...or maybe not. Slide 13 ...now we start to follow the money.

Number of Homes supplied with electricity generated by 45 MW Wind Turbine Plantation in the months of June, August & July 2010:


JUN: 46273KWH ÷ 1200KWH = 38 homes


JUL: 22845KWH ÷ 1200KWH = 19 homes


AUG: 11381KWH ÷ 1200KWH = 9 homes


The preceding was calculated by entering a total of 26,496 elements of recorded wind data over a 3 month period. The data was then upgraded in accordance with “wind profile power law” to accommodate for a difference of 30 meters of height between the proposed wind turbines and the data collection station used for this calculation. the results were then averaged and categorized into "production periods" between 3 meters/sec and 25 meters/sec and then recorded as kwh in accordance with GE Wind Power Curve data for 2.5 MW wind turbines.

Now, all we need is enough wind, or maybe not.[sic] Slide 12 ...now we get to the Don Quixote part

On Bent Mountain, we are blessed with a population of diverse interest and expertise. Our interests, education & expertise have shaped us into a community family that understands how to appreciate and live with our natural environment.


We have a registered private weather station on the Bent Mountain plateau the has been compiling 5-minute data for six years. The exact location of the station will be kept private for the sake of the owner, with the exception that is in an equal or greater wind classification area than the proposed turbine locations.

Now, all we need is enough wind, or maybe not.[sic] Slide 11 ...now we get to the Don Quixote part

A Capacity Factor is number that is applied to identify the “on site” productivity of the turbine or installation of turbines. The number is expressed as a ratio of energy produced (over time) divided by the “Name Plate Capacity” (over the same amount of time).


Invenergy, LLC provided a “35% capacity factor” to the Dean of the Department of Green Engineering at Virginia Tech. which he used to establish projections of providing electrical needs to 10,000 Roanoke Valley Homes annually. He also postulated that 98,000 tons of CO2 emissions would be curtailed as a result of using the 35% capacity factor.


To achieve this measure of efficiency, the wind must operate these turbines on Poor Mountain at a constant speed of 6.75 m/s (15.1 mph), 24 hours per day, 365 days per year. This alone makes claims of producing electricity needs for 10,000 homes (at an AEP rate of 14,400 KWH/home in Roanoke) exaggerated by over 500%!!!

Now, all we need is enough wind, or maybe not.[sic] Slide 10 ...now we get to the Don Quixote part

For the preceding Raleigh Distribution formula, the coefficient of performance factor used is between the 25% to 45% range. This number should not b e confused with the “capacity factor”, which addresses performance over periods of time (typically annual).

Now, all we need is enough wind, or maybe not.[sic] Slide 9 ...now we get to the Don Quixote part



This equation estimates theoretical energy conversion from wind to electricity. Wind turbine manufacturers provide conversion data for various turbine designs as is shown on the following slide.



Now, all we need is enough wind, or maybe not.[sic] Slide 8 ...now we get to the Don Quixote part

These algorithms are yielding “quite erroneous” estimates in mountainous terrain. The roughness factor clearly requires much more locally based evaluation. Terrain based factors for “wind-turbulence” impact in mountain terrain is not addressed, thereby, making data extrapolation less reliable. Wind shear, the radical shifts in wind flow are most common in the mountains. The wind characteristics that attract migrating species, thermals and downdrafts along ridges create a “roughness” factor on an extreme ratio of difference as described here that it become apparent that site specific data and analysis must be undertaken in mountainous terrain.

Click on the picture to get a larger image on your screen.

Now, all we need is enough wind, or maybe not.[sic] Slide 7

The characteristics of the wind in mountainous terrain where wind-flow patterns are “choppy” makes analysis of energy production potential extremely critical as to specific site data collection location & height. Reaping the wind of energy is far less predictable in mountainous terrain than deserts, plains, coastal and off-shore areas.

Click on the picture to get a larger image on your screen.

Now, all we need is enough wind, or maybe not.[sic] Slide 6

Off-shore winds are relatively smooth, stable, and more predictable in terms of constant flow.


On-shore winds vary wildly in the vertical direction as influenced by objects including buildings, trees and terrain.

Now, all we need is enough wind, or maybe not.[sic] Slide 5

With less than desirable siting, in wind power terms, than recommended by the American Wind Energy Association (AWEA), an industry supported organization: “In general, sites with a Wind Power Class rating of 4 or higher are now preferred for large scale wind plants.”


9 turbines proposed in Class 3 areas


5 turbines proposed in Class 4 areas


3 turbines proposed in Class 5 areas


1 turbine proposed in Class 6 area

Click on the picture to get a larger image on your screen.

Now, all we need is enough wind, or maybe not.[sic] Slide 4

Focusing in on the project site we see that the Wind Classes are predominately Class 3 & Class 4 areas. Notably, these are of lower energy producing potential than many other Class 5, 6 & 7 wind areas in the vicinity.

Click on the picture to get a larger image on your screen.

Now, all we need is enough wind, or maybe not.[sic] Slide 3

The yellow ring begins to focus on a specific area of a proposed 18 – 2.5MW Turbine Plantation. Note that there are an abundance of Class 5, 6 & some 7 wind areas in the Roanoke, Franklin, Montgomery & Floyd County vicinities. Notably along the Blue Ridge Parkway, Mason’s Knob and Cahas Mountain near Boone’s Mill to the east.

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Now, all we need is enough wind, or maybe not.[sic] Slide 2



Publicly available data created with a “computer-screen” pixel sized (650’ x650’) conclusion seems to be extraordinarily insensitive to local conditions and a poor basis for weighing benefit and environmental loss. The AWEA, an industry organization and AWS/Truepower, a private engineering consulting firm, both advise that local data be gathered to more accurately describe the energy production potential of the specific sites.

Click on the picture to get a larger image on your screen.

Now, all we need is enough wind, or maybe not.[sic] Slide 1

In response to the need to clearly conduct an objective, scientifically based, COST/Benefit Analysis; I have described, as accurately (to scale) as possible, within a graphic venue, and with an ethical standard based upon the best interests of the human and other species of being on our planet, a study in process, to arrive at a clear matrix-based understanding of the real cost of producing electricity/per acre on Poor Mountain.

My intention is to present this study as a virtual slide presentation/interactive study. Therefore please comment on each "slide/blog" as we proceed. I'll try to respond back and modify study approach as we proceed through our understanding. Click on the picture to get a larger image on your screen.

What's so special about Poor Mountain?

Ed Kinser, a wildlife biologist, former staff biologist at Mountain Lake, alpaca farmer, and most importantly, a neighbor in our Bent Mountain Community is generously sharing his love of the Appalachian Mountain Environment. Ed, originally from Tazewell County, Virginia is developing a particular interest in the Bent Mountain environment and it's unique place on the northern most peninsula of the greater Blue Ridge Plateau. Ed very recently shared his account of his geological survey with Bruce Davidson:

Quartz veins in granite indicate
a geological fault location on Poor Mountain
Photo by Eldon Karr

"Bruce Davidson, retired geologist, accompanied Bob Johnson and me up Poor Mt. on Wednesday. This allowed the addition of some geological information to our Poor Mt. Natural History, which, in turn, helps to verify that Poor Mt. is unique among the Blue Ridges. From the Parkway, and along 221, and up to the second entry onto Willet, the rock formations are granitic and are a part of the Blue Ridge Formation (Precambrian). Just before getting to the Karr’s driveway, there is a transition zone with a mixture of rocks and lots of fractures, along with intrusions, indicating a fault zone. From there on up to the top, there are sandstones and shales from the Unicoi Formation (younger and Cambrian). Most sources just say that the Blue Ridges are granitic in nature."

A shale concretion on Poor Mountain
Photo by Ed Kinser

"It was so much fun and so interesting-- I took Joanie back over to point out the features we saw. "

"We found a shale concretion that is in the road bank near the top. Think we can convince folks that it is a fossilized turtle? "


Earlier in the season, we also found an American chestnut that looks unusually healthy and has lots of fruiting bodies on it.

An American Chestnut on Poor Mountain
Photo by Ed Kinser

Ironweed and Jewelweed provide a late summer presentation of Poor Mountain colors.
In the late spring, look for displays of  Catawba Rhododendren & Flame Azaelea.

Photo by Eldon Karr