Services Provided:
* Guidance for an optimal wind measurement campaign.
* Met tower and remote sensing data processing, archiving, quality assurance (QA) and analysis.
* Long-term wind speed estimations based on met tower and/or remote sensing measurements.
* Wind prospecting, wind turbine siting and project lay-out. We use DeLorme XMap GIS Editor software.
* Terrain modeling to estimate the wind speed at each turbine site with patented RAMWind Terrain model.
* Turbine wake array loss calculations using validated, proprietary RAMWake model.
* Wind farm micrositing and optimization.
* Wind farm energy output (including 8760 and 12 x 24 calculations).
* Uncertainty analysis of energy estimations.
* Turbine performance analysis and modeling with RAMWind.
* Wind farm performance analysis including actual wake loss vs. modeled wake loss and model validation.
* Wind speed mapping using RAMWind model.
* Turbine power mapping using RAMWind model.
* Project due diligence review.
* Expert testimony.
* Guidance for an optimal wind measurement campaign.
* Met tower and remote sensing data processing, archiving, quality assurance (QA) and analysis.
* Long-term wind speed estimations based on met tower and/or remote sensing measurements.
* Wind prospecting, wind turbine siting and project lay-out. We use DeLorme XMap GIS Editor software.
* Terrain modeling to estimate the wind speed at each turbine site with patented RAMWind Terrain model.
* Turbine wake array loss calculations using validated, proprietary RAMWake model.
* Wind farm micrositing and optimization.
* Wind farm energy output (including 8760 and 12 x 24 calculations).
* Uncertainty analysis of energy estimations.
* Turbine performance analysis and modeling with RAMWind.
* Wind farm performance analysis including actual wake loss vs. modeled wake loss and model validation.
* Wind speed mapping using RAMWind model.
* Turbine power mapping using RAMWind model.
* Project due diligence review.
* Expert testimony.
RAMWind Fact Sheet:
In direct comparisons of modeling results between RAMWind, WAsP, MS-Micro3, WindSim, Meteodyn and NWP, RAMWind proved to be more accurate in modeling the wind speed at the “missing” met tower site than the other models at five of the six project sites used in the comparisons. [1], [2].
RAMWind is the only model that has been able to resolve terrain effects on wind flow with sufficient accuracy to be used in wake model validations presented at recent AWEA events. [3], [4].
Wind speed mapping with RAMWind at a project under development in the Great Plains identified additional windy land that allowed the expansion of the project from 200 MW to almost 400 MW. Mapping the same area with WAsP had underestimated the wind speeds on the same windy land.
At a project site with four met towers, a project-wide wind map prepared with RAMWind, based on the long-term hub height wind speeds at four met towers, modeled the average hub height wind speed at two new met sites to within 0.02 m/s in advance of their installation.
RAMWind has recently been adapted to model turbine performance of un-waked turbines at operating wind farms. This analysis has been used to identify turbines that perform better than others dues to performance enhancement technology and to identify under-performing turbines. No other known model is capable of performing this kind of analysis.
[1] VanLuvanee D., et al: Comparison of WAsP, MS-Micro3, CFD, NWP, and Analytical Methods for Estimating Site-Wide Wind Speeds, AWEA Wind Resource Workshop, September 2009.
[2] Vanden Bosche J: Wind Flow Modeling Software Comparison, AWEA Wind Resource Workshop, September 2009.
[3] Wolfe, Justin, et al: Deep Array Wake Loss in Large Onshore Wind Farms (A Model Validation), AWEA Wind Resource Workshop, Oklahoma City, OK, September 2010.
[4] Kline, J and Z Kline, Wind Flow Model Verification and Wake Model Test at an Operating Wind Farm, {Poster) WindPower 2014, Las Vegas, NV, May 2014.
(downloads of these presentations are available on the Publications page)
Discussion on the origin of the RAMWind terrain model.
Over vast expanses of the Great Plains, the Midwest and mountainous west of the United States, and in similar places the world over for that matter, there are not significant changes in surface roughness - i.e. ground cover - and wind speed variations are, to a large degree, driven by terrain effects - the effect that the changes in elevation over a given piece of ground, like a wind farm. Certainly, the diurnal changes in atmospheric stability affect the degree to which the terrain influences wind speed - more so under stable atmospheric conditions than under unstable conditions where buoyancy effects mask the terrain effects, but in the absence of significant differences in ground cover the terrain elevation variations are responsible for variations in wind speed over a wind farm site.
But how does terrain influence wind speed? Some aspects of terrain effects are quite clear - sites on ridges tend to be more windy and sites in low terrain and valleys tend to be less windy. But how to interpret the elevation variations is largely a mystery. At wind farm sites, as part of the development process, some number of meteorological (met) towers are erected at various locations to sample wind speed and direction, which are leveraged to make estimates of long-term wind speeds at those locations. Since no developer can afford to put a met tower at every turbine location, some means must be employed to estimate wind speeds at each location where a wind turbine is to be placed or to help determine the best locations to site turbines in order to maximize energy generation.
There are a number of modeling technologies that have been developed to provide estimates of wind speed in wind energy applications but in objective testing they can have significant errors. This includes linear models, such as WAsP and computational fluid dynamics (CFD) models such as WindSim and Meteodyn.
Not being satisfied with the status quo of wind speed modeling, and recognizing that terrain elevation variations were largely responsible for wind speed variations across a project site, RAM developed a modeling technique that describes the elevation variations surrounding each met tower (or wind turbine) site in terms of terrain exposure values, which are used with the wind speed data measured at the met tower sites to develop a relationship between wind speed and terrain exposure which can then be used to calculate wind speeds at the turbine sites. The model is basically self-calibrated to each project site's unique characteristics of terrain and stability, which together are responsible for most of the variation in wind speed across a site. Since we don't know a priori how the wind speed should vary with respect to terrain variation, we let the data tell us that in the context of terrain exposure. [Note: terrain exposure is essentially an integration of the elevation differences between a given location - met tower or turbine site - with respect to the surrounding terrain.]
One of the revelations from working with RAMWind is that at most project sites, it is clear that the terrain downwind from the point in question has the dominant effect on wind speed. Typically, the met tower sites with high downwind exposure have higher wind speeds than sites with low or even negative downwind exposure. Having high upwind exposure can have a negative effect on wind speed. Naturally, some exceptions exist, but to a very high degree, these trends have been observed at project sites all over the US and Canada.
Another significant finding that resulted from the analysis of wind speed in the context of terrain exposure is that atmospheric stability has a powerful influence on the degree to which wind speed is affected by terrain elevation differences, as described by RAMWind exposure calculations. Under high stability the terrain (particularly downwind) exerts much greater influence on wind speed than under neutral or unstable atmospheric conditions.
[5] Kline, J and Walls, E: Terrain Effects on Wind Speed Enhanced by Atmospheric Stability, AWEA Wind Resource Seminar, Pittsburgh, PA, September 2012. (download available on Publications page)
In direct comparisons of modeling results between RAMWind, WAsP, MS-Micro3, WindSim, Meteodyn and NWP, RAMWind proved to be more accurate in modeling the wind speed at the “missing” met tower site than the other models at five of the six project sites used in the comparisons. [1], [2].
RAMWind is the only model that has been able to resolve terrain effects on wind flow with sufficient accuracy to be used in wake model validations presented at recent AWEA events. [3], [4].
Wind speed mapping with RAMWind at a project under development in the Great Plains identified additional windy land that allowed the expansion of the project from 200 MW to almost 400 MW. Mapping the same area with WAsP had underestimated the wind speeds on the same windy land.
At a project site with four met towers, a project-wide wind map prepared with RAMWind, based on the long-term hub height wind speeds at four met towers, modeled the average hub height wind speed at two new met sites to within 0.02 m/s in advance of their installation.
RAMWind has recently been adapted to model turbine performance of un-waked turbines at operating wind farms. This analysis has been used to identify turbines that perform better than others dues to performance enhancement technology and to identify under-performing turbines. No other known model is capable of performing this kind of analysis.
[1] VanLuvanee D., et al: Comparison of WAsP, MS-Micro3, CFD, NWP, and Analytical Methods for Estimating Site-Wide Wind Speeds, AWEA Wind Resource Workshop, September 2009.
[2] Vanden Bosche J: Wind Flow Modeling Software Comparison, AWEA Wind Resource Workshop, September 2009.
[3] Wolfe, Justin, et al: Deep Array Wake Loss in Large Onshore Wind Farms (A Model Validation), AWEA Wind Resource Workshop, Oklahoma City, OK, September 2010.
[4] Kline, J and Z Kline, Wind Flow Model Verification and Wake Model Test at an Operating Wind Farm, {Poster) WindPower 2014, Las Vegas, NV, May 2014.
(downloads of these presentations are available on the Publications page)
Discussion on the origin of the RAMWind terrain model.
Over vast expanses of the Great Plains, the Midwest and mountainous west of the United States, and in similar places the world over for that matter, there are not significant changes in surface roughness - i.e. ground cover - and wind speed variations are, to a large degree, driven by terrain effects - the effect that the changes in elevation over a given piece of ground, like a wind farm. Certainly, the diurnal changes in atmospheric stability affect the degree to which the terrain influences wind speed - more so under stable atmospheric conditions than under unstable conditions where buoyancy effects mask the terrain effects, but in the absence of significant differences in ground cover the terrain elevation variations are responsible for variations in wind speed over a wind farm site.
But how does terrain influence wind speed? Some aspects of terrain effects are quite clear - sites on ridges tend to be more windy and sites in low terrain and valleys tend to be less windy. But how to interpret the elevation variations is largely a mystery. At wind farm sites, as part of the development process, some number of meteorological (met) towers are erected at various locations to sample wind speed and direction, which are leveraged to make estimates of long-term wind speeds at those locations. Since no developer can afford to put a met tower at every turbine location, some means must be employed to estimate wind speeds at each location where a wind turbine is to be placed or to help determine the best locations to site turbines in order to maximize energy generation.
There are a number of modeling technologies that have been developed to provide estimates of wind speed in wind energy applications but in objective testing they can have significant errors. This includes linear models, such as WAsP and computational fluid dynamics (CFD) models such as WindSim and Meteodyn.
Not being satisfied with the status quo of wind speed modeling, and recognizing that terrain elevation variations were largely responsible for wind speed variations across a project site, RAM developed a modeling technique that describes the elevation variations surrounding each met tower (or wind turbine) site in terms of terrain exposure values, which are used with the wind speed data measured at the met tower sites to develop a relationship between wind speed and terrain exposure which can then be used to calculate wind speeds at the turbine sites. The model is basically self-calibrated to each project site's unique characteristics of terrain and stability, which together are responsible for most of the variation in wind speed across a site. Since we don't know a priori how the wind speed should vary with respect to terrain variation, we let the data tell us that in the context of terrain exposure. [Note: terrain exposure is essentially an integration of the elevation differences between a given location - met tower or turbine site - with respect to the surrounding terrain.]
One of the revelations from working with RAMWind is that at most project sites, it is clear that the terrain downwind from the point in question has the dominant effect on wind speed. Typically, the met tower sites with high downwind exposure have higher wind speeds than sites with low or even negative downwind exposure. Having high upwind exposure can have a negative effect on wind speed. Naturally, some exceptions exist, but to a very high degree, these trends have been observed at project sites all over the US and Canada.
Another significant finding that resulted from the analysis of wind speed in the context of terrain exposure is that atmospheric stability has a powerful influence on the degree to which wind speed is affected by terrain elevation differences, as described by RAMWind exposure calculations. Under high stability the terrain (particularly downwind) exerts much greater influence on wind speed than under neutral or unstable atmospheric conditions.
[5] Kline, J and Walls, E: Terrain Effects on Wind Speed Enhanced by Atmospheric Stability, AWEA Wind Resource Seminar, Pittsburgh, PA, September 2012. (download available on Publications page)