scholarly journals A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation

Energies ◽  
2016 ◽  
Vol 9 (4) ◽  
pp. 286 ◽  
Author(s):  
Birgir Hrafnkelsson ◽  
Gudmundur Oddsson ◽  
Runar Unnthorsson
Keyword(s):  
Monte Carlo ◽  
Wind Speed ◽  
Energy Production ◽  
Speed Simulation

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.

Determination of rated wind speed for maximum annual energy production of variable speed wind turbines

2017 ◽  
Vol 205 ◽  
pp. 781-789 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Arash Hassanzadeh ◽  
Jamaloddin Jamali ◽  
Ali Mostafaeipour ◽  
Wei-Hsin Chen
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Energy Production ◽  
Variable Speed

scholarly journals Lidar arc scan uncertainty reduction through scanning geometry optimization

2016 ◽  
Vol 9 (4) ◽  
pp. 1653-1669 ◽  
Author(s):  
Hui Wang ◽  
Rebecca J. Barthelmie ◽  
Sara C. Pryor ◽  
Gareth. Brown
Keyword(s):  
Theoretical Model ◽  
Wind Speed ◽  
Wind Energy ◽  
Turbulence Intensity ◽  
Wind Direction ◽  
Energy Production ◽  
Elevation Angle ◽  
Relative Standard Error ◽  
Power Performance ◽  
Relative Standard

Abstract. Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.

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.

scholarly journals Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 254
Author(s):  
Minhyeop Kang ◽  
Kyungnam Ko ◽  
Minyeong Kim
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Prediction Models ◽  
Wrf Model ◽  
Coupled Model ◽  
Sea Surface ◽  
Offshore Wind ◽  
Wind Effect ◽  
Observation Data ◽  
Wind Resource

An atmosphere–ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere–ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.

scholarly journals Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine

2020 ◽  
Vol 10 (24) ◽  
pp. 9017
Author(s):  
Andoni Gonzalez-Arceo ◽  
Maitane Zirion-Martinez de Musitu ◽  
Alain Ulazia ◽  
Mario del Rio ◽  
Oscar Garcia
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Energy Production ◽  
Cost Effective ◽  
Reanalysis Data ◽  
Small Scale ◽  
Wide Range ◽  
Wind Measurements ◽  
Scale Experiment ◽  
Wind Resource

In this work, a cost-effective wind resource method specifically developed for the ROSEO-BIWT (Building Integrated Wind Turbine) and other Building Integrated Wind Turbines is presented. It predicts the wind speed and direction at the roof of an previously selected building for the past 10 years using reanalysis data and wind measurements taken over a year. To do so, the reanalysis wind speed data is calibrated against the measurements using different kinds of quantile mapping, and the wind direction is predicted using random forest. A mock-up of a building and a BIWT were used in a wind tunnel to perform a small-scale experiment presented here. It showed that energy production is possible and even enhanced over a wide range of attack angles. The energy production estimations made with the best performing kind of calibration achieved an overall relative error of 6.77% across different scenarios.

scholarly journals Wake and Turbulence Analysis for Wind Turbine Layouts in an Island

2018 ◽  
Vol 64 ◽  
pp. 06010
Author(s):  
Bachhal Amrender Singh ◽  
Vogstad Klaus ◽  
Lal Kolhe Mohan ◽  
Chougule Abhijit ◽  
Beyer Hans George
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Wind Farm ◽  
Full Length Paper ◽  
Turbulence Analysis ◽  
Wake Model ◽  
Wind Resources

There is a big wind energy potential in supplying the power in an island and most of the islands are off-grid. Due to the limited area in island(s), there is need to find appropriate layout / location for wind turbines suited to the local wind conditions. In this paper, we have considered the wind resources data of an island in Trøndelag region of the Northern Norway, situated on the coastal line. The wind resources data of this island have been analysed for wake losses and turbulence on wind turbines for determining appropriate locations of wind turbines in this island. These analyses are very important for understanding the fatigue and mechanical stress on the wind turbines. In this work, semi empirical wake model has been used for wake losses analysis with wind speed and turbine spacings. The Jensen wake model used for the wake loss analysis due to its high degree of accuracy and the Frandsen model for characterizing the turbulent loading. The variations of the losses in the wind energy production of the down-wind turbine relative to the up-wind turbine and, the down-stream turbulence have been analysed for various turbine distances. The special emphasis has been taken for the case of wind turbine spacing, leading to the turbulence conditions for satisfying the IEC 61400-1 conditions to find the wind turbine layout in this island. The energy production of down-wind turbines has been decreased from 2 to 20% due to the lower wind speeds as they are located behind up-wind turbine, resulting in decreasing the overall energy production of the wind farm. Also, the higher wake losses have contributed to the effective turbulence, which has reduced the overall energy production from the wind farm. In this case study, the required distance for wind turbines have been changed to 6 rotor diameters for increasing the energy gain. From the results, it has been estimated that the marginal change in wake losses by moving the down-stream wind turbine by one rotor diameter distance has been in the range of 0.5 to 1% only and it is insignificant. In the full-length paper, the wake effects with wind speed variations and the wind turbine locations will be reported for reducing the wake losses on the down-stream wind turbine. The Frandsen model has been used for analysing turbulence loading on the down-stream wind turbine as per IEC 61400-1 criteria. In larger wind farms, the high turbulence from the up-stream wind turbines increases the fatigues on the turbines of the wind farm. In this work, we have used the effective turbulence criteria at a certain distance between up-stream and down-stream turbines for minimizing the fatigue load level. The sensitivity analysis on wake and turbulence analysis will be reported in the full-length paper. Results from this work will be useful for finding wind farm layouts in an island for utilizing effectively the wind energy resources and electrification using wind power plants.

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