Presentation on theme: "1 Using GIS to Analyze Wind Turbine Sites with the Shakopee Public Utilities Electric Service Territory, Shakopee, MN USA Jay T. Berken March 25, 2010."— Presentation transcript:
1 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
2 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.
3 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.
4 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
5 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 Winds peak electric generation time is at night –Battery technology constantly researched for electric storage at these times
6 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
7 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
8 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)
9 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
10 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
11 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
12 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 ValueWind Speed (m/s) 14.1 - 4.5 24.5 - 5.0 35.0 - 5.5 45.5 - 5.6
14 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 ValueLand Use 0Residential 1Commercial 2Industrial 3Agricultural
16 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
17 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
18 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
19 Calculating raster layers LayersValue Land Contours1 Roads1 Electric Power Lines1 MUSA Railways Streams Shorelines Swamps/Wetland Woods
20 Four analyses constructed Environmental and Political Analysis Geographical Analysis Cost Effective Analysis Equal Value Layer Analysis
21 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)
22 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
23 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
24 Equal Value Layer Analysis Analysis gave insight to all layers with no emphasis on a set of layers
25 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.
26 Analyses Final Values LayersGeographyCost Effective Environmental & Political Analysis Value Wind ResourceTable 1 Land Parcel UseTable 2Table 3 Land Contours442 Roads222 Electric Power Lines342 MUSA-2 Railways-2 Streams-3-2-4 Shorelines-2 -4 Wetland-3 -4 Woods-3-2-3 ValueLand Use 1Residential 2Commercial 3Industrial 4Agricultural ValueLand Use 0Residential 1Commercial 2Industrial 3Agricultural ValueWind Speed (m/s) 14.1 - 4.5 24.5 - 5.0 35.0 - 5.5 45.5 - 5.6 Table 3: Table 2:Table 1:
27 Geography Analysis Results
28 Environmental and Political Analysis Results
29 Cost Effective Analysis Results
30 Equal Value Layer Analysis Results
31 Reclassify Groups of Values to Single Value Geographic AnalysisCost Effective Analysis Old ValuesNew ValuesOld ValuesNew Values (-5 - 0)1 1 (0 to 4)2 2 (4 to 8)3 3 (8 to 12)4 4 (12 to 14)5(12 to 16)5 Environment & Political AnalysisEqual Value Layer Analysis Old ValuesNew ValuesOld ValuesNew Values (-8 to 0)1(-1 to 0)1 (0 to 3)2 2 (3 to 6)3 3 (6 to 9)4 4 (9 to 12)5
32 Geography Analysis Final Results
33 Environmental and Political Analysis Final Results
34 Cost Effective Analysis Final Results
35 Equal Value Layer Analysis Final Results
36 Challenges to take into consideration The spatial reference of the datum between the wind resource and all other layers are slightly different
37 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
38 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 SPUs 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.
39 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
40 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.