1 / 23

Assessment of Environmental Benefits (AEB) Modeling System

Assessment of Environmental Benefits (AEB) Modeling System A coupled energy-air quality modeling system for describing air quality impact of energy efficiency. Fifth Annual CMAS Conference Chapel Hill, NC October 16-18, 2006 Session 5: Regulatory Modeling Studies.

phillip-turner
Download Presentation

Assessment of Environmental Benefits (AEB) Modeling System

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Assessment of Environmental Benefits (AEB) Modeling System • A coupled energy-air quality modeling system for describing air quality impact of energy efficiency Fifth Annual CMAS Conference Chapel Hill, NC October 16-18, 2006 Session 5: Regulatory Modeling Studies Principal Investigator Bob Imhoff bob.imhoff@baronams.com

  2. Assessment of Environmental Benefits Modeling System (AEB) Objective • Get SIP Credit for Air Quality Benefits of Energy Efficiency Technologies: • How do we make the case? • Link together accepted models using new S/W tools and new methods • ORCED = Oak Ridge Competitive Electricity Dispatch model (Stan Hadley, ORNL) • SMOKE • CMAQ • Follow USEPA Guidance of August 5, 2004 to ensure emission reductions will be: Quantifiable, Surplus, Enforceable, Permanent

  3. Development of the Sensitivity Matrix

  4. Source Domain for CMAQ Sensitivity Analyses Southern + TVA + VACAR subregions; that portion of SERC that most closely resembles VISTAS

  5. CMAQ modeling scenarios Future base case: VISTAS OTW 2018 F4 Modeling time period: 1 year Met data: 2002 (VISTAS) Grid resolution: 36 km

  6. Web-based End User Interface

  7. Results – SO2 Emission Reductions NC Scenarios

  8. Results – SO2 Emission Reductions TN Scenarios

  9. Results – SO2 Emission Reductions GA Scenarios

  10. Results – Comparison of SO2 Reductions

  11. “Power-gen Pictogram” originated by Stan Hadley of ORNL, developer of the ORCED power dispatch model

  12. Results – power dispatch

  13. Results – power dispatch

  14. Results – power dispatch

  15. Results – SO2 Reductions, joint action Coordinated EE implementation improves NC-only results by 35% from 43k tons to 58k tons

  16. Results – NOx Emission Reductions

  17. Results – 2018 Reductions at Current Costs Market rates for Allowances from Evolution Markets, Inc. at http://www.evomarkets.com/emissions/index.php?xp1=so2 and http://www.evomarkets.com/emissions/index.php?xp1=sipnox

  18. Results – 2018 Reductions at Projected Cost Beyond 300k Annual Tons SO2 Reduction: $5,000/ton* NOx Allowance: $5,000/ton** *according to recent analysis by G. Stella of Alpine Geophysics, SO2 reductions costs increase exponentially beyond 300k tons reduced **approximate value indicated for 2018 by EIA in AEO2005

  19. Results – 2018 Reductions, Conservative Projection of Costs and Demand Impact SO2 Reduction: $2,115/ton* NOx Allowance: $3,000/ton** *average of per ton cost for annual reductions less than 300k tons (data from analysis by G. Stella of Alpine Geophysics) **approximately mid-way between present day trade value and projection by EIA for 2018

  20. Results Summary

  21. Conclusion: Linkage Between Energy Modeling and Air Quality Modeling with AEB SM Sensitivity Matrix captures the intelligence of CMAQ modeling runs with pollutant-specific, gridded, hourly sensitivity factors. Expresses the modeled sensitivity of emissions and the ambient air in response to changes in power demand Principal benefit: states’ tool for characterizing emissions and air quality benefits from EERE technologies / programs.

  22. Acknowledgments • Bob Imhoff (BAMS),Principal Investigator • Jerry Condrey (BAMS), software tool development • Stan Hadley (ORNL), demand projections and power dispatch modeling • Ted Smith (BAMS), server side development (output products) • Joe Brownsmith (UNCA), EUI development • Dr. Saswati Datta (BAMS), data analysis • Jesse O’Neal (BAMS) project management and outreach • Marilyn Brown and Barbara Ashdown (ORNL), project directors Questions and comments to: Bob Imhoff Baron Advanced Meteorological Systems (BAMS) bob.imhoff@baronams.com

More Related