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Title: Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources

Abstract

Oak Ridge National Laboratory (ORNL) initiated an internal National Electric Generation Siting Study, which is an ongoing multiphase study addressing several key questions related to our national electrical energy supply. This effort has led to the development of a tool, OR-SAGE (Oak Ridge Siting Analysis for power Generation Expansion), to support siting evaluations. The objective in developing OR-SAGE was to use industry-accepted approaches and/or develop appropriate criteria for screening sites and employ an array of Geographic Information Systems (GIS) data sources at ORNL to identify candidate areas for a power generation technology application. The initial phase of the study examined nuclear power generation. These early nuclear phase results were shared with staff from the Electric Power Research Institute (EPRI), which formed the genesis and support for an expansion of the work to several other power generation forms, including advanced coal with carbon capture and storage (CCS), solar, and compressed air energy storage (CAES). Wind generation was not included in this scope of work for EPRI. The OR-SAGE tool is essentially a dynamic visualization database. The results shown in this report represent a single static set of results using a specific set of input parameters. In this case, the GIS inputmore » parameters were optimized to support an economic study conducted by EPRI. A single set of individual results should not be construed as an ultimate energy solution, since US energy policy is very complex. However, the strength of the OR-SAGE tool is that numerous alternative scenarios can be quickly generated to provide additional insight into electrical generation or other GIS-based applications. The screening process divides the contiguous United States into 100 x 100 m (1-hectare) squares (cells), applying successive power generation-appropriate site selection and evaluation criteria (SSEC) to each cell. There are just under 700 million cells representing the contiguous United States. If a cell meets the requirements of each criterion, the cell is deemed a candidate area for siting a specific power generation form relative to a reference plant for that power type. Some SSEC parameters preclude siting a power plant because of an environmental, regulatory, or land-use constraint. Other SSEC assist in identifying less favorable areas, such as proximity to hazardous operations. All of the selected SSEC tend to recommend against sites. The focus of the ORNL electrical generation source siting study is on identifying candidate areas from which potential sites might be selected, stopping short of performing any detailed site evaluations or comparisons. This approach is designed to quickly screen for and characterize candidate areas. Critical assumptions supporting this work include the supply of cooling water to thermoelectric power generation; a methodology to provide an adequate siting footprint for typical power plant applications; a methodology to estimate thermoelectric plant capacity while accounting for available cooling water; and a methodology to account for future ({approx}2035) siting limitations as population increases and demands on freshwater sources change. OR-SAGE algorithms were built to account for these critical assumptions. Stream flow is the primary thermoelectric plant cooling source evaluated in this study. All cooling was assumed to be provided by a closed-cycle cooling (CCC) system requiring makeup water to account for evaporation and blowdown. Limited evaluations of shoreline cooling and the use of municipal processed water (gray) cooling were performed. Using a representative set of SSEC as input to the OR-SAGE tool and employing the accompanying critical assumptions, independent results for the various power generation sources studied were calculated.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
1032036
Report Number(s):
ORNL/TM-2011/157
600303000; ORNL/TM-2011/157/R1; TRN: US201202%%118
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
01 COAL, LIGNITE, AND PEAT; 25 ENERGY STORAGE; ALGORITHMS; BLOWDOWN; CAPACITY; CARBON; COAL; COMPRESSED AIR ENERGY STORAGE; ECONOMICS; ENERGY POLICY; EVAPORATION; GEOGRAPHIC INFORMATION SYSTEMS; INFORMATION SYSTEMS; LAND USE; NUCLEAR POWER; POWER GENERATION; POWER PLANTS; SITE SELECTION; STORAGE

Citation Formats

Mays, Gary T, Belles, Randy, Blevins, Brandon R, Hadley, Stanton W, Harrison, Thomas J, Jochem, Warren C, Neish, Bradley S, Omitaomu, Olufemi A, and Rose, Amy N. Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources. United States: N. p., 2012. Web. doi:10.2172/1032036.
Mays, Gary T, Belles, Randy, Blevins, Brandon R, Hadley, Stanton W, Harrison, Thomas J, Jochem, Warren C, Neish, Bradley S, Omitaomu, Olufemi A, & Rose, Amy N. Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources. United States. doi:10.2172/1032036.
Mays, Gary T, Belles, Randy, Blevins, Brandon R, Hadley, Stanton W, Harrison, Thomas J, Jochem, Warren C, Neish, Bradley S, Omitaomu, Olufemi A, and Rose, Amy N. Tue . "Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources". United States. doi:10.2172/1032036. https://www.osti.gov/servlets/purl/1032036.
@article{osti_1032036,
title = {Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources},
author = {Mays, Gary T and Belles, Randy and Blevins, Brandon R and Hadley, Stanton W and Harrison, Thomas J and Jochem, Warren C and Neish, Bradley S and Omitaomu, Olufemi A and Rose, Amy N},
abstractNote = {Oak Ridge National Laboratory (ORNL) initiated an internal National Electric Generation Siting Study, which is an ongoing multiphase study addressing several key questions related to our national electrical energy supply. This effort has led to the development of a tool, OR-SAGE (Oak Ridge Siting Analysis for power Generation Expansion), to support siting evaluations. The objective in developing OR-SAGE was to use industry-accepted approaches and/or develop appropriate criteria for screening sites and employ an array of Geographic Information Systems (GIS) data sources at ORNL to identify candidate areas for a power generation technology application. The initial phase of the study examined nuclear power generation. These early nuclear phase results were shared with staff from the Electric Power Research Institute (EPRI), which formed the genesis and support for an expansion of the work to several other power generation forms, including advanced coal with carbon capture and storage (CCS), solar, and compressed air energy storage (CAES). Wind generation was not included in this scope of work for EPRI. The OR-SAGE tool is essentially a dynamic visualization database. The results shown in this report represent a single static set of results using a specific set of input parameters. In this case, the GIS input parameters were optimized to support an economic study conducted by EPRI. A single set of individual results should not be construed as an ultimate energy solution, since US energy policy is very complex. However, the strength of the OR-SAGE tool is that numerous alternative scenarios can be quickly generated to provide additional insight into electrical generation or other GIS-based applications. The screening process divides the contiguous United States into 100 x 100 m (1-hectare) squares (cells), applying successive power generation-appropriate site selection and evaluation criteria (SSEC) to each cell. There are just under 700 million cells representing the contiguous United States. If a cell meets the requirements of each criterion, the cell is deemed a candidate area for siting a specific power generation form relative to a reference plant for that power type. Some SSEC parameters preclude siting a power plant because of an environmental, regulatory, or land-use constraint. Other SSEC assist in identifying less favorable areas, such as proximity to hazardous operations. All of the selected SSEC tend to recommend against sites. The focus of the ORNL electrical generation source siting study is on identifying candidate areas from which potential sites might be selected, stopping short of performing any detailed site evaluations or comparisons. This approach is designed to quickly screen for and characterize candidate areas. Critical assumptions supporting this work include the supply of cooling water to thermoelectric power generation; a methodology to provide an adequate siting footprint for typical power plant applications; a methodology to estimate thermoelectric plant capacity while accounting for available cooling water; and a methodology to account for future ({approx}2035) siting limitations as population increases and demands on freshwater sources change. OR-SAGE algorithms were built to account for these critical assumptions. Stream flow is the primary thermoelectric plant cooling source evaluated in this study. All cooling was assumed to be provided by a closed-cycle cooling (CCC) system requiring makeup water to account for evaporation and blowdown. Limited evaluations of shoreline cooling and the use of municipal processed water (gray) cooling were performed. Using a representative set of SSEC as input to the OR-SAGE tool and employing the accompanying critical assumptions, independent results for the various power generation sources studied were calculated.},
doi = {10.2172/1032036},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 01 00:00:00 EDT 2012},
month = {Tue May 01 00:00:00 EDT 2012}
}

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