1 using gis to analyze wind turbine sites with the shakopee public utilities electric service...
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Using GIS to Analyze Wind Turbine Sites with the Shakopee Public
Utilities Electric Service Territory, Shakopee, MN USA
Jay T. Berken March 25, 2010
Mid – West ESRI Utility Users Group
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Abstract
This study is a comprehensive spatial analysis to determine the best placement
of wind turbines in Shakopee Public Utilities (SPU) electric territory using
geographic data layers.
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Introduction
With every increasing demand of electricity due to computers, plasma TVs, air-
conditioning, and all of other standard home appliances we take for granted, there is a every growing need for new
sources of electricity. Electricity produced by wind turbines is becoming one of the
viable sources to help meet this demand.
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Why examine wind turbine siting?
Projection of 130,000 1.5 mega-watt (MW) turbines will be built in the next five years globally per the Department of Interior– 1.5MW = power for about 500 households
per year
Electricity can be produced locally– Reduce transmission construction– Produce local employment with installation
and maintenance
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Wind turbine siting limitations
Site must consist of large open land such as agricultural and prairie land– Areas usually located in low populated
areas with low electric demand
Wind’s peak electric generation time is at night– Battery technology constantly
researched for electric storage at these times
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Why look at SPU?
With an electric service territory of about 33 square miles, consist of large tracts of agricultural land
SPU is a suburb of the Twin Cities metropolitan area with high demand
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Purpose of study
Develop a process to collect and analyze data related to wind turbine site selection within SPU electric service territory
Project is to produce four different analysis results
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Data layers
SPU electric service territory
Wind speed resource Scott county parcels Two foot land
contours SPU three phase
electric lines (OH/UG)
Roads
Streams Shorelines Wetlands Wooded areas Railways Metropolitan Urban
Service Area (MUSA)
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Layer Coding
Each layer was converted into raster
With raster's capabilities to add and subtract, each layer was given a (+) or (-) value depending whether the layer was an asset or a detriment to a turbine site
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Layers given (+) value
Wind speed resource and land contours layers insured maximum wind capacity
Land parcels and its zoning attribute determined which land areas were best suitable
Roadway and the electric power lines layer determined areas best to keep construction costs minimal
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Layers given (-) value
Streams, shorelines, wetlands, and woodlands layer were detriments due to removal and/or restoration
Railways layer was detriment due to difficulty to obtain permit
MUSA layer was a detriment since it has the capabilities of water and sewer utilities which have higher land value
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Calculating raster layers
Wind speed resource – data collected at 30 meters in height with cell size of 500 meters– Reclassified into four categories in
meters/second with lowest value set at 1 and highest value set at 4
Value Wind Speed (m/s)
1 4.1 - 4.5
2 4.5 - 5.0
3 5.0 - 5.5
4 5.5 - 5.6
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Calculating raster layers Parcel land use – data converted to
cell size of 50 foot– Parcels which were 2 acres or greater
were selected– The parcel subtypes used were the
residential, commercial, industrial, and agricultural such that residential being least desirable to agricultural being most desirable
Value Land Use0 Residential1 Commercial2 Industrial3 Agricultural
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Calculating raster layers Contour layer – data converted to
cell size of 50 foot– convert from two foot elevation lines to
10 foot– aspect to abstract northwest-facing
areas
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Calculating raster layers
Electric power lines selected three phase subtype from overhead and underground lines with buffer of 1320 feet and cell size of 50 foot
Roads layer selected main highways and county roads with buffer of 1320 feet and cell size of 50 foot
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Calculating raster layers
Streams - 150 foot buffer with cell size of 50 foot Woods - 150 foot buffer with cell size of 50 foot Shorelines - 150 foot buffer with cell size of 50
foot Wetlands - 70 foot buffer with cell size of 50 foot Railways - 150 foot buffer with cell size of 50
foot MUSA - zero foot buffer
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Calculating raster layers
Layers Value
Land Contours 1
Roads 1
Electric Power Lines 1
MUSA -1
Railways -1
Streams -1
Shorelines -1
Swamps/Wetland -1
Woods -1
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Four analyses constructed
Environmental and Political Analysis Geographical Analysis Cost Effective Analysis Equal Value Layer Analysis
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Environmental and Political Analysis
Emphasizes issues that impacted the sites environmentally and politically– areas where high bird and bat migration
and sensitive environmental areas– areas with larger population densities more
likely one will find expressions of NIMBY (not in my back yard)
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Geographical Analysis
Takes into consideration the physical land properties of the sites– having sufficient land area to structurally
construct a wind turbine– being in close proximity to roadways that
support larger vehicles
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Cost Effective Analysis Takes into consideration sites with the
greatest return on the investment of the wind turbine– evaluating the wind resource for the
greatest wind speed and direction– indicate land cost value which is more
costly to buy or lease the land– maintain low cost for construction and
maintenance of the site
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Equal Value Layer Analysis
Analysis gave insight to all layers with no emphasis on a set of layers
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Construction of Analyses
To get the values of each analysis a survey was given to 13 employees at SPU asked to rank each layer of each analysis on a scale of one to three with three being the most important value according to each analysis. Results
were than categorized, summed, and finally the layers reclassified.
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Analyses Final Values
Layers Geography Cost Effective Environmental
& Political
Analysis Value Analysis Value Analysis Value
Wind Resource Table 1 Table 1 Table 1
Land Parcel Use Table 2 Table 3 Table 3
Land Contours 4 4 2
Roads 2 2 2
Electric Power Lines 3 4 2
MUSA -2 -2 -2
Railways -2 -2 -2
Streams -3 -2 -4
Shorelines -2 -2 -4
Wetland -3 -3 -4
Woods -3 -2 -3
Value Land Use
1 Residential
2 Commercial
3 Industrial
4 Agricultural
Value Land Use
0 Residential
1 Commercial
2 Industrial
3 Agricultural
Value Wind Speed (m/s)
1 4.1 - 4.5
2 4.5 - 5.0
3 5.0 - 5.5
4 5.5 - 5.6
Table 3: Table 2: Table 1:
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Geography Analysis Results
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Environmental and Political Analysis Results
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Cost Effective Analysis Results
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Equal Value Layer Analysis Results
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Reclassify Groups of Values to Single ValueGeographic Analysis Cost Effective Analysis
Old Values New Values Old Values New Values
(-5 - 0) 1 (-5 - 0) 1
(0 to 4) 2 (0 to 4) 2
(4 to 8) 3 (4 to 8) 3
(8 to 12) 4 (8 to 12) 4
(12 to 14) 5 (12 to 16) 5
Environment & Political Analysis Equal Value Layer Analysis
Old Values New Values Old Values New Values
(-8 to 0) 1 (-1 to 0) 1
(0 to 3) 2 (0 to 3) 2
(3 to 6) 3 (3 to 6) 3
(6 to 9) 4 (6 to 9) 4
(9 to 12) 5
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Geography Analysis Final Results
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Environmental and Political Analysis Final Results
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Cost Effective Analysis Final Results
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Equal Value Layer Analysis Final Results
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Challenges to take into consideration
The spatial reference of the datum between the wind resource and all other layers are slightly different
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Challenges to take into consideration (Cont.)
Due to the large cell size of the wind layer, the overall project analysis was only able to be evaluated with using the larger cell size of 500 meter, but no evidence of significant change when viewed with side-by-side comparisons
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Challenges to take into consideration (Cont.) Each of the layer buffers (i.e.
wetlands buffer of 70 feet) were interpreted to specific bylaws and ordinances of SPU’s territory, so further interpretations within the border of SPU at a later date can change. Also there can be different buffers in other electric service territories.
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Results
Areas where the wind resources were strong, but other resources were weak (i.e. electric lines and roads)
Showed potentials of wind turbine sites according to analyses with different layer values
Unexpected areas of potential wind turbine sites
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Conclusion
Wind turbine site selecting is almost as much of an art as it is a science. Each layer can be manipulated in its value, buffer distance, and its original data
acquisition. This study was interpreted as a guide to finding the best wind turbine
sites in a given service territory at a macro level. A follow-up micro study with onsite data collection must be done for
suitable sites.
