scholarly journals Mean Wind Speed Comparison and Wind Farm Energy Prediction at Chang-Hua Offshore (Taiwan)

2016 ◽  
Author(s):  
Meharkumar Barapati ◽  
Jiun-Jih Miau ◽  
Pei-Chi Chang
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Stable Condition ◽  
Wind Farms ◽  
Green Energy ◽  
Offshore Wind ◽  
Wind Data ◽  
Electricity Production ◽  
Average Roughness ◽  
Mean Wind Speed

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

Technical and Economical Investigation of Wind Power Potential in Kuwait

2015 ◽  
Author(s):  
Yousef A. Gharbia ◽  
Haytham Ayoub
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Data ◽  
Electricity Production ◽  
Specific Power ◽  
Wind Rose ◽  
Average Wind Speed ◽  
Prevailing Wind Direction ◽  
Power Curves

The State of Kuwait is considering diversifying its energy sector and not entirely depend on oil. This desire is motivated by Kuwait commitment to reducing its share of pollution, as a result of burning fossil fuel, and to extend the life of its oil and gas reserves. The potential for solar energy in Kuwait is quite obvious; however, it is not the case when it comes to wind energy. The aim of this work was to analyze wind data from several sites in Kuwait and assess their suitability for building large-scale wind farms. The analysis of hourly averaged wind data showed that some sites can have an average wind speed as high as 5.3 m/s at 10 m height. The power density using Weibull distribution function was calculated for the most promising sites. The prevailing wind direction for these sites was also determined using wind-rose charts. The power curves of several Gamesa turbines were used in order to identify the best turbine model in terms of specific power production cost. The results showed that the area of Abraq Al-Habari has the highest potential for building a large-scale wind farm. The payback period of investments was found to be around 7 years and the cost of electricity production was around US Cent 4/kWh.

scholarly journals Creating Synthetic Wind Speed Time Series for 15 New Zealand Wind Farms

2011 ◽  
Vol 50 (12) ◽  
pp. 2394-2409 ◽  
Author(s):  
Richard Turner ◽  
Xiaogu Zheng ◽  
Neil Gordon ◽  
Michael Uddstrom ◽  
Greg Pearson ◽  
...  
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Wind Speed ◽  
Wind Farm ◽  
Central Government ◽  
Wind Farms ◽  
Practical Method ◽  
Wind Data ◽  
Regulatory Oversight ◽  
Short Period

AbstractWind data at time scales from 10 min to 1 h are an important input for modeling the performance of wind farms and their impact on many countries’ national electricity systems. Planners need long-term realistic (i.e., meteorologically spatially and temporally consistent) wind-farm data for projects studying how best to integrate wind power into the national electricity grid. In New Zealand, wind data recorded at wind farms are confidential for commercial reasons, however, and publicly available wind data records are for sites that are often not representative of or are distant from wind farms. In general, too, the public sites are at much lower terrain elevations than hilltop wind farms and have anemometers located at 10 m above the ground, which is much lower than turbine hub height. In addition, when available, the mast records from wind-farm sites are only for a short period. In this paper, the authors describe a novel and practical method to create a multiyear 10-min synthetic wind speed time series for 15 wind-farm sites throughout the country for the New Zealand Electricity Commission. The Electricity Commission (known as the Electricity Authority since 1 October 2010) is the agency that has regulatory oversight of the electricity industry and that provides advice to central government. The dataset was constructed in such a way as to preserve meteorological realism both spatially and temporally and also to respect the commercial secrecy of the wind data provided by power-generation companies.

scholarly journals Recovery processes in a large offshore wind farm

2021 ◽  
Vol 6 (5) ◽  
pp. 1089-1106
Author(s):  
Tanvi Gupta ◽  
Somnath Baidya Roy
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
High Wind ◽  
Offshore Wind Farm ◽  
High Wind Speed ◽  
Recovery Processes ◽  
Speed Range

Abstract. Wind turbines in a wind farm extract energy from the atmospheric flow and convert it into electricity, resulting in a localized momentum deficit in the wake that reduces energy availability for downwind turbines. Atmospheric momentum convergence from above, below, and the sides into the wakes replenishes the lost momentum, at least partially, so that turbines deep inside a wind farm can continue to function. In this study, we explore recovery processes in a hypothetical offshore wind farm with particular emphasis on comparing the spatial patterns and magnitudes of horizontal- and vertical-recovery processes and understanding the role of mesoscale processes in momentum recovery in wind farms. For this purpose, we use the Weather Research and Forecasting (WRF) model, a state-of-the-art mesoscale model equipped with a wind turbine parameterization, to simulate a hypothetical large offshore wind farm with different wind turbine spacings under realistic initial and boundary conditions. Different inter-turbine spacings range from a densely packed wind farm (case I: low inter-turbine distance of 0.5 km ∼ 5 rotor diameter) to a sparsely packed wind farm (case III: high inter-turbine distance of 2 km ∼ 20 rotor diameter). In this study, apart from the inter-turbine spacings, we also explored the role of different ranges of background wind speeds over which the wind turbines operate, ranging from a low wind speed range of 3–11.75 m s−1 (case A) to a high wind speed range of 11–18 m s−1 (case C). Results show that vertical turbulent transport of momentum from aloft is the main contributor to recovery in wind farms except in cases with high-wind-speed range and sparsely packed wind farms, where horizontal advective momentum transport can also contribute equally. Vertical recovery shows a systematic dependence on wind speed and wind farm density that is quantified using low-order empirical equations. Wind farms significantly alter the mesoscale flow patterns, especially for densely packed wind farms under high-wind-speed conditions. In these cases, the mesoscale circulations created by the wind farms can transport high-momentum air from aloft into the atmospheric boundary layer (ABL) and thus aid in recovery in wind farms. To the best of our knowledge, this is one of the first studies to look at wind farm replenishment processes under realistic meteorological conditions including the role of mesoscale processes. Overall, this study advances our understanding of recovery processes in wind farms and wind farm–ABL interactions.

Offshore wind farm far field - Results of the project WIPAFF

2020 ◽  
Author(s):  
Andreas Platis ◽  
Jens Bange ◽  
Konrad Bärfuss ◽  
Beatriz Canadillas ◽  
Marie Hundhausen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Order Approximation ◽  
Wind Farms ◽  
In Situ Measurements ◽  
Offshore Wind ◽  
Stable Conditions ◽  
Wind Park ◽  
Meso Scale

<p>Wind farm far wakes are of particular interest for offshore installations, as turbulence intensity, which is the main driver for wake dissipation, is much lower over the ocean than over land. Therefore, wakes behind offshore wind turbines and wind parks are expected to be much longer than behind onshore parks. </p><p>In situ measurements of the far wakes were missing before the initiation of the research project WIPAFF (WInd PArk Far Fields) in 2015. The main results of which are reported here. WIPAFF has been funded by the German Federal Ministry for Economic Affairs and Energy and ran from November 2015 to April 2019.  The main goal of WIPAFF was to perform a large number of in situ measurements from aircraft operations at hub height behind wind parks in the German Bight (North Sea), to evaluate further SAR images and to update and validate existing meso-scale and industrial models on the basis of the observations to enable a holistic coverage of the downstream wakes.<br> <br>A  unique  dataset  from  airborne in situ data,  remote sensing  by  laser  scanner  and  SAR  gained  during  the WIPAFF  project  proves  that  wakes  up to  several  tens of kilometers exist downstream of offshore wind farms during stable conditions, while under neutral/unstable conditions, the wake length amounts to 15 km or less. Turbulence occurs at the lateral boundaries of the wakes, due to shear between the reduced wind speed inside the wake and the undisturbed flow. Data also indicates that a denser wind park layout increases the wake length additionally due to a higher initial wind speed deficit. The recovery of the decelerated flow in the wake can be modeled as a first order approximation by an exponential function. The project could also reveal that wind-farm parameterizations in the numerical meso-scale WRF model show a feasible agreement with the observations. </p>

scholarly journals The effects of mean wind speed uncertainty on project finance debt sizing for offshore wind farms

2019 ◽  
Vol 252 ◽  
pp. 113419 ◽  
Author(s):  
Esteve Borràs Mora ◽  
James Spelling ◽  
Adriaan H. van der Weijde ◽  
Ellen-Mary Pavageau
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Offshore Wind ◽  
Project Finance ◽  
Offshore Wind Farms ◽  
Mean Wind Speed

scholarly journals MULTI-PERSON SELECTION OF THE BEST WIND TURBINE BASED ON THE MULTI-CRITERIA INTEGRATED ADDITIVE-MULTIPLICATIVE UTILITY FUNCTION

2014 ◽  
Vol 20 (4) ◽  
pp. 590-599 ◽  
Author(s):  
Vygantas Bagočius ◽  
Edmundas Kazimieras Zavadskas ◽  
Zenonas Turskis
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Green Energy ◽  
Offshore Wind ◽  
Modern World ◽  
Multiple Use ◽  
Flexible Tool ◽  
Major Purpose ◽  
Offshore Area

Energy generation and savings is a vital problem for the social and economic development of a modern world. The construction of wind farms is a challenge of crucial importance to Lithuania. Offshore wind farms are one of the possibilities of the multiple use of marine space. Wind energy industry has become the fastest growing renewable energy in the world. An offshore wind farm is considered one of the most promising sources of green energy towards meeting the EU targets for 2020 and 2050. They provide long-term green energy production. The major purpose of this study is the selection and ranking of the feasible location areas of wind farms and assessing the types of wind turbines in the Baltic Sea offshore area. Multi-criteria decision making methods represent a robust and flexible tool investigating and assessing possible discrete alternatives evaluated applying the aggregated WSM and WPM method namely WASPAS. The following criteria such as the nominal power of the wind turbine, max power generated in the area, the amount of energy per year generated in the area, investments and CO2 emissions have been taken into consideration.

scholarly journals Simulation Based Investigation of the Impact of Information Sharing on the Offshore Wind Farm Installation Process

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Moritz Quandt ◽  
Thies Beinke ◽  
Abderrahim Ait-Alla ◽  
Michael Freitag
Keyword(s):  
Resource Availability ◽  
Information Acquisition ◽  
Wind Farm ◽  
Wind Farms ◽  
Limited Resource ◽  
Energy Generation ◽  
Green Energy ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
The Impact

In the recent decades, the introduction of a sustainable and green energy infrastructure, and, by this, the reduction of emissions caused by fossil energy generation, has been focused on by industry-oriented nations worldwide. Among the technologies of renewable energy generation, wind energy has the highest deployment rate, due to the high wind resource availability and the high technology maturity reached mainly by the onshore installation of wind turbines. However, the planning and the installation of offshore wind farms are a challenging task, because of harsh weather conditions and limited resource availability. Due to the current practice of decentralised information acquisition by the supply chain partners, we investigate the impact of sharing information on the installation process of offshore wind farms by means of a simulation model. Therefore, relevant information items will be identified in order to improve the installation process.

Effect of Mean Wind Speed Variations on Power Generation of Wind Farm

2012 ◽  
Vol 215-216 ◽  
pp. 1298-1307
Author(s):  
Chen Guo ◽  
Yan He
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Farm ◽  
High Reliability ◽  
Wind Farms ◽  
Measured Data ◽  
Data Sets ◽  
Mean Wind Speed ◽  
Relationship Of ◽  
The Relationship

Through two methods, wind speed data sets sequence, the elements of which increased in Mean Wind Speed (MWS) orderly, are introduced first, and a numerical integration method depending on Weibull fitting result and power curve data to calculate Power Generation (PG) is proposed in this paper. Then, with measured data of 3 wind farms, PG with different heights are calculated and contrastive studies are made, employing the proposed data sets processing and PG calculating methods. Research results indicate that the PG calculating method has high reliability, and Equivalent Available Duration (EAD) increases about 50-60h when MWS increased by 0.1m/s. The results provide important basis for studies on the relationship of PG variation and measured data correction methods.

scholarly journals Assessing Variability of Wind Speed: Comparison and Validation of 27 Methodologies

2018 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
M. Jason Fields ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Resource Assessment ◽  
Assessment Process ◽  
Wind Speeds ◽  
Statistical Confidence ◽  
Monthly Total ◽  
Mean Wind Speed

Abstract. Because wind resources vary from year to year, the inter-monthly and inter-annual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process thereby causing challenges to wind-farm operators and owners. We present a critical assessment of several common approaches for calculating variability by applying each of the methods to the same 37-year monthly wind-speed and energy-production time series to highlight the differences between these methods. We then assess the accuracy of the variability calculations by correlating the wind-speed variability estimates to the variabilities of actual wind-farm energy production. We recommend the Robust Coefficient of Variation (RCoV) for systematically estimating variability, and we underscore its advantages as well as the importance of using a statistically robust and resistant method. Using normalized spread metrics, including RCoV, high variability of monthly mean wind speeds at a location effectively denotes strong fluctuations of monthly total energy generations, and vice versa. Meanwhile, the wind-speed IAVs computed with annual-mean data fail to adequately represent energy-production IAVs of wind farms. Finally, we find that estimates of energy-generation variability require 10 ± 3 years of monthly mean wind-speed records to achieve 90 % statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.

scholarly journals Pathways to bring the costs down of floating offshore wind farms in the Atlantic Area

2019 ◽  
Author(s):  
Juan José Cartelle-Barros ◽  
David Cordal-Iglesias ◽  
Eugenio Baita-Saavedra ◽  
Almudena Filgueira-Vizoso ◽  
Bernardino Couñago-Lorenzo ◽  
...  
Keyword(s):  
Power Plants ◽  
Wind Farm ◽  
Fossil Fuels ◽  
Net Present Value ◽  
Wind Farms ◽  
Offshore Wind ◽  
Electricity Production ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Atlantic Area

Abstract. Every nations' development lies on the electricity production, since it facilitates life and development of their society (heating, lighting, etc.). Nevertheless, conventional power plants, which use fossil fuels, cause environmental impacts, such as global warming, acidification, eutrophication, among many others. In addition, these conventional resources generate a dependence of external providers, which obstructs the progress of the developing countries. Renewable energies came to solve part of these problems. In this context, wind energy is one the technologies with more expansion all over the world. Offshore locations have a better wind resource than onshore ones and their exploitation is lower. The objective of this work is to present a holistic approach to assess the feasibility of a floating offshore wind farms in a life cycle perspective. The methodology proposed analyses the Net Present Value, the Internal Rate of Return, the Payback Period and the Levelized Cost of Energy of the farm. The case study is built based on a disruptive floating spar-type platform called TELWIND®, to be implemented in the Atlantic Area region. Results indicate how important these parameters are in economic terms and shows the pathways to reduce the costs of this type of infrastructures Furthermore, the methodology proposed allows the selection of the best region where a floating offshore wind farm can be installed. Finally, this study can be useful for Governments and relevant authorities to determine the best location of a floating offshore wind farm and develop the roadmap of offshore wind in their country.

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