Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability. (September 2015)

• Record Type: Journal Article
• Authors: Brewer, Justin; Ames, Daniel P.; Solan, David; Lee, Randy; Carlisle, Juliet
• Is Part Of: Renewable energy. Volume 81(2015)
• Page Start: 825
• Page End: 836
• Publication Date: 2015-09
• Subjects: Photovoltaic electricity -- Site suitability -- Public attitudes -- GIS -- Solar energy; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.04.017
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Determining socially acceptable and economically viable locations for utility-scale solar projects is a costly process that depends on many technical, economic, environmental and social factors. This paper presents a GIS-based multi-criteria solar project siting study conducted in the southwestern United States with a unique social preference component. Proximity raster layers were derived from features including roads, power lines, and rivers then overlain with 10 × 10 m raster terrain datasets including slope and potential irradiance to produce a high resolution map showing solar energy potential from "poor" to "excellent" for high potential counties across the southwestern United States. Similar maps were produced by adding social acceptance data collected from a series of surveys showing the potential public resistance to development that can be expected in areas of high solar energy suitability. Applying social preferences to the model significantly reduced the amount of suitable area in each of the selected study areas. The methods demonstrated are expected to help reduce time, money, and resources currently allocated toward finding and assessing areas of high solar power suitability. Highlights: We conduct a GIS site suitability analysis for utility scale photovoltaic facilities. We identify optimal counties in several U.S. states for further investigation. A model of site suitability based on social preference data is compared to the initial model. Optimal areas under both models are compared within the case study counties. Results indicate a significant reduction in buildable areas when social preferences information is included.