Optimal Micrositing and Annual Energy Production Prediction for Wind Farm Using Long-term Wind Speed Correlation Between AWS and MERRA

2016 ◽  
Vol 40 (4) ◽  
pp. 201-212
Author(s):  
Mi Ho Park ◽  
Bum Suk Kim
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Production Prediction

ADAMA-II wind farm performance assessment in comparison to feasibility study

2021 ◽  
pp. 0309524X2110351
Author(s):  
Abeba Debru ◽  
Mulu Bayray ◽  
Marta Molinas
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Performance Assessment ◽  
Feasibility Study ◽  
Energy Production ◽  
Wind Farm ◽  
Weather Conditions ◽  
Turbine Generators ◽  
Wind Turbine Generators

The objective of this paper was to assess the performance of the Adama-II Wind Farm in comparison to the feasibility study. Using 1-year mast data, the site potential was reassessed by WAsP software and the performance of wind turbine generators was assessed by 2 years of SCADA data. The obtained mean annual wind speed and power density were 7.75 m/s, and 462 W/m2 while in the feasibility study, 9.55 m/s, and 634.6 W/m2, which resulted in 18.8%, and 27.1% deviations respectively. The prevailing and secondary wind directions obtained were ENE and NE with 35.7% and 19.1% while, in the feasibility study, ENE with 36.5% and E with 17.3%. From the SCADA data, the Capacity factor, Annual Energy Production (AEP), and Availability of wind turbines were determined as 30.5%, 398 GWh, and 95.1%. The reasons for the deviation were difference in long-term correction data and weather conditions during study period.

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.

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.

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.

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.

scholarly journals Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization

2018 ◽  
Author(s):  
Andrés Santiago Padrón ◽  
Jared Thomas ◽  
Andrew P. J. Stanley ◽  
Juan J. Alonso ◽  
Andrew Ning
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Energy Production ◽  
Wind Farm ◽  
Polynomial Chaos ◽  
Optimization Problems ◽  
Probability Distributions ◽  
Regression Result ◽  
Computationally Efficient ◽  
Wind Farm Optimization

Abstract. In this paper, we develop computationally-efficient techniques to calculate statistics used in wind farm optimization with the goal of enabling the use of higher-fidelity models and larger wind farm optimization problems. We apply these techniques to maximize the Annual Energy Production (AEP) of a wind farm by optimizing the position of the individual wind turbines. The AEP (a statistic itself) is the expected power produced by the wind farm over a period of one year subject to uncertainties in the wind conditions (wind direction and wind speed) that are described with empirically-determined probability distributions. To compute the AEP of the wind farm, we use a wake model to simulate the power at different input conditions composed of wind direction and wind speed pairs. We use polynomial chaos (PC), an uncertainty quantification method, to construct a polynomial approximation of the power over the entire stochastic space and to efficiently (using as few simulations as possible) compute the expected power (AEP). We explore both regression and quadrature approaches to compute the PC coefficients. PC based on regression is significantly more efficient than the rectangle rule (the method most commonly used to compute the expected power). With PC based on regression, we have reduced by as much as an order of magnitude the number of simulations required to accurately compute the AEP, thus enabling the use of more expensive, higher-fidelity models or larger wind farm optimizations. We perform a large suite of gradient-based optimizations with different initial turbine locations and with different numbers of samples to compute the AEP. The optimizations with PC based on regression result in optimized layouts that produce the same AEP as the optimized layouts found with the rectangle rule but using only one-third of the samples. Furthermore, for the same number of samples, the AEP of the optimal layouts found with PC is 1 % higher than the AEP of the layouts found with the rectangle rule.

scholarly journals A Comparative Study of Activation Functions of NAR and NARX Neural Network for Long-Term Wind Speed Forecasting in Malaysia

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Rasel Sarkar ◽  
Sabariah Julai ◽  
Sazzad Hossain ◽  
Wen Tong Chong ◽  
Mahmudur Rahman
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Power Generation ◽  
Activation Functions ◽  
Wind Speed Forecasting ◽  
Nonlinear Autoregressive ◽  
Narx Neural Network

Since wind power is directly influenced by wind speed, long-term wind speed forecasting (WSF) plays an important role for wind farm installation. WSF is essential for controlling, energy management and scheduled wind power generation in wind farm. The proposed investigation in this paper provides 30-days-ahead WSF. Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN) with different network settings have been used to facilitate the wind power generation. The essence of this study is that it compares the effect of activation functions (namely, tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. A set of wind speed data was collected from different meteorological stations in Malaysia, situated in Kuala Lumpur, Kuantan, and Melaka. The proposed activation functions tansig of NARNN and NARXNN resulted in promising outcomes in terms of very small error between actual and predicted wind speed as well as the comparison for the logsig transfer function results.

scholarly journals On wake modeling, wind-farm gradients and AEP predictions at the Anholt wind farm

2017 ◽  
Author(s):  
Alfredo Peña ◽  
Kurt Schaldemose Hansen ◽  
Søren Ott ◽  
Maarten Paul van der Laan
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Mesoscale Model ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Westerly Flow ◽  
Wake Model ◽  
Inflow Conditions ◽  
Speed Gradient

Abstract. We investigate wake effects at the Anholt offshore wind farm in Denmark. We perform the analysis with three commonly-used wake models; two engineering approaches (the Park and G. C. Larsen models) and a linearized Reynolds-averaged Navier-Stokes approach (Fuga). From analysis of SCADA and mesoscale model simulations, we show that for westerly flow in particular, there is a clear horizontal wind-speed gradient over the wind farm, which results from the effect of the land nearby. We also show that for annual energy production estimates, in which a wake model is run with inflow conditions derived from mesoscale model outputs, accounting for the horizontal wind-speed gradient gives nearly the same results as averaging all the wake-free wind climates at the turbines' positions or using the wind climate of a position in the middle of the wind farm. However, annual energy production estimates can largely differ when using wind climates that are strongly influenced by the wind-speed gradient. When looking at westerly flow wake cases, where the impact of the wind-speed gradient is largest, the wake models agree with the SCADA fairly well; when looking at a southerly flow case, where the wake losses are highest, they tend to underestimate the wake loss. With the mesoscale-wake model setup, we are also able to estimate the capacity factor of the wind farm rather well when compared to that derived from the SCADA. Finally, we estimate the uncertainty of the wake models and some of its variants by bootstrapping the SCADA. The models tend to underestimate the wake losses and the engineering wake models are as uncertain as Fuga. These results are specific for this wind farm, the available dataset, and the derived inflow conditions.

scholarly journals Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization

2019 ◽  
Vol 4 (2) ◽  
pp. 211-231 ◽  
Author(s):  
Andrés Santiago Padrón ◽  
Jared Thomas ◽  
Andrew P. J. Stanley ◽  
Juan J. Alonso ◽  
Andrew Ning
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Energy Production ◽  
Wind Farm ◽  
Polynomial Chaos ◽  
Optimization Problems ◽  
Computational Cost ◽  
Layout Optimization ◽  
Wind Farm Layout Optimization ◽  
Wind Farm Optimization

Abstract. In this paper, we develop computationally efficient techniques to calculate statistics used in wind farm optimization with the goal of enabling the use of higher-fidelity models and larger wind farm optimization problems. We apply these techniques to maximize the annual energy production (AEP) of a wind farm by optimizing the position of the individual wind turbines. The AEP (a statistic) is the expected power produced by the wind farm over a period of 1 year subject to uncertainties in the wind conditions (wind direction and wind speed) that are described with empirically determined probability distributions. To compute the AEP of the wind farm, we use a wake model to simulate the power at different input conditions composed of wind direction and wind speed pairs. We use polynomial chaos (PC), an uncertainty quantification method, to construct a polynomial approximation of the power over the entire stochastic space and to efficiently (using as few simulations as possible) compute the expected power (AEP). We explore both regression and quadrature approaches to compute the PC coefficients. PC based on regression is significantly more efficient than the rectangle rule (the method most commonly used to compute the expected power). With PC based on regression, we have reduced on average by a factor of 5 the number of simulations required to accurately compute the AEP when compared to the rectangle rule for the different wind farm layouts considered. In the wind farm layout optimization problem, each optimization step requires an AEP computation. Thus, the ability to compute the AEP accurately with fewer simulations is beneficial as it reduces the cost to perform an optimization, which enables the use of more computationally expensive higher-fidelity models or the consideration of larger or multiple wind farm optimization problems. We perform a large suite of gradient-based optimizations to compare the optimal layouts obtained when computing the AEP with polynomial chaos based on regression and the rectangle rule. We consider three different starting layouts (Grid, Amalia, Random) and find that the optimization has many local optima and is sensitive to the starting layout of the turbines. We observe that starting from a good layout (Grid, Amalia) will, in general, find better optima than starting from a bad layout (Random) independent of the method used to compute the AEP. For both PC based on regression and the rectangle rule, we consider both a coarse (∼225) and a fine (∼625) number of simulations to compute the AEP. We find that for roughly one-third of the computational cost, the optimizations with the coarse PC based on regression result in optimized layouts that produce comparable AEP to the optimized layouts found with the fine rectangle rule. Furthermore, for the same computational cost, for the different cases considered, polynomial chaos finds optimal layouts with 0.4 % higher AEP on average than those found with the rectangle rule.

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