Samantha Harris
Final Project
UEP 232
12/15/2010
Wind Energy Suitability Analysis for the Matanuska-Susitna Valley
Project Description:
This project is a wind turbine suitability project in Alaska, specifically, for the
Matanuska-Susitna (Mat-Su) Borough. Alaska is blessed with peaks, valleys and coasts that
create massive wind flows. However, because of Alaska’s natural beauty many
environmentalists and residents oppose wind projects that would destroy the beautiful
views as well as the flora and fauna. Likewise, accessibility is also a deterrent to
establishing wind farms throughout Alaska because of the lack of road infrastructure
throughout most of the state.
The project is to find the most suitable areas for wind turbine farm projects in the
borough given certain criteria. Primarily, the analysis addresses the NIMBYism (Not In My
BackYard) of wind farms by avoiding national parks, environmentally sensitive areas like
wetlands and forests, and addressing the social issues like the fact that the turbines
obstruct scenic views, are noisy, can cause health issues and can cause electromagnetic
interference. These specific criteria are:
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Not in national or state parks
Land use criteria which classifies certain land use areas
Wind speeds over the class of 3 deemed by the NREL (National Renewable Energy
Laboratory) close to roads for easy access for both construction and maintenance
Away from airports
Not on steep slopes
Safe distance from developed lands
The data used in the project are as follows:
Data Layer
Source
Scale
Data Date
Elevation
USGS National Map
Viewer
NREL
Mat-Su Borough GIS
1: 250,000
Could not find
Could not find
1: 6,000
November, 2003
October, 2008
Mat-Su/town
Boundary
National/State Parks
Airports
Mat-Su Borough GIS
1: 250,000
July, 2009
ESRI
DOT RITA
Could not find
n/a
2005
2010
Land Cover Dataset,
Developed Land
MRLC Data
1: 100,000
March, 2008
Wind Speed
Roads
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Samantha Harris
Final Project
UEP 232
12/15/2010
Obviously, the ideal situation would be to have perfect accuracy, however, this is not an
option. Because this project is a regional project, there can be room for some error. The
reasonable accuracy level would be between +/- 200 meters or so. If a team were to
investigate the results of the GIS analysis, something more than this estimate such as a mile,
would have them wandering around looking for the site in too big of an area.
Websites for the data:
Mat-Su Borough GIS:
http://www.matsugov.us/it/index.php?option=com_content&view=article&id=14:shapefil
es&catid=5:downloadable-data&Itemid=7
Metadata website:
http://ww1.matsugov.us/index.php?option=com_docman&task=doc_download&gid
=101&Itemid=202
NREL: http://www.nrel.gov/gis/data_analysis.html
USGS: http://nationalmap.gov/viewers.html
Airports: http://www.bts.gov/publications/national_transportation_atlas_database/2010/
MRLC: http://www.mrlc.gov/
DOT: Research and Innovative Technology Administration (RITA):
http://www.mrlc.gov/nlcd_multizone_map.php
ESRI (M: Drive)
Methodology:
Chosen coordinate system: NAD 1983 StatePlane Alaska 4 coordinate system.
For exactness and because so much of the valley is undeveloped, the project uses the entire
Mat-Su borough for the analysis area. Using the borough boundary, the data layers were
clipped using both vector and raster data clipping tools to only include the data inside the
Mat-Su Borough boundary line. This not only made the layers smaller and more
manageable, but more aesthetically pleasing for final map outputs. The Spatial Analysis
Options were also set with the extent and analysis mask as the Mat-Su Borough boundary,
and cell size of 30 meters. However, since the NAD 1983 State Plane Alaska 4 coordinate
system is in feet this had to be converted to 98.45 feet, which is the same distance as 30
meters.
The first step was to select relevant wind speeds by using the select by attribute tool. NREL
states that out of the wind classes of 1-7, the classes over 3 are sufficient for wind farms of
larger sizes. The wind classes selected were 3-7. This selection of wind classes was used to
for another selection to make sure they were not located in a national or state park. Since
the select by location tool does not have a “not in” tool, the selection was made for the wind
classes in parks, and then the switch selection feature was used to finally obtain good wind
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Samantha Harris
Final Project
UEP 232
12/15/2010
speeds not located in a park. This data layer was then converted into a raster data set
containing possible wind classes.
The land cover data set was downloaded from MRLC, and by using the raster clipping tool,
clipped to the Mat-Su Borough boundary layer. The developed land layer was obtained
from this layer by reclassifying the land uses with developed land as a 1, and everything
else as a 0. It was then possible to create a dataset of only the developed land.
The elevation data was obtained from USGS National Map Viewer in 4 files and by using the
mosaic tool, molded together to create one raster file with the relevant elevation data
within the Mat-Su Borough. A hillshade layer was created from this dem data by using the
spatial analyst, surface analysis, hillshade tool. There was one issue because the coordinate
system of the NAD 1983 State Plane Alaska 4 expresses the units in feet and the elevation
data is expressed in meters. The Z factor, or vertical factor, had to be converted from meters
into feet with the conversion factor of 3.281.
To get the slope, the same spatial analyst, surface analysis was used, but this time with
slope. Again because of the coordinate system, the Z factor had to be converted from
meters to feet. The slope layer was then reclassified into 5 classes, but keeping the classes
as natural breaks and not defining values.
The airport data was downloaded from DOT RITA, and by using the select by location tool
the airports only within the boundary layer were made into a new data layer.
The roads were useable as is.
For each input raster analysis, a spatial analysis was completed by using the spatial analyst
tools provided in ArcMap.
For distance to roads, and airports, straight line distance analyses were created, and
reclassified to give classifying distances of 1 mile and so on. Also, values were given of 1-5,
5 being the best outcome and 1 being the worst. For the distance to roads classification, a 5
was the best if it was closest to the roads and a 1 when it was further away because this
would be easier for construction and maintenance of the wind turbines. The distance to
airports was the opposite given the turbines would be better not close to airports. A
similar analysis was completed for distance to developed areas as distance to airports,
except the initial distance was not less than 3 miles. There are many different beliefs and
studies that relate to the science of the effects wind farms have on the health of nearby
communities, and the minimum safe distances ranged from 1 mile to 5 miles. The largest
distance of 5 miles consisted of a safe distance to offshore wind farms. None of the wind
farms of this project are planned as offshore wind farms, so the minimum distance was set
to 3 miles and so on.
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Samantha Harris
Final Project
UEP 232
12/15/2010
The land cover data was reclassified to 6 classes to which types of land were restricted, 1, 2,
3, 4, and 5. It is important to note that there are 6 classes, because some of the data not
used and classified as “no data” so it would not be used. The classifications are as follows:
The next step was to use the weighted overlay tool to perform a weighted analysis with all
of the input criteria of distance to roads, distance to airports, wind speeds, distance to
developments, land use, and slope. The first analysis was given equal weighting adding up
to 100% and the same prior classifications of 1-5. The weighted analysis was performed
twice, once with equal weighting, and then with the following weightings equaling 100%:
Wind:
30%
Land Use:
20%
Distance to Roads:
15%
Distance to Airports:
15%
Distance to Developed Land: 5%
Slope:
15%
100%
The final part of the analysis was completed by applying the grouping tool to find cells or
areas that are next to each other. The output is a new layer final with the different groups
and an attribute table that shows the size of the groupings. I decided on areas over 100
acres would be sufficient for a good wind farm because according to the NREL it requires
approximately .25 acres for 1 turbine. For an area of 100 acres, it can be assumed that it
would hold numerous turbines and could theoretically be placed out of sight. In the
attribute table, there is a “count” column that provides the number of “cells” that pertain to
a certain value, the values of 1-5, 5 being the best and meeting most of the input criteria. To
obtain the acreage, the field calculator was used. Knowing each cell is 30m by 30m, which
can be converted to 98.425 feet. The formula is as follows for the conversion to acres:
(count*(98.425 *98.425))/43,560. Areas over 100 acres that have a suitability of 3, 4 or 5
were then selected out by a combination of adding a 3, 2/1/0 column, areas over 100 acres
and classified by a 5 receiving a 1, areas over 100 acres and classified by a 4 receiving a 2,
areas over 100 acres and classified as a 3 a 3, and everything else a 0. These 2 different
types of suitable areas could then be mapped.
This finally showed the best and moderate and least suitable sites for wind farms.
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Samantha Harris
Final Project
UEP 232
12/15/2010
Difficulties:
There were many challenges in this project. The first and most annoying being that the
files were extremely large and there was not enough space on the computer drives to
download many of the files, clip them and do repeated analyses of the already large files.
An external hard drive was finally needed to deal with this issue. Also, since the coordinate
system was projected in feet, it was necessary to convert the Z factor, or vertical factor of
the elevation files and to do this it was necessary to know the conversion factors. Also, just
acquiring the data for the project took hours and hours of searching and filling out online
forms, lots of waiting and dealing with corrupted data. There were many complications due
to the fact that if the exact criteria weren’t established from the beginning of using the data,
including the extent and the cell size, then the results were incorrect and the resulting
layers often didn’t function well if at all.
There were also complications that were never really explained to me and still remain a
mystery. For example, when using the weighted overlay table, the correct information was
input, as well as the “evaluation scale” which I made sure to change before I made any
changes or input any data. After saving the file after processing the overlay, if I wanted to
make any changes in the same table, when I opened the table, the program arbitrarily
changed my rankings. Shown below:
Needless to say that repeatedly re-doing the analyses resulted in a pretty solid knowledge
of the suitability analysis tools and methods.
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Samantha Harris
Final Project
UEP 232
12/15/2010
Conclusions:
This project is a good start for a wind farm suitability analysis, however, not very realistic
without addressing factors like utility lines and if the wind energy generated would really
be enough to power a city or a town in Alaska. The amount of available land for the project
seems to be plentiful which can be seen in the figure below, however, the majority of these
areas are not even close to the roads and appear to be in high elevations, equally as
inaccessible. Even the best site, which is shown in yellow, is far from the largely populated
areas so any generated wind would have to be transferred large distances. So although it’s
great that the project is located far from highly populated areas because of NIMBY issues, it
doesn’t appear that it will be very useful. Addressing the NIMBY criteria does not seem like
a feasible analysis to actually find real usable sites. This might show why wind projects
aren’t sprouting up in Alaska even though the winds are fierce. Alaska is the last frontier of
the United States and many are not convinced that wind farms are necessary enough to ruin
its preserved beauty.
My knowledge of Arc GIS is greater than I thought it ever would be, and I don’t think it
would have been had I chosen something in the state of Massachusetts. Choosing
something out of state might have been more work for both me and my cool professor, but I
learned a great deal about GIS and the program as well as my project. Hwoever, I also
learned that GIS analysis can only take a project so far and needs to be couple with other
planning knowledge and analyses.
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Samantha Harris
Final Project
UEP 232
12/15/2010
Annotated Bibliography:
Journal of Environmental Management: “Environmental management framework for wind
farm siting: Methodology and
case study.” This article is regarding a wind suitability project in Greece, and addresses the
technical, economic, social and environmental aspects of the project to determine whether
areas are suitable for wind energy development. The project uses a multi-criteria analysis
with GIS and a case study of Lesvos Island in Greece. The constraints are listed for the
island which include archaeological sites, wetlands, distance from settlements, and
historical sites, airports, highly productive lands, etc. Many of these are the same
constraints to the Mat-Su Valley, with archaeological sites being similar to national parks
and untouched land. The study then explains an elaborate scheme for GIS analysis to result
in the best suited sites for the wind projects. Although their methodology seems more
convoluted than mine, this article is very helpful to me in terms of knowing specifics for
wind speeds, and relevant distances from certain settlements.
June 2006, Medical Engineering Magazine: “Wind Out of Their Sails”
This article discusses the opposition to the Cape Wind project in Cape Cod, MA. The most
discussed opposition is that the turbines will destroy the beautiful view from Cape Cod
because they will be placed off shore in the ocean. The other issues of how the turbines
will impinge on wildlife and the environment are there as well, but primarily it is one of
NIMBY. Nobody wants to live in a million dollar house with turbines obstructing the view
out the window. This battle has gone on for years, and oppositionists have searched for
every loophole and fiercely battled the turbines. This same battle happens in the Mat-Su
Valley because of its natural beauty, more so, because Alaska is still thought of as the last
frontier with so much untouched, undeveloped land.
May/June 2010: Rural Cooperatives: This article is short, but important because it
discusses a wind farm in a rural area, Unalakleet Valley funded by the $250million Alaska
Renewable Energy Fund. This area is not a tourist site and not highly populated. Although
the area is nonetheless beautiful, the wind far was not opposed and was primarily thought
of as a sustainbility project because the people were forced to spend a lot of money to
power their village due to the lack of state infrastructure and higher energy prices. This
project is different than the Mat-Su Valley but does show that the state has funding for
wind energy as well as motivation.
Renewable Energy, September 2001: “Developing and applying a GIS-assisted approach to
locating wind farms in the UK.” This article discusses Geographical
Information System (GIS)-assisted wind farm location criteria that were developed for the
UK due to the increased demand for wind energy. This article is dated, but provides an
excellent history of wind energy projects. The criteria used for the case study in Lacashire,
UK, were physical, economic, environmental impact, resource,
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Samantha Harris
Final Project
UEP 232
12/15/2010
visual and planning considerations. Specifically, these are proximity to residential areas,
noise/nuisance, shadow flicker, greenbelt, topography, ecology, agricultural land
classification, conservation areas, and distance from electricity gridlines. The private
consultancies listed the following factors: wind speed, prevailing wind, terrain, adjacent
terrain, vegetation, proximity to residential areas, noise and
appearance. They provide their methodology and results of the study. This article with the
case study of Greece provides excellent information because I’ll be doing a similar
suitability analysis.
These following articles were found for Assignment 1 and are still relevant:
The article “Wind and Sun and Farm-Based Energy Sources,” by Don Cormis in the Journal,
Agricultural Research, specifically sites Alaska, Texas and Minnesota as prime states for
wind energy because of the large flat, remote areas. Specifically in Alaska, there are many
remote communities and areas that could easily receive most, if not all, of their energy from
wind turbines. The Matanuska-Susitna Valley in Alaska is thought of as a suburban area of
Alaska, and as well could easily be powered by wind energy, for at least some parts of the
community. The article also states that Alaska would not be a candidate for solar panels
because the state doesn’t receive enough sunlight, especially during the winter.
The article “Steel Forests or Smoke Stacks: the Politics of Visualization in the Cape Wind
Controversy,” by Roopali Phadke in the Environmental Politics Journal, maps out the issues
of NIMBYism and the Cape Wind Project which is much like the NIMBYism and opposition
to wind projects seen in the Mat-Su Valley. Although this is not specifically Alaska, I think
it’s critical to look at one of the most publicized wind projects and its opposition. There are
not a lot of journal articles written specifically regarding the opposition in Alaska, but there
are a lot of web sources, which have the same information. However, this article analyzes
how powerful NIMBYism is and its affect on policies.
This is a news article http://www.alaskapower.org/pdf/windvalley.pdf
proving that the wind turbines are desired in the valley. As a previous resident of the area,
I too can agree with the absurd amounts of winds, especially those that typically blow out
our vehicle windows. It’s clear that small area wind projects, or backyard wind projects are
possible, but what I want to find out is if large-scale projects are possible and to what
extent.
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