Offshore Wind Speed Research Articles

Offshore wind energy resource simulation forced by different reanalyses: Comparison with observed data in the Iberian Peninsula

Due to the increasing interest in the prospection of potential sites for the installation of offshore wind farms, it becomes important to extend the tests presented on Carvalho et al. (2014) to offshore areas. For that, the WRF model was used to conduct ocean surface wind simulations forced by different initial and boundary conditions (NCEP-R2, ERA-Interim, NCEP-CFSR, NASA-MERRA, NCEP-FNL and NCEP-GFS) aiming to assess which one of these datasets provides the most accurate ocean surface wind simulation and offshore wind energy estimates. Six near surface wind simulations were performed, each one of them forced by a different initial and boundary dataset. Results were evaluated using data collected at five buoys that measure the wind in the Iberian Peninsula region (Galician coast and Gulf of Cádiz).The results show that the simulation driven with ERA-Interim reanalysis provided the lowest errors in terms of offshore wind temporal variability. NCEP-R2 driven simulation showed the lowest offshore wind speed bias, mean wind speed and offshore wind energy production estimates. However, it was the one with the highest errors related to the wind temporal variability. The simulations driven with the NCEP-FNL and NCEP-GFS analyses products also showed interesting results, better than the NCEP-CFSR and NASA-MERRA reanalyses.Based on the results presented in this work and in Carvalho et al. (2014), ERA-Interim reanalysis likely provide the most accurate initial and boundary data to force near-surface wind simulations for the offshore and onshore areas. However, for offshore sites the NCEP-R2 reanalysis seem to provide the most accurate estimation of the potential wind energy production, fact that is of great importance for the wind energy industry. Furthermore, the NCEP-GFS and NCEP-FNL analyses can be considered as valid alternatives to ERA-Interim and NCEP-R2, in particular for cases where reliable forcing data is needed for real-time applications due to their fast availability.

The Feasibility Study of Offshore Wind Energy Potential in Kijal, Malaysia: The New Alternative Energy Source Exploration in Malaysia

Nowadays, many countries have been endeavoring to explore and exploit the offshore wind energy in terms of solving the problem of limitations of onshore wind energy. Wind energy is one of potential alternative for generating electricity in Malaysia. The offshore wind speed is better compared to onshore, so there is more potential contribution by offshore wind power in terms of electricity generation. This paper provides a summary of the offshore wind resources of the Kijal, Malaysia by analyzing the QuikSCAT satellite data which is available and can be downloaded from the WindPRO application. Based on these datasets, wind resources are assessed, and economical efficiency is evaluated by means of the expected capacity factor. Research was conducted using WindPRO and WAsP software to assess the potential of wind energy along the South China Sea in Kijal, Terengganu. Seven models of the wind turbines with different rated power; 110kW, 225kW, 275kW, 600kW, 850kW, 1000kW and 1250kW are taken for energy and economic feasibility analyses. The wind turbine with rated power 850 kW was indicated as the best wind turbine for installation in Kijal based on its best capacity factor, 26.8% In economical aspect, Malaysia does not have Feed in Tariff (FiT) rates for offshore wind projects, thus the range of predicted FiT rates, RM 0.30 to RM 2.00 were tested by using Net Present Value (NPV) and Payback Period (PBP) calculation. Finally, the conducive FiT rates for wind projects with secured investment were identified and the rates were in range RM 0.80 to RM 1.38. The overall results suggest that the wind energy has a bright future for use as an alternative source for generating electricity in Kijal, Malaysia.

태풍 모의를 통한 해상 설계풍속 추정

Long-term measured wind data are absolutely necessary to estimate extreme offshore wind speed. However, it is almost impossible to collect offshore wind measured data. Therefore, typhoon simulation is widely used to analyze offshore wind conditions. In this paper, 74 typhoons which affected the western sea of Korea during 1978-2012(35 years) were simulated using Holland(1980) model. The results showed that 49.02 m/s maximum wind speed affected by BOLAVEN(1215) at 100 m heights of HeMOSU-1 (Herald of Meteorological and Oceanographic Special Unit 1) was the biggest wind speed for 35 years. Meanwhile, estimated wind speeds were compared with observed data for MUIFA, BOLAVEN, SANBA at HeMOSU-1. And to estimate extreme wind speed having return periods, extreme analysis was conducted by assuming 35 annual maximum wind speed at four site(HeMOSU-1, Gunsan, Mokpo and Jeju) in western sea of the Korean Peninsular to be Gumbel distribution. As a results, extreme wind speed having 50-year return period was 50 m/s, that of 100-year was 54.92 m/s at 100 m heights, respectively. The maximum wind speed by BOLAVEN could be considered as a extreme winds having 50-year return period.

Utilization of excess wind power in electric vehicles

This article describes the assessment of future wind power utilization for charging electric vehicles (EVs) in Germany. The potential wind power production in the model years 2020 and 2030 is derived by extrapolating onshore wind power generation and offshore wind speeds measured in 2007 and 2010 to the installed onshore and offshore wind turbine capacities assumed for 2020 and 2030. The energy consumption of an assumed fleet of 1 million EVs in 2020 and 6 million in 2030 is assessed using detailed models of electric vehicles, real world driving cycles and car usage.It is shown that a substantial part of the charging demand of EVs can be met by otherwise unused wind power, depending on the amount of conventional power required for stabilizing the grid. The utilization of wind power is limited by the charging demand of the cars and the bottlenecks in the transmission grid.

A new turbulence model for offshore wind turbine standards

ABSTRACTCorrect turbulence intensity modeling is crucial for fatigue load estimation for wind turbine structural design. It is well known that the International Electrotechnical Commission 61400‐3 Normal Turbulence Model recommended for offshore wind turbine design is not representative of offshore wind conditions. A new model is urgently needed as offshore wind energy is rapidly developing worldwide. After evaluating the suitability of the Normal Turbulence Model at three sites in Asia, Europe and the USA, it is found that wind–wave interaction and stability correction should be taken into account in modeling the offshore turbulence intensity and wind speed relationship. Therefore, a new turbulence intensity model, which models wind–wave interaction with the Charnock equation and adjusts for the influence of atmospheric stability through empirical turbulence scaling functions for the unstable atmospheric boundary layer, was developed. The new model is physically based and is tested against observations from the three sites. It shows better performance than the Normal Turbulence Model and hence is recommended to replace the Normal Turbulence Model. For model application, only two parameters are required, which are defined herein to represent offshore sites with high, medium and low turbulence intensities. Copyright © 2013 John Wiley & Sons, Ltd.

Use of multi-parameter distributions for offshore wind speed modeling: The Johnson SB distribution

Weibull distribution is widely suggested for modeling the behavior of offshore wind speeds. However, it has been often proved to be inadequate, while its indiscriminate use is not justified. Thus, in order to minimize estimation errors in offshore wind energy, it is necessary to select the most appropriate distribution for the wind climate description of a specific area. In this context, the performance of several probability distributions for offshore wind speed modeling, using long-term time series from 11 buoys in Eastern Mediterranean Sea (Greek waters) and 8 buoys in Western Mediterranean Sea (Spanish coastal waters) will be assessed for the first time here. We focus on the efficiency of three bounded multi-parameter distributions: Wakeby and Kappa, that have performed very well in completely different areas of the US coasts, Atlantic and Pacific Oceans, and Johnson SB distribution, which is introduced here for the first time. It is shown that Johnson SB, Kappa and Wakeby distributions accurately describe the empirical distribution of offshore wind speed; they have better adaptability than the 3-parameter Weibull distribution and qualify as reliable and prominent candidates for the assessment of offshore wind speed in any sea area. Moreover, Johnson SB is the only distribution that suits very well for all examined cases, providing consistently fair fits with respect to all goodness-of-fit tests applied. Alternative criteria, such as the performance of the examined probability models in terms of wind power density and average wind turbine power, have also been used for evaluating the fitted wind speed distributions. In this case the results proved to be different.

HeMOSU-1호 관측 자료를 이용한 해상풍속 산정오차 분석

본 연구에서는 해상풍력발전 후보지인 영광해상에 설치한 해상 기상타워 해모수 1호(HeMOSU-1)의 2011년 연간 풍속 관측 자료와 기상타워 해모수 1호 설치 지점에 인접한 부안, 고창, 영광 3개 지점의 육상 풍속자료를 이용하여 해상 임의고도에서의 풍속 산정 과정에서 발생하는 오차에 대한 분석을 수행하였다. 먼저 육상 풍속자료와 해상 풍속자료의 선형회귀분석으로 유도된 관계식을 이용하여 해상 기준고도(평균해수면 98.69 m)의 해상풍속자료를 추정하였다. 그리고, 추정된 해상풍속 자료는 관측자료를 통해 산출된 고도분포지수 값(<TEX>${\simeq}0.115$</TEX>)과 멱법칙 풍속프로파일을 이용하여 87.65 m 높이로 고도보정하여 관측치와 비교하였다. 연구 수행결과, 공간보정오차는 1.6~2.2 m/s 정도이며, 고도보정오차는 0.1 m/s 정도로 공간보정오차의 약 5% 정도에 불과한 것으로 파악되었다. 육상자료를 환산하여 해상임의지점의 풍속을 추정하는 경우, 큰 오차가 발생하기 때문에 장기간의 해상자료를 확보하거나 정확도가 높은 모델링 자료를 이용하여야 할 것으로 판단된다. In this paper, error analyses on the calculation of offshore wind speed have been conducted using HeMOSU-1 data to develop offshore wind energy in Yeonggwang sea of Korea and onshore observed wind data in Buan, Gochang and Yeonggwang for 2011. Offshore wind speed data at 98.69 m height above M.S.L is estimated using relational expression induced by linear regression analysis between onshore and offshore wind data. In addition, estimated offshore wind speed data is set at 87.65 m above M.S.L using power law wind profile model with power law exponent(0.115) and its results are compared with the observed data. As a result, the spatial adjustment error are 1.6~2.2 m/s and the altitude adjustment error is approximately 0.1 m/s. This study shows that the altitude adjustment error is about 5% of the spatial adjustment error. Thus, long term observed data are needed when offshore wind speed was estimated by onshore wind speed data. because the conversion of onshore wind data lead to large error.

Offshore wind speed and wind power characteristics for ten locations in Aegean and Ionian Seas

This paper utilizes wind speed data measured at 3 and 10 m above water surface level using buoys at 10 stations in Ionian and Aegean Seas to understand the behaviour of wind and thereafter energy yield at these stations using 5 MW rated power offshore wind turbine. With wind power densities of 971 and 693 W/m 2 at 50 m above water surface level, Mykonos and Lesvos were found to be superb and outstanding windy sites with wind class of 7 and 6, respectively. Other locations like Athos, Santorini and Skyros with wind power density of more than 530 W/m 2 and wind class of 5 were found to be the excellent sites. Around 15–16% higher winds were observed at 10 m compared to that at 3 m. Lower values of wind speed were found during summer months and higher during winter time in most of the cases reported in the present work. Slightly decreasing (∼2% per year) linear trends were observed in annual mean wind speed at Lesvos and Santorini. These trends need to be verified with more data from buoys or from nearby onshore meteorological stations. At Athos and Mykonos, increasing linear trends were estimated. At all the stations the chosen wind turbine could produce energy for more than 70% of the time. The wind speed distribution was found to be well represented by Weibull parameters obtained using Maximum likelihood method compared to WAsP and Method of Moments.

Accuracy and Characteristics of Offshore Wind Speeds Simulated by WRF

This paper discusses the accuracy and characteristics of offshore wind speeds simulated by the Weather Research and Forecasting (WRF) model. Firstly, the accuracy of the simulated wind speed is examined using in-situ measurements from the Shirahama offshore research platform for the whole year of 2005. It is found that the WRF surface wind speed has an annual bias of 15.3%, which is much larger than the National Centers for Environmental Prediction Final Analysis data (NCEP FNL) used as input. Secondly, the possibility of accuracy improvement of the simulated wind speed is discussed in terms of the decomposition of RMSE and the estimation error of wind energy density. As a result, it is shown that reducing the large positive bias is a key to increase the accuracy of the WRF wind speed. Without the large bias, the error in the estimation of annual mean wind energy density drastically decreases from 47% to 4%. Finally, the accuracy of wind speeds above the surface layer is examined using wind profiler measurements from Mihama. It is found that the WRF wind speed has large positive biases not only near the sea surface but also in the lower PBL. Thus, the large positive biases are speculated to be mainly due to inaccuracy of the PBL scheme in WRF.

Probability distributions for offshore wind speeds

In planning offshore wind farms, short-term wind speeds play a central role in estimating various engineering parameters, such as power output, extreme wind load, and fatigue load. Lacking wind speed time series of sufficient length, the probability distribution of wind speed serves as the primary substitute for data when estimating design parameters. It is common practice to model short-term wind speeds with the Weibull distribution. Using 10-min wind speed time series at 178 ocean buoy stations ranging from 1 month to 20 years in duration, we show that the widely-accepted Weibull distribution provides a poor fit to the distribution of wind speeds when compared with more complicated models. We compare distributions in terms of three different metrics: probability plot R 2, estimates of average turbine power output, and estimates of extreme wind speed. While the Weibull model generally gives larger R 2 than any other 2-parameter distribution, the bimodal Weibull, Kappa, and Wakeby models all show R 2 values significantly closer to 1 than the other distributions considered (including the Weibull), with the bimodal Weibull giving the best fits. The Kappa and Wakeby distributions fit the upper tail (higher wind speeds) of a sample better than the bimodal Weibull, but may drastically over-estimate the frequency of lower wind speeds. Because the average turbine power is controlled by high wind speeds, the Kappa and Wakeby estimate average turbine power output very well, with the Kappa giving the least bias and mean square error out of all the distributions. The 2-parameter Lognormal distribution performs best for estimating extreme wind speeds, but still gives estimates with significant error. The fact that different distributions excel under different applications motivates further research on model selection based upon the engineering parameter of interest.

The influence of humidity fluxes on offshore wind speed profiles

Abstract. Wind energy developments offshore focus on larger turbines to keep the relative cost of the foundation per MW of installed capacity low. Hence typical wind turbine hub-heights are extending to 100 m and potentially beyond. However, measurements to these heights are not usually available, requiring extrapolation from lower measurements. With humid conditions and low mechanical turbulence offshore, deviations from the traditional logarithmic wind speed profile become significant and stability corrections are required. This research focuses on quantifying the effect of humidity fluxes on stability corrected wind speed profiles. The effect on wind speed profiles is found to be important in stable conditions where including humidity fluxes forces conditions towards neutral. Our results show that excluding humidity fluxes leads to average predicted wind speeds at 150 m from 10 m which are up to 4% higher than if humidity fluxes are included, and the results are not very sensitive to the method selected to estimate humidity fluxes.

Summer diatom and dinoflagellate blooms in Lisbon Bay from 2002 to 2005: Pre-conditions inferred from wind and satellite data

Sea surface temperature (SST) and chlorophyll ( Chl) maps derived from satellite data, together with wind and phytoplankton cell concentration time-series, are used to characterize the wind forcing and the spatio-temporal SST/ Chl variability for periods preceding the summer (June–September) diatom and dinoflagellate blooms. The blooms (concentrations of cells higher than 200 × 10 3 cells l - 1 ) were identified in the weekly samples collected at the long-term phytoplankton study site in Lisbon Bay, from 2002 to 2005. It is shown that bloom detection at the site occurs either during upwelling events, i.e. when offshore northerly wind speed is greater than 5 m s - 1 for more than 5 days, or up to 4 days after wind relaxation. However, no direct relationship was found between the blooms and either the intensity or duration of the wind pulses. The satellite image sequences show that the Chl patterns in Lisbon Bay are strongly linked to the orientation and branching of the upwelling filament rooted at Cape Roca and the westward progression of the warm-core cyclones shed from the slope off Setúbal Bay. High Chl values are typically maintained throughout the upwelling cycle along the inshore boundary of the Roca filament, covering the whole bay during the intense wind phase. During the relaxation phase, there is a Chl increase concurrent with the contraction of the high Chl area to the northern part of the bay, associated with an inshore poleward current. It is proposed that the earlier setup of the offshore stratification conditions and the shedding of a slope eddy favored the bloom of dinoflagellates in July 2004, while the stable position of the main circulation features in July 2002 favored a diatom bloom. The results add further evidence that, in summer, the ocean circulation in the embayments south Cape Roca is dominated by the upwelling dynamics and plays a key role in the development of phytoplankton blooms off central Portugal.

High‐frequency P‐wave seismic noise driven by ocean winds

Earth's background vibrations at frequencies below about 0.5 Hz have been attributed to ocean‐wave energy coupling into the ground and propagating as surface waves and P‐waves (compressional waves deep within the Earth). However, the origin and nature of seismic noise on land at frequencies around 1 Hz has not yet been well studied. Using array beamforming, we analyze the seismic noise fields at two remote sites (Parkfield and the Mojave Desert) in California, for durations of one and six months respectively. We find that (1) the seismic background noise at about 0.6–2 Hz consists of a significant amount of continuous P‐waves originating offshore, and (2) the power of the P‐wave noise is highly correlated with the offshore wind speed, demonstrating that these high‐frequency P‐waves are excited by distant ocean winds. Our result suggests a land‐based seismological proxy for monitoring oceanic weather.

Forecasting offshore wind speeds above the North Sea

AbstractWe investigate the expected performance of short‐term wind power prediction systems for offshore sites in the German Bight and at Horns Rev. Despite the special meteorological situation over the North Sea, it is found that the accuracy of wind speed predictions provided by the numerical prediction model of the German weather service is comparable to that of onshore predictions. However, although relative forecast errors look promising, the absolute errors are fairly large, with a root mean square error up to 3 m s−1 for the 48 h forecast. Moreover, vertical wind profiles which are typically needed to calculate the wind speed at hub height are considered at Horns Rev. In all thermal conditions the measured profiles show significant deviations from the expected shapes. The reason for this has to be clarified. Assuming that the deviations are due to the physical processes in the marine boundary layer, we present an alternative approach to derive wind profiles over the ocean which involves the inertial coupling of the Ekman layers of atmosphere and sea via a wave boundary layer with constant shear stress. Profiles calculated by this method are compared with measured profiles, showing rather good agreement. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions? Part II: Quantifying Uncertainties Associated with Distribution Fitting Methods

Abstract Remote sensing tools represent an attractive proposition for measuring wind speeds over the oceans because, in principle, they also offer a mechanism for determining the spatial variability of flow. Presented here is the continuation of research focused on the uncertainties and biases currently present in these data and quantification of the number of independent observations (scenes) required to characterize various parameters of the probability distribution of wind speeds. Theoretical and empirical estimates are derived of the critical number of independent observations (wind speeds derived from analysis of remotely sensed scenes) required to obtain probability distribution parameters with an uncertainty of ±10% and a confidence level of 90% under the assumption of independent samples, and it is found that approximately 250 independent observations are required to fit the Weibull distribution parameters. Also presented is an evaluation of Weibull fitting methods and determination of the fitting method based on the first and third moments to exhibit the “best” performance for pure Weibull distributions. Further examined is the ability to generalize parameter uncertainty bounds presented previously by Barthelmie and Pryor for distribution parameter estimates from sparse datasets; these were found to be robust and hence generally applicable to remotely sensed wind speed data series.

Cartographie des vents dans le golfe du Saint-Laurent à l'aide de l'imagerie RADARSAT-1

The development of wind power, both in Canada and around the world, often requires an assessment of the wind power potential in coastal regions, such as in the Gulf of St. Lawrence. Synthetic aperture radar (SAR) is becoming a more common tool for offshore wind mapping and is complementary to ground measurements and meteorological models. To test the applicability of RADARSAT-1 SAR scenes for this purpose, we use a hybrid model using the semi-empirical CMOD-IFR2 model and the polarization ratio to link wind speed to radar backscatter in dB, radar geometry, and wind direction. Wind direction is interpolated from ground wind direction measurements and co-located QuickSCAT data. This methodology gives accurate results when used with the Kirchhoff polarization ratio and ScanSAR Narrow (SNA) images. The complex geography and topography of the Gulf of St. Lawrence region do not seem to interfere with this process, as offshore wind speed accuracy is good, with a root mean square error (RMSE) of 1.30 m/s in comparison with QuickSCAT wind speeds. Closer to the coast as well, the precision of this technique in comparison with coastal ground measurements seems to be sufficiently good (RMSE of 2.12 m/s) to allow an estimate of the wind power potential in areas not covered by ground-based techniques.

Identifying the Effect of Tidal Height on Offshore Wind Speed Profiles

AbstractThe rise and fall of the sea surface due to the tide effectively moves an offshore wind turbine hub through the wind shear profile. Offshore wind farms are being built around the coasts of Europe, including in the Baltic and the North Sea. Tidal ranges in the North Sea are greater than those in the Baltic, and the potential effect on the wind shear profile of the change in sea surface height is likely to be more significant. This article seeks to identify the effect of tidal height on the shear profile at a mast off the east coast of the UK where the maximum tidal range is 7 m. Definite evidence for the effect of tidal height on wind shear is presented, though the effect is small and there is considerable scatter in the data. Copyright © 2003 John Wiley &amp; Sons, Ltd.

Economics of off-shore/on-shore wind energy systems in Qatar

This work presents an assessment of the potential and economical feasibility of adopting off-shore/on-shore wind energy as a renewable source of energy in Qatar. An analysis is presented for the long term measured on-shore wind speed (1976–2000) at Doha International Airport. A similar analysis is presented for the measured off-shore wind speed at the Qatari Haloul Island. For the on-shore measurements, the average annual wind speed (at 20 m height) was found to be about 5.1 m/s. On the other hand, for the off-shore measurements at Haloul, the average annual wind speed was found to be about 6.0 m/s. This result indicates the suitability of utilizing small to medium-size wind turbine generators, efficiently. Such generators can be implemented for water pumping and to produce sufficient electricity to meet vital, limited needs of remote locations, such as isolated farms, which do not have access to the national electricity grid. An economical assessment is presented which takes into consideration the interest recovery factor, the lifetime of the wind energy conversion system (WECS), the investment rate and operation and maintenance costs. The results indicate that the cost of electricity generation from the wind in Qatar compares favorably to that from fossil fuel resources. The feasibility of utilizing off-shore wind turbine systems to meet the power requirements of the island of Haloul and possibly provide additional power for nearby on-shore areas is discussed.

Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions?

Wind speeds over the oceans are required for a range of applications but are difficult to obtain through in situ methods. Hence, remote sensing tools, which also offer the possibility of describing spatial variability, represent an attractive proposition. However, the uncertainties inherent in application of current remote sensing methodologies have yet to be fully quantified. Aside from known issues regarding absolute accuracy and precision, there are a number of biases inherent in remote retrieval of wind speeds using satellite-borne instrumentation that lead to overestimation of the wind resource and are demonstrated here to be of sufficient magnitude to merit further consideration. As an interim measure, error bounds are proposed for the wind speed probability distribution parameters, which may be applied to sparse datasets such as those likely to be obtained from satelliteborne instrumentation.

The Statistical Prediction Of Offshore Winds From Land-Based Data For Wind-Energy Applications

Land-based meteorological measurements at two locations on the Danish coast are used to predict offshore wind speeds. Offshore wind-speed data are used only for developing the statistical prediction algorithms and forverification. As a first step, the two datasets were separated into ninepercentile-based bins, with a minimum of 30 data records in each bin. Next, the records were randomly selected with approximately 70% of the data in each bin being used as a training set for development of the prediction algorithms, and the remaining 30% being reserved as a test set for evaluation purposes. The binning procedure ensured that both training and test sets fairly represented the overall data distribution.To base the conclusions on firmer ground, five permutations of these training and test sets were created. Thus, all calculations were based on five cases, each one representing a different random selection from the same data, but maintaining the (approximate) 70-30 split in each bin. This procedure served to ensure that conclusions were not based on a single randomly-selected case. Two statistical methods are employed:multiple linear regression (MLR), and Classification and Regression Trees(CART). MLR produces excellent results using only land-based predictors.The CART results are similar to those from MLR, and tend to be slightly better.