Using GIS to Determine Wind Energy Potential in Minnesota, USA Zihan
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Using GIS to Determine Wind Energy Potential in Minnesota, USA Zihan Yang Department of Resource Analysis, Saint Mary’s University of Minnesota, Winona, MN 55987 Keywords: GIS, Wind Energy, Terrain Elevation, Multi-Criteria, Spatial Analysis Abstract Wind power is an alternative to fossil fuels. It is plentiful, renewable, widely distributed, clean, produces no greenhouse gas emissions during operation and uses relatively little land. Recent studies indicate that Minnesota has plentiful wind energy potential including the possibility to use wind to generate 25 times the electricity Minnesota used in 2010 (American Wind Energy Association, 2012a). This study evaluates the potential wind energy resources in Minnesota by analyzing technical, environmental, and political criteria for developing local renewable wind energy resources. The approach assesses suitable locations for exploring wind turbines energy sources with the aid of a geographic information system. The study considers local wind conditions and other restrictions such as terrain, land use, environment, and human activities. The model identifies areas that have an excellent suitability for future wind turbine placement in Minnesota. When it compares to the approximate estimations based solely on analysis of wind speed, this rule-based model may provide a more accurate method to evaluate wind energy in Minnesota. Introduction contributing cause of global warming is the emission of greenhouse gases (in The issue of global warming is particular carbon dioxide), mainly becoming a potential severe challenge generated by fossil fuel combustion that nations around the world would be (United States Environmental wise to confront. The Protection Agency, 2010). Intergovernmental Panel on Climate The negative effects of fossil Change (IPCC) indicated during the fuel combustion on the environment, in twenty-first century the average global addition to limited fossil fuel supply, surface temperature will likely rise have forced many countries to explore between 1.1° and 2.9° C (2° to 5.2° F) and change to environmentally friendly according to their lowest emissions renewable alternatives in order to meet scenario and between 2.4° and 6.4° C increasing energy demands. Currently, (4.3° to 11.5° F) according to their wind energy is one of the fastest highest emissions scenario (IPCC, developing renewable energy 2007). Scientists have confirmed that a technologies around the world Zihan Yang. 2013. Using GIS to Determine Wind Energy Potential in Minnesota, USA. Volume 15, Papers in Resource Analysis. 13 pp. Saint Mary’s University of Minnesota University Central Services Press. Winona, MN. Retrieved (date) from http://www.gis.smumn.edu. (Ramachandra and Shruthi, 2005). (2) Environmental constraints such According to a report by the as wildlife conservation areas American Wind Energy Association and bodies of water. (AWEA, 2012b), by the end of 2012 (3) Human impact factors such as the United States (US) wind industry major roads, noise, state totaled 60,007 MW of cumulative parkland, and electric lines. wind capacity (from more than 45,100 turbines). Over one fifth of the world’s Datasets for the project were installed wind power capacity is obtained or derived from existing located in the US (BP p.1.c., 2013). datasets and analyzed using separate Although the US wind industry has physical, environmental, and human already made significant strides in impact models. This rule-based model developing electric energy from wind integrated individual outputs into one turbines, there is still plenty of final result which identified suitable opportunity for growth (Schrader, sites for wind turbines in Minnesota. 2012). “Minnesota ranked 3rd in the Methods US in 2010 for percentage of electricity derived from wind” (AWEA, Database Development 2012a). This fact sent a signal to wind energy companies that Minnesota is a The rule-based model used in this wind-friendly state. According to study was a spatial multi-criteria model National Wind (2012), Minnesota has a using diverse variables to represent the total wind resource of 489,271 MW, physical, environmental, and human and this capacity of wind power could impact factors determining wind support nearly 25 times the amount of turbine site suitability. By using Esri’s electricity Minnesota currently needs. ArcMap software, data for each of Thus, there are many potential areas these criteria were converted from for wind turbines. vector format to raster format and However, the locations with the assessed using overlay analysis. All highest wind resources are not always tools referenced in this paper are from feasible sites for wind farms. A variety Esri’s ArcMap software. of factors are decisive in the site The study area for this project selection of wind turbine farms. In this included the entire state of Minnesota. study, a rule-based modeling method In 2006, the Minnesota Department of was used to evaluate and target Commerce developed maps of suitable wind power sites. Using a Minnesota wind resources at different similar approach as Rodman and heights, 30, 80, and 100 m above Meentemeyer (2006), this model ground. Since the height of large wind incorporated the following factors: turbines is generally between 50 and 80 m above ground, wind speed data at (1) Physical criteria such as wind 80 m was used in this study (Figure 1). resources, obstacles, and Terrain strongly affects the terrain. performance of wind turbines; 2 therefore, a slope dataset was derived components: average annual wind from a 30 m digital elevation model speed at 80 m height, obstacles, and (DEM) developed by the United States terrain. Geological Survey (USGS, 2009). The annual average wind speed The environmental data for is useful as a predominant indicator of Minnesota, such as wildlife wind resources. Ten meters is the conservation areas and open water standard height for a typical areas were obtained by the Minnesota meteorological station to measure wind Department of Natural Resources speed. The wind speed suitability for (MNDNR, 2013). Human impact developing wind turbines was factors such as populated areas, major classified into four categories based on roads, and state parkland were also wind velocity at 10 m: poor, marginal, obtained from the MNDNR (2013). good, and excellent (Table 1) The energy grid dataset for Minnesota (Ramachandra and Shruthi, 2005). was obtained from the Minnesota Land Management Information Center Table 1. Annual mean wind speed and (2007). The data above were converted potential value of the wind energy resource at to 30 m resolution raster datasets for 10 m height (Ramachandra and Shruthi, 2005.) the raster overlay analysis. Annual mean wind Indicated value speed at 10 m of wind resource <4.5 m/s Poor 4.5-5.4 m/s Marginal 5.4-6.7 m/s Good >6.7 m/s Excellent Since the hub height of a modern wind turbine is greater than 50 m and the available wind speed data was measured at 80 m above ground, the wind speed classification at 10 m was converted to the wind speed at 80 m in order to provide a uniform standard for analysis. The following formula developed by Davenport (1960) was used to extrapolate wind speed at different heights: Figure 1. Minnesota average annual wind speed at 80 m height. VZ Z (1) Model Development VG Z G Physical Model VZ = the known mean wind speed at height Z in the study area (m/s) The physical model incorporated three VG = the mean wind speed at height ZG, 3 which needs to be extrapolated wind flow and decrease the efficiency (m/s) Table 3. Annual mean wind speed and Z = the height for which the wind potential value of the wind energy resource at speed is computed (m) 80 m above ground in Minnesota. ZG = the height at which is first Annual mean wind Indicated value observed in the same terrain speed at 80 m of wind resource <6.03 m/s Poor (1) α = an empirical exponent, which 6.03-7.24 m/s Good (2) depends on the roughness of the >7.24 m/s Excellent (3) surface. of wind power generation. Since wind According to the available turbines sites are best located away wind data in this research, formula (1) from highly populated areas (described was adapted as the following equation later), obstructions such as buildings (2), which estimated wind speed in the are unlikely obstacles. The presence of same terrain for 80 m height. trees is recognized as an obstacle in open areas. Wind turbine suitability 80 0.14 decreases as the density of trees = V 10 (2) 10 increase (Rodman and Meentemeyer, 2006). For this model, a forest layer was derived from the National Gap By using equation (2), the new Analysis Program (2001) Land Cover wind classification at 80 m height was Data. Forest densities were determined developed (Table 2). from vegetation types at a given location. If the attribute table of the Table 2. Annual mean wind speed and vegetation dataset showed the land was potential value of the wind energy resource at occupied by trees in the study area, it 80 m height. was defined as a high density forest. If Annual mean wind Indicated value the attribute table showed there were speed at 80 m of wind resource no trees in the study area, it was <6.03 m/s Poor defined as no forest. 6.03-7.24 m/s Marginal The “no forest” class was 7.24-8.98 m/s Good further divided into two subclasses. >8.98 m/s Excellent These were “Shrub Land and Grassland” and “Agricultural However, the range of wind Vegetation.” The Ames Laboratory speed values published by the (2010) reported “wind turbines help Minnesota Department of Commerce channel beneficial breezes over nearby (4.92 m/s to 8.86 m/s) was narrower plants.” This can keep the crops cooler than the resulting suitability and drier, which helps them fend off classification.