scholarly journals Supplementary material to "Are Uncertainty Categories in a Wind Farm Annual Energy Production Estimate Actually Uncorrelated?"

Author(s):  
Nicola Bodini ◽  
Mike Optis
Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3615
Author(s):  
Adelaide Cerveira ◽  
Eduardo J. Solteiro Pires ◽  
José Baptista

Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms’ cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.


2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie

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.


2020 ◽  
pp. 0309524X2092540
Author(s):  
Addisu Dagne Zegeye

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.


2011 ◽  
Vol 347-353 ◽  
pp. 3545-3550 ◽  
Author(s):  
Chang Xu ◽  
Yan Yan ◽  
De You Liu ◽  
Yuan Zheng ◽  
Chen Qi Li

In order to increase wind energy utilization efficiency by the optimization of the wind farm micro sitting, a method which could calculate the wind farm velocity is proposed by consideration of multi turbines wake loss and superposition. Based on the given velocity data of a wind farm, the maximal annual energy production is set as the optimal objective and the ordinates of wind turbines would be the optimal variables, micro sittings of the wind farm turbines are optimized by genetic algorithm. Layout calculation result of the optimal method is quite similar to that of other successful search method, but higher efficiency is reached, and the micro sitting layout is agreement with the regular plum-type layout. Annual energy productions are also calculated under the condition of different wind turbine number. Results show annual energy production increases with the wind turbine number increased, but the increasing trend is lower and lower. The research could provide a reference to wind farm micro-sitting.


2018 ◽  
Vol 42 (6) ◽  
pp. 624-632 ◽  
Author(s):  
Alexander Gleim ◽  
Rolf-Erik Keck ◽  
John Amund Lund

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.


2018 ◽  
Vol 64 ◽  
pp. 06010
Author(s):  
Bachhal Amrender Singh ◽  
Vogstad Klaus ◽  
Lal Kolhe Mohan ◽  
Chougule Abhijit ◽  
Beyer Hans George

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.


2018 ◽  
Vol 8 (9) ◽  
pp. 1668 ◽  
Author(s):  
Jianghai Wu ◽  
Tongguang Wang ◽  
Long Wang ◽  
Ning Zhao

This article presents a framework to integrate and optimize the design of large-scale wind turbines. Annual energy production, load analysis, the structural design of components and the wind farm operation model are coupled to perform a system-level nonlinear optimization. As well as the commonly used design objective levelized cost of energy (LCoE), key metrics of engineering economics such as net present value (NPV), internal rate of return (IRR) and the discounted payback time (DPT) are calculated and used as design objectives, respectively. The results show that IRR and DPT have the same effect as LCoE since they all lead to minimization of the ratio of the capital expenditure to the energy production. Meanwhile, the optimization for NPV tends to maximize the margin between incomes and costs. These two types of economic metrics provide the minimal blade length and maximal blade length of an optimal blade for a target wind turbine at a given wind farm. The turbine properties with respect to the blade length and tower height are also examined. The blade obtained with economic optimization objectives has a much larger relative thickness and smaller chord distributions than that obtained for high aerodynamic performance design. Furthermore, the use of cost control objectives in optimization is crucial in improving the economic efficiency of wind turbines and sacrificing some aerodynamic performance can bring significant reductions in design loads and turbine costs.


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