1. 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. 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. 3 Development of the Sensitivity Matrix

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

5. 5 CMAQ modeling scenarios CMAQ Modeling Round Computer & procs # Orig Run #DSM Impact Area Future Demand Reduced Source Domain Case NameLine Loss Factor ORCED Control BASENewton 4 Replication of VISTAS 2018 OTW CMAQ modeling run ONENewton 4 Bound Run V8%V V-ALL-8E0L (V8LL0) 0%scen 36 TWONewton 47NC8%V V-NC-8E0L (V-NC8EE0LL) 0%scen 36 TWONewton 48NC8%VST VST-NC-8E0L (VST-NC8EE0LL) 0%scen 34 TWONewton 49NC8%VST VST-NC-8E2L (VST-NC8EE2LL) 2%scen 35 TWONewton 432 NCTN GA 8%VST VST-3-8E0L (VST-NCTNGAEE0LL) 0%scen 34 THREE nNewton 41NC4%VV-NC-4E0L0%scen 36 THREE nNewton 419GA4%SS-GA-4E0L0%scen 40 THREE nNewton 425GA8%SS-GA-8E0L0%scen 40 THREE nNewton 428 NCTN GA 4%VSTVST-3-4E0L0%scen 34 THREE wWinter-star 610TN4%TT-TN-4E0L0%scen 38 THREE wWinter-star 816TN8%TT-TN-8E0L0%scen 38 Future base case: VISTAS OTW 2018 F4 Modeling time period: 1 year Met data: 2002 (VISTAS) Grid resolution: 36 km

6. 6 Web-based End User Interface

7. 7

8. 8 Results – SO 2 Emission Reductions NC Scenarios

9. 9 Results – SO 2 Emission Reductions TN Scenarios

10. 10 Results – SO 2 Emission Reductions GA Scenarios

11. 11 Results – Comparison of SO 2 Reductions

12. 12 “Power-gen Pictogram” originated by Stan Hadley of ORNL, developer of the ORCED power dispatch model

13. 13 Results – power dispatch

14. 14 Results – power dispatch

15. 15 Results – power dispatch

16. 16 Results – SO2 Reductions, joint action Coordinated EE implementation improves NC-only results by 35% from 43k tons to 58k tons

17. 17 Results – NOx Emission Reductions

18. 18 Results – 2018 Reductions at Current Costs Market rates for Allowances from Evolution Markets, Inc. at http://www.evomarkets.com/emissions/index.php?xp1=so2http://www.evomarkets.com/emissions/index.php?xp1=so2 and http://www.evomarkets.com/emissions/index.php?xp1=sipnox

19. 19 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

20. 20 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

21. 21 Results Summary Annual Tons (000s) of Emissions Reduced in 2018 Projected Avoided Costs for Joint Action Scenario ($M) NC 8% Scenario TN 8% Scenario GA 8% Scenario Joint Action 8% Scenario Current Mkt Prices 8% Scen Future High Case 8% Scen Future Interm Case 6% Scen SO2 Emission Reductions 43.125.33.258.2$31.7$291.0$93.3 NOx Emission Reductions 16.06.35.924.0$27.6$138.0$55.5 total costs avoided$59.3$429.0$148.8

22. 22 Conclusion: Linkage Between Energy Modeling and Air Quality Modeling with AEB Intersection is Sensitivity Matrix (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. SM

23. 23 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