scholarly journals Winds of Change: How Up-To-Date Forecasting Methods Could Help Change Brazilian Wind Energy Policy and Save Billions of US$

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2952 ◽  
Author(s):  
Fernando Bernardes ◽  
Douglas Vieira ◽  
Vasile Palade ◽  
Rodney Saldanha

This paper proposes a revaluation of the Brazilian wind energy policy framework and the energy auction requirements. The proposed model deals with the four major issues associated with the wind policy framework that are: long-term wind speed sampling, wind speed forecasting reliability, energy commercialization, and the wind farm profitability. Brazilian wind policy, cross-checked against other countries policies, showed to be too restrictive and outdated. This paper proposes its renewal, through the adoption of international standards by Brazilian policy-makers, reducing the wind time sampling necessary to implement wind farms. To support such a policy change, a new wind forecasting method is designed. The method is based on fuzzy time series shaped with a statistical significance approach. It can be used to forecast wind behavior, by drawing the most-likely wind energy generation intervals given a confidence degree. The proposed method is useful to evaluate a wind farm profitability and design the biding strategy in auctions.

2021 ◽  
Vol 13 (18) ◽  
pp. 10465
Author(s):  
Huey-Shian Chung

Taiwan’s offshore wind energy policy (OWE policy) is a response to sustainable development goals. Offshore wind energy has become one of the fastest growing renewable energies on Taiwan’s coastline, with the government’s full support for the promotion and implementation of the OWE policy. With the operation of Taiwan’s first wind farm in 2021, increasing controversies are specifically concerned with the distribution of social, economic, and environmental burdens and benefits resulting from the OWE policy. More offshore wind farms are forthcoming by 2025. However, little attention has been paid to policy dilemmas for many aspects relevant to sustainable development. Therefore, this paper conducts a policy analysis to construct policy-relevant information of the OWE policy and identifies policy dilemmas in relation to concerns about sustainable development. This paper presents policy recommendations on the design and decision-making processes for facilitating the smooth promotion and implementation of Taiwan’s OWE policy and future renewable energy policies.


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.


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.


2013 ◽  
Vol 72 ◽  
pp. 103-110 ◽  
Author(s):  
B. Yaniktepe ◽  
M.M. Savrun ◽  
T. Koroglu

2020 ◽  
Author(s):  
Yang-Ming Fan

<p>The purpose of this study is to develop an ensemble-based data assimilation method to accurately predict wind speed in wind farm and provide it for the use of wind energy intelligent forecasting platform. As Taiwan government aimed to increase the share of renewable energy generation to 20% by 2025, among them, the uncertain wind energy output will cause electricity company has to reserve a considerable reserve capacity when dispatching power, and it is usually high cost natural gas power generation. In view of this, we will develop wind energy intelligent forecasting platform with an error of 10% within 72 hours and expect to save hundred millions of dollars of unnecessary natural gas generators investment. Once the wind energy can be predicted more accurately, the electricity company can fully utilize the robustness and economy of smart grid supply. Therefore, the mastery of the change of wind speed is one of the key factors that can reduce the minimum error of wind energy intelligent forecasting.</p><p>There are many uncertainties in the numerical meteorological models, including errors in the initial conditions or defects in the model, which may affect the accuracy of the prediction. Since the deterministic prediction cannot fully grasp the uncertainty in the prediction process, so it is difficult to obtain all possible wind field changes. The development of ensemble-based data assimilation prediction is to make up for the weakness of deterministic prediction. With the prediction of 20 wind fields as ensemble members, it is expected to include the uncertainty of prediction, quantify the uncertainty, and integrate the wind speed observations of wind farms as well to provide the optimal prediction of wind speed for the next 72 hours. The results show that the prediction error of wind speed within 72 hours is 6% under different weather conditions (excluding typhoons), which proves that the accuracy of wind speed prediction by combining data assimilation technology and ensemble approach is better.</p>


Energy Policy ◽  
2014 ◽  
Vol 74 ◽  
pp. 621-631 ◽  
Author(s):  
M. Fera ◽  
R. Iannone ◽  
R. Macchiaroli ◽  
S. Miranda ◽  
M.M. Schiraldi

2021 ◽  
Vol 49 (1) ◽  
pp. 244-251
Author(s):  
Narayanan Natarajan ◽  
S. Rehman ◽  
Nandhini Shiva ◽  
M. Vasudevan

An accurate estimate of wind resource assessment is essential for the identification of potential site for wind farm development. The hourly average wind speed measured at 50 m above ground level over a period of 39 years (1980-2018) from 25 locations in Tamil Nadu, India have been used in this study. The annual and seasonal wind speed trends are analyzed using linear and Mann-Kendall statistical methods. The annual energy yield, and net capacity factor are obtained for the chosen wind turbine with 2 Mega Watt rated power. As per the linear trend analysis, Chennai and Kanchipuram possess a significantly decreasing trend, while Nagercoil, Thoothukudi, and Tirunelveli show an increasing trend. Mann-Kendall trend analysis shows that cities located in the southern peninsula and in the vicinity of the coastal regions have significant potential for wind energy development. Moreover, a majority of the cities show an increasing trend in the autumn season due to the influence of the retreating monsoons which is accompanied with heavy winds. The mean wind follows an oscillating pattern throughout the year at all the locations. Based on the net annual energy output, Nagercoil, Thoothukudi and Nagapattinam are found to be the most suitable locations for wind power deployment in Tamil Nadu, followed by Cuddalore, Kumbakonam, Thanjavur and Tirunelveli.


Sign in / Sign up

Export Citation Format

Share Document