scholarly journals Wind potential assessment and optimized turbine distribution in wind farm using WAsP and WindPRO software

2020 ◽  
Vol 2 (3) ◽  
pp. 131-139
Author(s):  
Vinh Thanh Le
Keyword(s):  
Wind Farm ◽  
Offshore Wind ◽  
Wind Data ◽  
Weibull Function ◽  
Input Constraints ◽  
Short Term ◽  
Wind Potential ◽  
Wind Statistics ◽  
Scale Data

In order to develop a wind farm project, the wind potential assessment and siting wind turbine are very important. It directly impacts energy production – a huge influence on the economic efficiency of the wind farm project. So, this paper presents the method to assess wind potential and optimized turbine distribution in Vietnam's offshore wind farm site, based on data from the met mast of GIZ organization (2012 - 2017) at An Ninh Dong commune, Tuy An district, Phu Yen province. The paper presents wind statistics theory from measured data through Weibull function. Comparing the short-term and long-term wind data (from meso-scale data sources – NASA, Hydrometeorological Station ...) is done by module MCP (Measure-Correlate-Predict). Wind potential is assessed when considering the effects of elevation and terrain roughness from wind data that has been long-term adjusted through WAsP and WindPRO software. Jensen model assesses the effects of wake loss between the turbines. The method calculates the power output of the wind farm when considering the influence of turbines is presented, as well as the algorithm of optimized turbine distribution. The optimized turbine distribution is done through WindPRO software. Finally, the turbine distribution results are presented with wind potential has been assessed and the input constraints of optimization.

scholarly journals Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1795
Author(s):  
Woochul Nam ◽  
Ki-Yong Oh
Keyword(s):  
Wind Farm ◽  
Reference Data ◽  
Economic Feasibility ◽  
International Electrotechnical Commission ◽  
Short Term ◽  
Wind Speeds ◽  
Wind Potential ◽  
Wind Resource ◽  
Term Data

Evaluating the economic feasibility of wind farms via long-term wind-resource assessments is indispensable because short-term data measured at a candidate wind-farm site cannot represent the long-term wind potential. Prediction errors are significant when seasonal and year-on-year variations occur. Moreover, reliable long-term reference data with a high correlation to short-term measured data are often unavailable. This paper presents an alternative solution to predict long-term wind resources for a site exhibiting seasonal and year-on-year variations, where long-term reference data are unavailable. An analysis shows that a mutually complementary measure-correlate-predict method can be employed, because several datasets obtained over short periods are used to correct long-term wind resource data in a mutually complementary manner. Moreover, this method is useful in evaluating extreme wind speeds, which is one of the main factors affecting site compliance evaluation and the selection of a suitable wind turbine class based on the International Electrotechnical Commission standards. The analysis also shows that energy density is a more sensitive metric than wind speed for sites with seasonal and year-on-year variations because of the wide distribution of wind speeds. A case study with short-term data measured at Fujeij, Jordan, clearly identifies the factors necessary to perform the reliable and accurate assessment of long-term wind potentials.

scholarly journals Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis

2021 ◽  
Vol 13 (5) ◽  
pp. 2862
Author(s):  
Amer Al-Hinai ◽  
Yassine Charabi ◽  
Seyed H. Aghay Kaboli
Keyword(s):  
Data Analysis ◽  
Wind Energy ◽  
Wind Turbines ◽  
Energy Resource ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Data ◽  
Offshore Wind Energy ◽  
Offshore Wind Turbines ◽  
Wind Potential

Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment and production of offshore wind energy in Oman. A thorough study using bankable wind data along with various logistical considerations would still be required to turn offshore wind potential into real wind farms in Oman.

Comparison of Spar and Semisubmersible Floater Concepts of Offshore Wind Turbines Using Long-Term Analysis

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Hasan Bagbanci ◽  
D. Karmakar ◽  
C. Guedes Soares
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Transfer Functions ◽  
Probability Distributions ◽  
Offshore Wind ◽  
Short Term ◽  
Offshore Wind Turbines ◽  
Floating Wind Turbine ◽  
Offshore Floating Wind Turbine

The long-term probability distributions of a spar-type and a semisubmersible-type offshore floating wind turbine response are calculated for surge, heave, and pitch motions along with the side-to-side, fore–aft, and yaw tower base bending moments. The transfer functions for surge, heave, and pitch motions for both spar-type and semisubmersible-type floaters are obtained using the fast code and the results are also compared with the results obtained in an experimental study. The long-term predictions of the most probable maximum values of motion amplitudes are used for design purposes, so as to guarantee the safety of the floating wind turbines against overturning in high waves and wind speed. The long-term distribution is carried out using North Atlantic wave data and the short-term floating wind turbine responses are represented using Rayleigh distributions. The transfer functions are used in the procedure to calculate the variances of the short-term responses. The results obtained for both spar-type and semisubmersible-type offshore floating wind turbine are compared, and the study will be helpful in the assessments of the long-term availability and economic performance of the spar-type and semisubmersible-type offshore floating wind turbine.

Site‐specific life cycle assessment of a pilot floating offshore wind farm based on suppliers’ data and geo‐located wind data

2020 ◽  
Vol 24 (1) ◽  
pp. 248-262 ◽  
Author(s):  
Baptiste Poujol ◽  
Anne Prieur‐Vernat ◽  
Jean Dubranna ◽  
Romain Besseau ◽  
Isabelle Blanc ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Wind Farm ◽  
Offshore Wind ◽  
Wind Data ◽  
Offshore Wind Farm ◽  
Site Specific

Height Extrapolation of Wind Data

1985 ◽  
Vol 107 (1) ◽  
pp. 10-14 ◽  
Author(s):  
A. S. Mikhail
Keyword(s):  
Wind Speed ◽  
Power Law ◽  
Wind Data ◽  
Power Law Model ◽  
Short Term ◽  
Shape Factors ◽  
Wind Speeds ◽  
Average Wind ◽  
Modified Power Law

Various models that are used for height extrapolation of short and long-term averaged wind speeds are discussed. Hourly averaged data from three tall meteorological towers (the NOAA Erie Tower in Colorado, the Battelle Goodnoe Hills Tower in Washington, and the WKY-TV Tower in Oklahoma), together with data from 17 candidate sites (selected for possible installation of large WECS), were used to analyze the variability of short-term average wind shear with atmospheric and surface parameters and the variability of the long-term Weibull distribution parameter with height. The exponents of a power-law model, fit to the wind speed profiles at the three meteorological towers, showed the same variability with anemometer level wind speed, stability, and surface roughness as the similarity law model. Of the four models representing short-term wind data extrapolation with height (1/7 power law, logarithmic law, power law, and modified power law), the modified power law gives the minimum rms for all candidate sites for short-term average wind speeds and the mean cube of the speed. The modified power-law model was also able to predict the upper-level scale factor for the WKY-TV and Goodnoe Hills Tower data with greater accuracy. All models were not successful in extrapolation of the Weibull shape factors.

scholarly journals Long-term effects of an offshore wind farm in the North Sea on fish communities

2015 ◽  
Vol 528 ◽  
pp. 257-265 ◽  
Author(s):  
C Stenberg ◽  
JG Støttrup ◽  
M van Deurs ◽  
CW Berg ◽  
GE Dinesen ◽  
...  
Keyword(s):  
North Sea ◽  
Wind Farm ◽  
Fish Communities ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Long Term Effects ◽  
The North ◽  
The North Sea

scholarly journals Short-term ecological effects of an offshore wind farm in the Dutch coastal zone; a compilation

2011 ◽  
Vol 6 (3) ◽  
pp. 035101 ◽  
Author(s):  
H J Lindeboom ◽  
H J Kouwenhoven ◽  
M J N Bergman ◽  
S Bouma ◽  
S Brasseur ◽  
...  
Keyword(s):  
Coastal Zone ◽  
Wind Farm ◽  
Ecological Effects ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Short Term

scholarly journals Platform Optimization and Cost Analysis in a Floating Offshore Wind Farm

2020 ◽  
Vol 8 (11) ◽  
pp. 835
Author(s):  
Alberto Ghigo ◽  
Lorenzo Cottura ◽  
Riccardo Caradonna ◽  
Giovanni Bracco ◽  
Giuliana Mattiazzo
Keyword(s):  
Wind Farm ◽  
Weighted Average ◽  
Static Stability ◽  
Offshore Wind ◽  
Capital Investments ◽  
Floating Platform ◽  
Global Weight ◽  
Cost Of Energy ◽  
Wind Potential ◽  
Fixed Structure

Floating offshore wind represents a new frontier of renewable energies. The absence of a fixed structure allows exploiting wind potential in deep seas, like the Atlantic Ocean and Mediterranean Sea, characterized by high availability and wind potential. However, a floating offshore wind system, which includes an offshore turbine, floating platform, moorings, anchors, and electrical system, requires very high capital investments: one of the most relevant cost items is the floating substructure. This work focuses on the choice of a floating platform that minimizes the global weight, in order to reduce the material cost, but ensuring buoyancy and static stability. Subsequently, the optimized platform is used to define a wind farm located near the island of Pantelleria, Italy in order to meet the island’s electricity needs. A sensitivity analysis to estimate the Levelized Cost Of Energy is presented, analyzing the parameters that influence it most, like Capacity Factor, Weighted Average Capital Cost (WACC) and number of wind turbines.

scholarly journals Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 146
Author(s):  
Joongjin Shin ◽  
Seokheum Baek ◽  
Youngwoo Rhee
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Optimization Procedure ◽  
Pso Algorithm ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Metropolitan City ◽  
Wind Potential

This paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. South Korea’s offshore wind farms, which are deep in water and cannot be installed far off the coast, are affected by land complex terrain. Thus, domestic offshore wind farms should consider the separation distance from the coastline as a major variable depending on the topography and marine environmental characteristics. As a case study, a 60 MW offshore wind farm was optimized for the coast of the Busan Metropolitan City. For the analysis of wind conditions in the candidate site, wind conditions data from the meteorological tower and Ganjeolgot AWS at Gori offshore were used from 2001 to 2018. The optimization procedure is performed by evolutionary algorithm (EA) and particle swarm optimization (PSO) algorithm with the purpose of maximizing the AEP while minimizing the total wake loss. The optimization procedure can be applied to the optimized placement of WTs within a wind farm and can be extended for a variety of wind conditions and wind farm capacity. The results of the optimization were predicted to be 172,437 MWh/year under the Gori offshore wind potential, turbine layout optimization, and an annual utilization rate of 26.5%. This could convert 4.6% of electricity consumption in the Busan Metropolitan City region in 2019 in offshore wind farms.

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