłowski ◽  
Piotr Rybacki
Keyword(s):  
Mathematical Model ◽  
Wind Speed ◽  
Water Pump ◽  
Average Wind Speed ◽  
Flow Rates ◽  
Wind Speeds ◽  
Average Wind ◽  
Flow Through ◽  
Analysis Of Performance ◽  
Research Period

In the introduction to this paper, the characteristics of Góreckie lake and the construction and operation of the wind-driven pulverizing aerator are presented. The purpose of this manuscript is to determine the efficiency of the pulverizing aerator unit in the windy conditions of Góreckie Lake. The efficiency of the pulverization aerator depends on the wind conditions at the lake. It was necessary to conduct thorough research to determine the efficiency of water flow through the pulverization segment (water pump). It was necessary to determine the rotational speed of the paddle wheel, which depended on the average wind speed. Throughout the research period, measurements of hourly average wind speed were carried out. It was possible to determine the efficiency of the machine by developing a dedicated mathematical model. The latest method was used in the research, consisting of determining the theoretical volumetric flow rates of water in the pulverizing aerator unit, based on average hourly wind speeds. Pulverization efficiency under the conditions of Góreckie Lake was determined based on 6600 average wind speeds for spring, summer and autumn, 2018. Based on the model, the theoretical efficiency of the machine was calculated, which, under the conditions of Góreckie Lake, amounted to 75,000 m3 per year.

Calculation of Weibull Distribution parameters at low wind speed and performance analysis

2021 ◽  
pp. 1-24
Author(s):  
Yusuf Alper Kaplan
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Statistical Indicator ◽  
Low Wind Speed ◽  
Distribution Parameters ◽  
Wind Energy Potential ◽  
And Performance

In this study, the compatibility of the real wind energy potential to the estimated wind energy potential by Weibull Distribution Function (WDF) of a region with low average wind speed potential was examined. The main purpose of this study is to examine the performance of six different methods used to find the coefficients of the WDF and to determine the best performing method for selected region. In this study seven-year hourly wind speed data obtained from the general directorate of meteorology of this region was used. The root mean square error (RMSE) statistical indicator was used to compare the efficiency of all used methods. Another main purpose of this study is to observe the how the performance of the used methods changes over the years. The obtained results showed that the performances of the used methods showed slight changes over the years, but when evaluated in general, it was observed that all method showed acceptable performance. Based on the obtained results, when the seven-year data is evaluated in this selected region, it can be said that the MM method shows the best performance.

scholarly journals Formation and distribution of sea-surface microlayers

2010 ◽  
Vol 7 (4) ◽  
pp. 5719-5755 ◽  
Author(s):  
O. Wurl ◽  
E. Wurl ◽  
L. Miller ◽  
K. Johnson ◽  
S. Vagle
Keyword(s):  
Wind Speed ◽  
Primary Production ◽  
Sea Surface ◽  
Surface Microlayer ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind ◽  
The Pacific ◽  
Sea Surface Microlayer ◽  
Natural Surfactants

Abstract. Results from a study of surfactants in the sea-surface microlayer (SML) in different regions of the ocean (subtropical, temperate, polar) suggest that this interfacial layer between the ocean and atmosphere covers the ocean's surface to a significant extent. Threshold values at which primary production acts as a significant source of natural surfactants have been derived from the enrichment of surfactants in the SML relative to underlying water and local primary production. Similarly, we have also derived a wind speed threshold at which the SML is disrupted. The results suggest that surfactant enrichment in the SML is typically greater in oligotrophic regions of the ocean than in more productive waters. Furthermore, the enrichment of surfactants persisted at wind speeds of up to 10 m s−1 without any observed depletion above 5 m s−1. This suggests that the SML is stable enough to exist even at the global average wind speed of 6.6 m s−1. Global maps of primary production and wind speed are used to estimate the ocean's SML coverage. The maps indicate that wide regions of the Pacific and Atlantic Oceans between 30° N and 30° S are more significantly affected by the SML than northern of 30° N and southern of 30° S due to higher productivity (spring/summer blooms) and wind speeds exceeding 12 m s−1 respectively.

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 super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

2019 ◽  
Vol 4 (2) ◽  
pp. 343-353 ◽  
Author(s):  
Tyler C. McCandless ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Wind Speed ◽  
Wind Power ◽  
Power Conversion ◽  
Jensen’S Inequality ◽  
Jensen's Inequality ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared with a super-turbine power conversion for a hypothetical wind farm of 100 2 MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with a 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with a 2 m s−1 standard deviation of wind speeds as a consequence of Jensen's inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen's inequality. These significant differences can lead to wind power forecasters overestimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicate that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen's inequality.

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.

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