1 Assessment of Environmental Benefits (AEB) Modeling System A coupled energy-air quality modeling...
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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
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Transcript of 1 Assessment of Environmental Benefits (AEB) Modeling System A coupled energy-air quality modeling...
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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, 2006Session 5: Regulatory Modeling Studies
Principal Investigator Bob Imhoff
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
Reductions in Power Demand
Reductions in Power Plant Emissions
Improvements in Air Quality
3
CMAQ output: hr by hr, gridded
adjusted conc of ambient pollutant
(Cmod)
CMAQ using modified EI
M3FILECALC Creates Files of
Differences:Cbase-Cmod,
End-user Interface
Sensitivity Matrix
DSM Impact area, Power
source domain, Temporal profile, Line Loss Factor
Run ORCED model to solve dispatch given
set-up parameters
Emission Inventory and
Generation Modified
Software development undertaken through ASTEC AEB project
Sensitivity Matrix as
NetCDF files using M3HDR
VISTASEmission Inventory 2018 OTW Base F4
raw emissions
CMAQ output: hr by hr, gridded conc
of ambient pollutant (Cbase)
SMOKE Emissions Processor
System
CMAQbase case
Emission Inventory
Base Case andPower Demand
Base Case
VISTASMeteorological Episode (MM5)
M3EMISADJM3FILECALCModify EGUs
generation and emissions
ORCED2-EMISADJInterface
between ORCED and M3EMISADJ
Sens Matrix devel w output 5 7 for
DC.vsd
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
CMAQ Modeling Round
Computer & procs #
Orig Run # DSM Impact Area
Future Demand Reduced
Source Domain
Case Name Line Loss Factor
ORCEDControl
BASE Newton 4 Replication of VISTAS 2018 OTW CMAQ modeling run
ONE Newton 4Bound
Run V 8% VV-ALL-8E0L
(V8LL0)0% scen 36
TWO Newton 4 7 NC 8% VV-NC-8E0L
(V-NC8EE0LL)0% scen 36
TWO Newton 4 8 NC 8% VSTVST-NC-8E0L
(VST-NC8EE0LL)0% scen 34
TWO Newton 4 9 NC 8% VSTVST-NC-8E2L
(VST-NC8EE2LL)2% scen 35
TWO Newton 4 32NCTN
GA8% VST
VST-3-8E0L(VST-NCTNGAEE0LL)
0% scen 34
THREE n Newton 4 1 NC 4% V V-NC-4E0L 0% scen 36
THREE n Newton 4 19 GA 4% S S-GA-4E0L 0% scen 40
THREE n Newton 4 25 GA 8% S S-GA-8E0L 0% scen 40
THREE n Newton 4 28NCTN
GA4% VST VST-3-4E0L 0% scen 34
THREE w Winter-star 6 10 TN 4% T T-TN-4E0L 0% scen 38
THREE w Winter-star 8 16 TN 8% T T-TN-8E0L 0% scen 38
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
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Results – SO2 Emission Reductions NC Scenarios
Power Plant SO2 Emissions Reductions in 2018 from Percentage Decrease in NC Electricity Demand
(EGU domain = VACAR)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
To
ns
(000
s) p
er Y
ear
VA EGUs 1.4 2.8 4.2 5.6
SC 4.0 8.0 12.1 16.3
NC 5.9 11.8 16.5 21.2
NC 2% NC 4% NC 6% NC 8%
9
Power Plant SO2 Emissions Reductions in 2018from Percentage Decrease in TN Electricity Demand
(EGU domain = TVA)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
To
ns
(000
s) p
er Y
ear
AL EGUs 3.5 6.9 10.1 13.4
KY 1.1 2.1 3.7 5.2
TN 1.4 2.8 4.7 6.7
TN 2% TN 4% TN 6% TN 8%
Results – SO2 Emission Reductions TN Scenarios
10
Power Plant SO2 Emissions Reductions in 2018from Percentage Decrease in GA Electricity Demand
(EGU domain = Southern)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
To
ns
(000
s) p
er Y
ear
MS EGUs 0.0 0.0 0.1 0.1
GA 0.3 0.6 1.0 1.3
FL 0.3 0.5 0.9 1.2
AL 0.2 0.4 0.6 0.8
GA 2% GA 4% GA 6% GA 8%
Results – SO2 Emission Reductions GA Scenarios
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Results – Comparison of SO2 Reductions
Power Plants SO2 Emissions Reductions in 2018 Comparison of State Demand Reduction Scenarios
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
NC 2% NC 4% NC 6% NC 8% TN 2% TN 4% TN 6% TN 8% GA 2% GA 4% GA 6% GA 8%
To
ns
(000
s) p
er Y
ear
TN KY AL FL GA MS NC SC VA EGUs
12
“Power-gen Pictogram” originated by Stan Hadley of ORNL,developer of the ORCED power dispatch model
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Results – power dispatch
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Results – power dispatch
15
Results – power dispatch
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Results – SO2 Reductions, joint action
Power Plant SO2 Emissions Reductions in 2018 from Coordinated Percentage Decrease in Electricity Demand for NC, TN & GA
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
To
ns
(000
s) p
er Y
ear
VA EGUs 0.7 1.3 2.1 2.9
SC 0.7 1.5 2.3 3.2
NC 6.5 12.9 16.5 20.0
MS 0.2 0.4 0.6 0.8
GA 2.2 4.5 7.3 10.1
FL 0.8 1.7 2.7 3.7
AL 2.4 4.7 7.8 10.8
KY 0.7 1.4 2.3 3.2
TN 0.9 1.7 2.6 3.5
NC+TN+GA 2% NC+TN+GA 4% NC+TN+GA 6% NC+TN+GA 8%
Coordinated EE implementation improves NC-only results by 35% from 43k tons to 58k tons
17
Results – NOx Emission Reductions
Power Plant NOx Emissions Reductions in 2018 Comparison of State Demand Reduction Scenarios
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
NC 2% NC 4% NC 6% NC 8% TN 2% TN 4% TN 6% TN 8% GA 2% GA 4% GA 6% GA 8%
To
ns
(000
s) p
er Y
ear
TN KY AL FL GA MS NC SC VA EGUs
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Results – 2018 Reductions at Current Costs
Avoided Emissions Control Costs in 2018 with Current Market Value of Allowance
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
To
tal
Avo
ided
Co
st (
$M)
NOx reducts cost ($M) 18.5 7.2 6.9 27.5
SO2 reducts cost ($M) 23.5 13.7 1.9 31.7
NC 8% TN 8% GA 8% NC+TN+GA 8%
10/3/06 Market RateSO2 Allowance: $545NOx Allowance: $1,150
Market rates for Allowances from Evolution Markets, Inc. at
http://www.evomarkets.com/emissions/index.php?xp1=so2 and
http://www.evomarkets.com/emissions/index.php?xp1=sipnox
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Results – 2018 Reductions at Projected Cost
Avoided Emissions Control Costs in 2018 Beyond 300k Annual Tons Reduced
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0T
ota
l A
void
ed C
ost
($M
)
NOx reducts cost ($M) 80.5 31.5 29.8 119.7
SO2 reducts cost ($M) 215.3 126.1 17.3 290.4
NC 8% TN 8% GA 8% NC+TN+GA 8%
Beyond 300k Annual TonsSO2 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
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Results – 2018 Reductions, Conservative Projection of Costs and Demand Impact
Avoided Emissions Control Costs in 2018, Intermediate Scenario
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
To
tal
Avo
ided
Co
st (
$M)
NOx reducts cost ($M) 36.8 13.7 13.8 55.5
SO2 reducts cost ($M) 69.4 39.2 5.2 93.3
NC 6% TN 6% GA 6% NC+TN+GA 6%
SO2 Reduction: $2,115/ton*NOx Allowance: $3,000/ton**
*average of per ton cost for annual reductions less than 300k tons (data from analysis by G. Stella of Alpine Geophysics)
**approximately mid-way between present day trade value and projection by EIA for 2018
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Results Summary
Annual Tons (000s) of Emissions Reduced in 2018
Projected Avoided Costs for Joint Action Scenario ($M)
NC 8% Scenario
TN 8% Scenario
GA 8% Scenario
Joint Action 8% Scenario
Current Mkt Prices8% Scen
Future High Case8% Scen
Future Interm Case
6% Scen
SO2 Emission Reductions
43.1 25.3 3.2 58.2 $31.7 $291.0 $93.3
NOx Emission Reductions
16.0 6.3 5.9 24.0 $27.6 $138.0 $55.5
total costs avoided $59.3 $429.0 $148.8
22
Conclusion: Linkage Between Energy Modeling and Air Quality Modeling with AEB
Air Quality Modeling and Policy Development
Energy Modeling and Policy Development
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
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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)
