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ERS-2 SAR-derived wind map (100 km x 100 km) showing the wake phenomenon downstream of Horns Rev offshore wind farm. 25 February 2003.
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Alternative data sources include scatterometry (Monaldo et al. 2004), or atmospheric model predictions (Monaldo et al. 2001). Figure 1 shows a wind speed map retrieved from an Envisat ASAR image acquired in wide swath mode (WSM) over Denmark on 13 October 2004. The image conveniently covers the majority of Danish Seas. A software tool developed at...
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... of sharing maintenance costs and grid connections are counteracted by potential power loss and fatigue loads caused by turbulence, as wind farms are clustered. Figure 4 illustrates the wake phenomenon downstream of the wind farm at Horns Rev for a scene acquired by ERS-2 on 25 February 2003. To quantify the wake effect, we have analyzed a series of airborne and spaceborne SAR images. ...
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... Met-Towers and masts) which is developed due to wind stream collision with structures. Within the region, flow distortion, wind speed decrement and turbulence level increment are evident [9,10]. For the aforesaid reasons, wind signals recorded in the wake regions of Met-Towers are generally ignored in practical purposes. ...
Understanding dynamic nature of atmospheric turbulences is an important issue in different topics of environmental researches. The first step of this understanding is subjected to collect wind data in an adequate way and later analyzing them with suitable mathematical methods. Nowadays due to significant limitations of employing traditional wind sensors, utilizing remote sensing equipment like Sodar is widespread in practical wind data collection. During Samar storm occurrence in September 2015 in south of Iran, simultaneous operation of a Sodar device and wind sensors mounted on a Met-Tower provided a unique situation to investigate dynamic nature of the turbulent flow induced by the storm. However, data provided by Met-Tower’s sensors suffered from less accuracy due to wake effect caused by tower’s structure. To probe the essence of turbulence, one conventional way is to calculate power spectral densities of observed wind signals. Although this calculation provides some fundamental information about turbulence structure of the flow, revealing hidden physical aspects of the phenomenon needs more innovative methods. One of partly new method in the field is to implement discrete wavelet analysis on observed wind signals which sheds light on latent properties of the flow. In this study turbulence essence of Samar storm is investigated through calculating Welch power spectral density and implementing discrete wavelet analysis on wind signal observed by Sodar. The outcomes of current research bring out hidden features of turbulent flow from shadow to light. Furthermore superiority of Sodar for investigating atmospheric flows is emphasized.
... Execution of these satellite based images in order to derive wind speed in the early stages of wind farm planning is becoming praiseworthy because of its spatial and time coverage along with cost effectiveness [1]. This useful method can predict perfect area of higher wind resources which leads to the planning of feasibility studies and provide a faithful supplement to the traditional assessments [3]. ...
... SAR is a remote sensing technique which transmits microwave pulses of energy to illuminate an area of the Earth surface. The pulses gets scattered because of the surface properties and a portion returns to the instrument with a change in amplitude, polarization, travel time and phase [3]. All these backscattered signals for each foot print area stores in the satellite to process a two dimensional image of the surface. ...
... All these backscattered signals for each foot print area stores in the satellite to process a two dimensional image of the surface. SAR has higher spatial resolution (around 1km) and preferred more for coastal wind energy measurements [5].The data acquisition from the SAR pulses can be performed in day or night even if it is cloudy or light rainy (Christiansen, 2006). SAR signals are electromagnetic waves with a polarization determined by the orientation of the electric field [3]. ...
... There are several options that can be used for pre-assessment wind data but the most common ones are based on remote sensing measurement instruments. In the last two decades, satellite wind measurement technologies are developed with Synaptic Aperture Radar (SAR) [24] and Scatterometer (SCAT) [25] techniques. The data is mostly open source but difficult to process and of low quality due to high uncertainty (±2 m/s). ...
Although, offshore wind energy development emerged under way at the beginning of the millennium, Europe is planning to bring offshore wind energy capacity to over 11.6 GW until 2020. This is nearly 10 times todays installed offshore capacity and equal to nearly 50% of the new planned investment in the wind energy market. The North Sea and the Baltic Sea are the main investment areas due to the shallower sea depth. In this paper an approach to use old gas/oil platforms as the foundation for a wind turbine is examined. An offshore gas platform close to Istanbul Turkey with over 20 years more lifetime is taken as a real-life case, with the wind resource information extracted from the recent large-scale wind atlas study, Global Wind Atlas version 2. The study aims to combine recent offshore economical models with up-to-date scientific wind energy yield assessment models to have a more realistic look on the feasibility of such an approach. The results show that, with the assumption of no extra support structure and capital loan costs, a project can be feasible with bigger then 8MW wind turbines. These may involve a large initial investment but the return of the investment (ROI) can be as low as 8 years. With bigger turbines, profit can be increased, and ROI can be decreased while the Levelized Cost of Energy (LCOE) displays minor decrease after 10 MW.
... The second modification to the wind speed time series is the magnitude adjustment necessary to compensate for the wind deficits caused by turbine wakes. As discussed in Section 2, downstream turbines will experience a lower mean wind speed than their upstream counterparts [see Christiansen, 2006, for a description of the magnitude adjustments for Horns Rev]. Together with the temporal shift, these are the modifications considered for generating a time series of wind speeds for every turbine within the wind farm. ...
Wind power is becoming an increasingly important technology for electric power generation in many power systems around the world. A typical wind farm consists of a relatively large number of turbines under some form of decentralized control. In this context, a cyber-attack to wind power infrastructure may not only cause immediate physical damage to the facility in question, but may also threaten the stability of the power system in which this facility operates. In this paper, we present a novel distributed algorithm aimed at detecting cyber-attacks in a wind farm. The cyber-attacks considered take the form of adversarial manipulation of different turbine-specific control logic parameters. The proposed algorithm relies on the operational dynamics specific to wind farms namely that turbines that are relatively close are subject to similar wind patterns and hence must be in similar control “states” most of the time. This can be seen as a “wisdom of the crowd” effect: the “true” state is the state of the majority of turbines in physical proximity when only a few have been compromised. In the proposed algorithm, each turbine in the farm periodically shares information regarding their own control “state” with neighboring turbines through a dedicated wireless channel. We test the performance of the proposed algorithm in a simulation test-bed based on Denmark's Horns Rev wind farm. The results indicate that iterative reputation or trust re-allocation enables a distributed identification of affected turbines under a wide array of cyber-attack scenarios.
... Figure 23 shows an example for RadarSAT -2 . The image resolution (NRCS pixel size) is on the order of 5 -50 m, but noise at this level produces "speckle" in the image which must be normalized by area averaging to reduce random noise (Christiansen 2006). The resolution of wind retrievals from SAR is therefore not quite as good, but can be subkilometer at any swath width. ...
Efficient development of offshore wind power will require accurate information about the wind field on a wide range of spatial and temporal scales. Lidar and Satellite Microwave Radar/Radiometry have been evaluated and used for wind speed measurement. Numerous studies have been published that examine the error, bias, and performance characteristics of variants of both technologies under a range of conditions. This paper reviews recent research and technological advances and outlines strategies for applying the technologies to reduce costs, increase energy production and improve energy forecasting through advanced rotor controls and more accurate resource estimation and mapping.
A literature search was conducted to identify the most recent and relevant correlation and validation studies of Lidar, synthetic aperture radar, scatterometers, and radiometers used for estimating wind speed. Database queries were conducted to estimate inventory for satellite wind data. Estimates of the accuracy (bias and uncertainty) and availability (sample density) of these technologies were developed based on the literature search and database queries. Both “snapshot” wind speed and energy density estimates were compared for satellite microwave systems and Lidar technologies. Offshore, where turbulence is lower, Lidar is found to have very high accuracy and availability, comparable to cup anemometers at a range of up to 200m on fixed platforms. Floating Lidar is rapidly approaching the same level of accuracy and availability, and is easily re-positioned. However, the short time series of Lidar is less useful for long term indexing, and it is limited to a single site per sensor. Satellite microwave wind retrievals are available over a 20 year period and are found to have good time-averaged accuracy at 10 meters above sea level for wind speeds between 3 and 15 m/s, but are subject to minor bias (below +/- 0.2 m/s) from the use of inaccurate shear profiles, from diurnal effects, and from local metocean conditions. Three strategies for use of these technologies are outlined and evaluated.
• Siting and Resource Assessment - By processing all available satellite microwave data sets, calibrated with data from a one year field campaign using floating Lidar systems, cross-correlated through a parametric geophysical model function, bias and error of wind speeds generated from the satellite data can be reduced, and wind mapping can be significantly improved in resolution and accuracy.
• Energy Production Estimates- By using wind profile data from floating Lidars, deployed on site, and indexed to a 20 year time series from calibrated satellite wind data, Annual Energy Production estimates can be greatly improved by reducing uncertainty (and thus, the risk premium on financing). In the near future, this methodology can obviate the need for a met tower for resource assessment.
• Rotor Control - By using nacelle or hub mounted Lidar to look upstream, new Lidar-assisted control systems can adjust blade pitch and nacelle yaw pro-actively to match rapid changes in wind speed or direction. This can reduce fatigue and extreme gust loading on components, allowing longer blades and greater swept area. It can also improve efficiency be reducing yaw mis-alignment.
In addition to power production benefits, rough, first-order costs were developed to check economic justification, and the expected change in Breakeven Price was calculat-ed for two different build-out scenarios of the study area. The analysis indicates that the recommended strategies for improving Rotor Control and reducing uncertainty of AEP estimates can reduce the Breakeven Price of power for the base case wind farm by at least 4%.
The benefits of improved mapping are more difficult to monetize due to high levels of uncertainty in all the primary factors, so two different scenarios are considered. If the benefits of improved mapping and better siting (2% to 3% lower BP) are available to the first ten or twelve projects, and the mapping effort is federally funded, or the costs are somehow distributed industry-wide over full build-out of the study area, the Break-even Price for the first phase of wind farms could be reduced by a total of around 6% to 7%. If mapping benefits are assumed to diminish over time as the study area builds out, the long term, annualized reduction in Breakeven Price over the entire study area will be lower, at around 4% to 5%. In either case the mapping effort is justified, and the cost can be reduced by about $120 million using Lidar equipped met buoys.
Keywords: offshore wind power, wind resource assessment, satellite radar, Lidar, floating Lidar, AEP estimates, wind turbine pitch control, wind turbine yaw control, wind energy mapping
... Measurements of ocean wind vectors serve as a basis for marine weather forecasting and offshore wind farms planning and contribute to the understanding of air-sea interactions and atmospheric dynamics [1][2][3]. Conventional wind observations from ships, buoys and meteorological stations cannot characterize the detailed distribution of offshore wind vectors. Representative long-term offshore meteorological time series with high spatial and temporal resolution are often not available. ...
We present and validate a method of reconstructing high-resolution sea surface wind fields from multi-sensor satellite data over the Grand Banks of Newfoundland off Atlantic Canada. Six-hourly ocean wind fields from blended products (including multi-satellite measurements) with 0.25° spatial resolution and 226 RADARSAT-2 synthetic aperture radar (SAR) wind fields with 1-km spatial resolution have been used to reconstruct new six-hourly wind fields with a resolution of 10 km for the period from August 2008 to December 2010, except July 2009 to November 2009. The reconstruction process is based on the heapsort bucket method with topdown search and the modified Gauss–Markov theorem. The result shows that the mean difference between the reconstructed wind speed and buoy-estimated wind speed is smaller than 0.6 m/s, and the standard deviation is smaller than 2.5 m/s. The mean difference in wind direction between reconstructed and buoy estimates is 3.7°; the standard deviation is 40.2°. There is fair agreement between the reconstructed wind vectors and buoy-estimated ones.
... The wind speed can be acquired from the Synthetic Aperture Radar (SAR) (Vachon et al. 1996;Scoon et al. 1996;Korsbakken et al. 1997). Fig 3-2 shows the wind speed map of Danish seas (Denmark), which is retrieved from SAR image with spatial resolution of 150×1 50 m 2 on 22 October 2004 (Christiansen 2006). In Fig 3-2, the arrows represent the wind speed direction. ...
The location and capacity size of the renewable energy facility and the influencing factors of renewable energy consumption play an important role in planning renewable energy. Therefore, the solutions to determining the optimal locations and optimal capacity sizes of renewable energy facilities in renewable energy supply chain and identifying the spatial influencing factors of renewable energy consumption are essential in making scientific renewable energy planning strategy with optimal cost. The main focus of the study is to develop two renewable energy models to provide such solutions. The study also combines remotely-sensed data into the models. The study is composed of two main stages: the development of an optimization supply model integrating spatial information from remote sensing and a consumption model also integrating spatial information. Both are needed for optimizing the energy market but each of them has first to be developed individually.
In the first stage, the study developed an optimization supply model with remotely-sensed data to determine the optimal locations and optimal capacity sizes of renewable energy facilities in renewable energy supply chain. In order to develop this model, the first step was to restrict the studied renewable energy supply chain to electricity supply chain of solar and wind energy and then to simplify the supply chain. In the second step, the solar and wind energy potentials were evaluated with remote sensing data. A grid structure was established to integrate remote sensing data and remote sensing-based constraints. The spatial distributions of supply and demand were modeled taking into account remote sensing data. The by-products and pre-determined capacity sizes for solar and wind energy facilities were also developed and can be taken into account if needed. Then the equilibrium theory with fixed demand was used to formulate the objective function of the model such that the model will not be used to optimize the profit of one single plant, but rather to maximize the social welfare of the demands. The model was called Optimization Facility Location and capacity size model for solar energy facilities and wind energy facilities in electricity supply chain system integrating Remote sensing data (OFLR model). It was a mixed integer program. As renewable energy planning is important, the model is a valuable tool for decision makers in order to make spatially explicit location planning strategies for solar and/or wind energy.
In the second stage, the study aimed to develop a statistical model to identify the spatial influencing factors of renewable energy consumption. Initially, the considered influencing factor of renewable energy consumption was primarily the locations of consumptions and therefore the spatial trend model was used to identify location factor. The spatial trend model did not meet the requirements and an Improved Spatial Trend model (IST model) was developed to further identify location factor and non-location spatial influencing factors of renewable energy consumption and solve the ill-conditioned least squares problem occurred in spatial trend model. Finally, a spatial statistical model selection procedure was provided to select the best IST model for renewable energy consumption, regarding the spatial autocorrelation among residuals.
The OFLR model was developed with GAMS programming language and the IST model was developed with Matlab programming language. These two models were illustrated by case studies.
... The horizontal wind speed gradient may also be determined from remotely sensed data, such as Synthetic Aperture Radar (SAR) (see description in (Christiansen & Hasager 2006, Hasager et al. 2006, Nielsen et al. 2004. Figure 3 shows the average transect composited for the 16 initial cases using the same weighting as for KAMM2 and WAsP Engineering. ...
Simulations, from mesoscale numerical models, and analyses of in-situ and remote sensing data from offshore wind farms in Denmark, are used to examine both horizontal and vertical gradients of wind speeds in the coastal zone. Results suggest that the distance from the coastline over which wind speed vertical profiles are not at equitibrium with the sea surface (which defines the coastal zone) extends to 20 km and possibly 70 km from the coast. Using this operational definition of the coastal zone, these results thus imply the typical-width of the coastal zone in northern Europe is between 20 and 70 km. The width of the coastal zone, and the winds vertical (shear) and horizontal gradients within the coastal zone, depend on atmospheric stability. Although vertical wind speed profiles above 50 m are likely responding to additional factors such as the height of the boundary-layer, using a stability correction improves predictions of wind speed compared with the logarithmic profile. Modelling indicates that within the coastal zone, wind speeds at typical turbine hub-heights can change by 2 m/s over the horizontal extent of a large wind farm, depending on stability. However, if the fetch is sufficiently long (or the windfarm is further from the coast) both horizontal and vertical wind speed gradients over the area of the wind farm appear to be small and negligible.
... There are several steps needed to be taken when planning for a wind farm, but the most significant component of wind power applications is understanding and evaluating the wind resource of the region (Christiansen, 2006, AWEA, 2009. Wind resource assessment is the process by which the potential of wind power at a site/region is studied and determined. ...
The analysis of OWF’s influence on the atmosphere and ocean is separated into three parts. Two parts study the effect of offshore wind farms on the atmosphere (this chapter) and on the ocean (Chap. 5) in theory by simulation type TOS-01 based on an idealized model area—the ocean box. Part 3 (Chap. 6) gives insight into the future of the German Bight regarding plans of wind farm development in 2030.
