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

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Title

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

Creator

  • Author: Mays, Gary T
    Creator Type: Personal
    Creator Info: ORNL
  • Author: Belles, Randy
    Creator Type: Personal
    Creator Info: ORNL
  • Author: Blevins, Brandon R
    Creator Type: Personal
    Creator Info: ORNL
  • Author: Hadley, Stanton W
    Creator Type: Personal
    Creator Info: ORNL
  • Author: Harrison, Thomas J
    Creator Type: Personal
    Creator Info: ORNL
  • Author: Jochem, Warren C
    Creator Type: Personal
    Creator Info: ORNL
  • Author: Neish, Bradley S
    Creator Type: Personal
    Creator Info: ORNL
  • Author: Omitaomu, Olufemi A
    Creator Type: Personal
    Creator Info: ORNL
  • Author: Rose, Amy N
    Creator Type: Personal
    Creator Info: ORNL

Contributor

  • Sponsor: Oak Ridge National Laboratory
    Contributor Type: Organization

Publisher

  • Name: Oak Ridge National Laboratory
    Place of Publication: United States

Date

  • Creation: 2012-05-01

Language

  • English

Description

  • Content Description: 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.

Subject

  • STI Subject Categories: 25 Energy Storage
  • Keyword: Power Generation
  • Keyword: Storage
  • Keyword: Information Systems
  • Keyword: Capacity
  • Keyword: Economics
  • STI Subject Categories: 01 Coal, Lignite, And Peat
  • Keyword: Land Use
  • Keyword: Site Selection
  • Keyword: Evaporation
  • Keyword: Coal
  • Keyword: Blowdown
  • Keyword: Power Plants
  • Keyword: Algorithms
  • Keyword: Carbon
  • Keyword: Energy Policy
  • Keyword: Nuclear Power
  • Keyword: Compressed Air Energy Storage
  • Keyword: Geographic Information Systems

Collection

  • Name: Office of Scientific & Technical Information Technical Reports
    Code: OSTI

Institution

  • Name: UNT Libraries Government Documents Department
    Code: UNTGD

Resource Type

  • Report

Format

  • Text

Identifier

  • Report No.: ORNL/TM-2011/157
  • Grant Number: DE-AC05-00OR22725
  • DOI: 10.2172/1032036
  • Office of Scientific & Technical Information Report Number: 1032036
  • Archival Resource Key: ark:/67531/metadc836564