Mapping suitability areas for concentrated solar power plants using remote sensing data
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, in this paper, we present applications of remote sensing data for mapping suitability areas for concentrated solar power plants. Our approach uses digital elevation model derived from NASA s Shuttle Radar Topographic Mission (SRTM) at a resolution of 3 arc second (approx. 90m resolution) 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 site suitability value for each cell by computing a list of metrics based on applicable siting requirements using GIS data. The computed metrics include population density, solar energy potential, federal lands, and hazardous facilities. Overall, some 30 GIS data 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.more »
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1207053
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Journal of Applied Remote Sensing
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 1931-3195
- Publisher:
- SPIE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; concentrated solar power; geographic information system; digital elevation model; power plant siting; solar radiation; solar power plant
Citation Formats
Omitaomu, Olufemi A., Singh, Nagendra, and Bhaduri, Budhendra L. Mapping suitability areas for concentrated solar power plants using remote sensing data. United States: N. p., 2015.
Web. doi:10.1117/1.JRS.9.097697.
Omitaomu, Olufemi A., Singh, Nagendra, & Bhaduri, Budhendra L. Mapping suitability areas for concentrated solar power plants using remote sensing data. United States. https://doi.org/10.1117/1.JRS.9.097697
Omitaomu, Olufemi A., Singh, Nagendra, and Bhaduri, Budhendra L. 2015.
"Mapping suitability areas for concentrated solar power plants using remote sensing data". United States. https://doi.org/10.1117/1.JRS.9.097697. https://www.osti.gov/servlets/purl/1207053.
@article{osti_1207053,
title = {Mapping suitability areas for concentrated solar power plants using remote sensing data},
author = {Omitaomu, Olufemi A. and Singh, Nagendra and Bhaduri, Budhendra L.},
abstractNote = {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, in this paper, we present applications of remote sensing data for mapping suitability areas for concentrated solar power plants. Our approach uses digital elevation model derived from NASA s Shuttle Radar Topographic Mission (SRTM) at a resolution of 3 arc second (approx. 90m resolution) 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 site suitability value for each cell by computing a list of metrics based on applicable siting requirements using GIS data. The computed metrics include population density, solar energy potential, federal lands, and hazardous facilities. Overall, some 30 GIS data 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. Furthermore, we present results for concentrated solar power that drives a stream turbine and parabolic mirror connected to a Stirling Engine.},
doi = {10.1117/1.JRS.9.097697},
url = {https://www.osti.gov/biblio/1207053},
journal = {Journal of Applied Remote Sensing},
issn = {1931-3195},
number = 1,
volume = 9,
place = {United States},
year = {Thu May 14 00:00:00 EDT 2015},
month = {Thu May 14 00:00:00 EDT 2015}
}
Web of Science
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