Abstract
The political push to increase power generation from renewable sources, such as solar energy, requires knowing the best places to site new solar power plants with respect to the applicable regulatory, operational, engineering, environmental, and socioeconomic criteria. Therefore, we present applications of remote sensing data for mapping suitable areas for concentrated solar power (CSP) plants. Our approach uses satellite data from National Aeronautical and Space Administration’s Global Energy and Water Cycle Surface Radiation Budget project at a resolution of 1 deg for estimating global solar radiation for the study area. Then we develop a computational model built on a geographic information system (GIS) platform that divides the study area into a grid of cells and estimates the site suitability value for each cell by computing a list of metrics based on applicable site requirements using GIS data. The computed metrics include population density, solar energy potential, federal lands, and hazardous facilities. Overall, some 30 GIS datasets are used to compute eight metrics. The site suitability value for each cell is computed as an algebraic sum of all metrics for the cell with the assumption that all metrics have equal weight. Finally, we color each cell according to its suitability value. We present results for CSP that drives a stream turbine and parabolic mirror connected to a Stirling engine.
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