GIS has become a critical planning and management tool for addressing where and how to enable sustainable energy efforts.
One persistent problem with developing sustainable energy sources that utilize wind, water, solar, biomass, and thermal energy is distance between population and the energy source utilized. Not surprisingly, of the various types of energy sources, wind and solar are perhaps the most common. An example of addressing the power distribution problem comes from Colorado. In this case, wind and solar energy are possible sources throughout the state; however, few places provide an optimal location for each energy type that can benefit from high-energy input (i.e., high winds and persistent sun) and are near enough to population centers and power distributions sources to make the energy delivery cost effective.
Multi-criteria Modeling in GIS for Optimal Sites
Multi-criteria modeling in GIS has proven useful for locating optimal areas within Colorado. In this case, layers of data include wind potential, solar potential, distance to transmission lines, distance to cities, population density, distance to roads, type of land cover, and if the area is on federal lands (i.e., lack of desire to build energy resources in these lands). Using ArcGIS, the study scaled the layers and weighted their relative worth and then applied them in the multi-criteria model. Results show greater benefits of wind farms in the northeast part of the state relative to other regions, while solar farms are best placed east of Denver.
Analytic Hierarchy Process (AHP) for Optimal Sites
Other attempts also look at optimal location of solar energy project using GIS with analytic hierarchy process (AHP), which is a decision-making technique that breaks problems into hierarchical components that organize data to make them more analyzable. In this case, GIS can be used for spatial analysis, which the benefits and drawbacks for give locations, whether social or physical, can be assessed using AHP as a way to evaluate the worth of a given location. In effect, the location benefits are estimated using GIS but other aspects in each region are also investigated with GIS and scored for the overall model analysis.
Another study looks at different part of the landscape and their carrying capacity for a sustainable energy source, in this case wind energy. This calculation for wind potential is then assessed based on location across the land covers, similar to the study applied for Colorado, and if the location of the wind farms are ideal relative to land cover factors. These example studies demonstrate the burgeoning use of GIS in assessing where to place sustainable energy resources and forecasting their sustainability potential for given regions.
 For more on the study looking at Colorado’s sustainable energy options using GIS, see: Janke, J. R. (2010). Multicriteria GIS modeling of wind and solar farms in Colorado. Renewable Energy, 35(10), 2228–2234.
 For more on using GIS and AHP, see: Uyan, M. (2013). GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews, 28, 11–17.
 For more on this study, see: Tsoutsos, T., Tsitoura, I., Kokologos, D., & Kalaitzakis, K. (2015). Sustainable siting process in large wind farms case study in Crete. Renewable Energy, 75, 474–480.