Transcript 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.

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
[email protected]
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Assessment of Environmental Benefits Modeling
System (AEB) Objective

Get SIP Credit for Air Quality Benefits of Energy Efficiency
Technologies:
Reductions in
Power Demand

Improvements in
Air Quality
How do we make the case?


Reductions in
Power Plant
Emissions
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
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Development of the Sensitivity Matrix
VISTAS
Emission Inventory
2018 OTW Base F4
raw emissions
SMOKE
Emissions
Processor
System
ORCED2EMISADJ
Interface
between ORCED
and M3EMISADJ
Sens Matrix devel
w output 5 7 for
DC.vsd
CMAQ
base case
M3EMISADJ
M3FILECALC
Modify EGUs
generation and
emissions
VISTAS
Meteorological
Episode (MM5)
Emission
Inventory and
Generation
Modified
Run ORCED
model to solve
dispatch given
set-up
parameters
DSM Impact
area, Power
source domain,
Temporal profile,
Line Loss Factor
Emission
Inventory
Base Case and
Power Demand
Base Case
End-user
Interface
Sensitivity
Matrix
CMAQ using
modified EI
Sensitivity
Matrix as
NetCDF files
using M3HDR
Software development undertaken through ASTEC AEB project
CMAQ output: hr
by hr, gridded conc
of ambient
pollutant (Cbase)
CMAQ output: hr
by hr, gridded
adjusted conc of
ambient pollutant
(Cmod)
M3FILECALC
Creates Files of
Differences:
Cbase-Cmod,
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Source Domain for CMAQ Sensitivity Analyses
Southern + TVA + VACAR
subregions; that portion of
SERC that most closely
resembles VISTAS
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CMAQ modeling scenarios
CMAQ
Modeling Round
Computer &
procs #
Orig Run #
DSM
Impact
Area
Future
Demand
Reduced
Source
Domain
Case Name
Line Loss
Factor
ORCED
Control
Replication of VISTAS 2018 OTW CMAQ modeling run
BASE
Newton 4
ONE
Newton 4
Bound
Run
V
8%
V
V-ALL-8E0L
(V8LL0)
0%
scen 36
TWO
Newton 4
7
NC
8%
V
V-NC-8E0L
(V-NC8EE0LL)
0%
scen 36
TWO
Newton 4
8
NC
8%
VST
VST-NC-8E0L
(VST-NC8EE0LL)
0%
scen 34
TWO
Newton 4
9
NC
8%
VST
VST-NC-8E2L
(VST-NC8EE2LL)
2%
scen 35
TWO
Newton 4
32
NCTN
GA
8%
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
28
NCTN
GA
4%
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
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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)
50.0
45.0
40.0
Tons (000s) per Year
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
NC 2%
NC 4%
NC 6%
VA EGUs
1.4
2.8
4.2
NC 8%
5.6
SC
4.0
8.0
12.1
16.3
NC
5.9
11.8
16.5
21.2
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Results – SO2 Emission Reductions TN Scenarios
Power Plant SO2 Emissions Reductions in 2018
from Percentage Decrease in TN Electricity Demand
(EGU domain = TVA)
30.0
25.0
Tons (000s) per Year
20.0
15.0
10.0
5.0
0.0
TN 2%
TN 4%
TN 6%
TN 8%
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
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Results – SO2 Emission Reductions GA Scenarios
Power Plant SO2 Emissions Reductions in 2018
from Percentage Decrease in GA Electricity Demand
(EGU domain = Southern)
4.0
3.5
Tons (000s) per Year
3.0
2.5
2.0
1.5
1.0
0.5
0.0
GA 2%
GA 4%
GA 6%
GA 8%
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
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Results – Comparison of SO2 Reductions
Power Plants SO2 Emissions Reductions in 2018
Comparison of State Demand Reduction Scenarios
50.0
45.0
40.0
Tons (000s) per Year
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
NC 2%
NC 4%
NC 6%
NC 8%
TN
TN 2%
KY
AL
TN 4%
FL
GA
TN 6%
MS
NC
TN 8%
SC
GA 2%
GA 4%
GA 6%
GA 8%
VA EGUs
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“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
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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
70.0
60.0
Tons (000s) per Year
50.0
40.0
30.0
20.0
10.0
0.0
NC+TN+GA 2%
NC+TN+GA 4%
NC+TN+GA 6%
NC+TN+GA 8%
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
Coordinated EE implementation improves NC-only results
by 35% from 43k tons to 58k tons
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Results – NOx Emission Reductions
Power Plant NOx Emissions Reductions in 2018
Comparison of State Demand Reduction Scenarios
18.0
16.0
14.0
Tons (000s) per Year
12.0
10.0
8.0
6.0
4.0
2.0
0.0
NC 2%
NC 4%
NC 6%
NC 8%
TN
TN 2%
KY
AL
TN 4%
FL
GA
TN 6%
MS
NC
TN 8%
SC
GA 2%
GA 4%
GA 6%
GA 8%
VA EGUs
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Results – 2018 Reductions at Current Costs
Avoided Emissions Control Costs in 2018 with Current Market Value of Allowance
70.0
10/3/06 Market Rate
SO2 Allowance: $545
NOx Allowance: $1,150
60.0
Total Avoided Cost ($M)
50.0
40.0
30.0
20.0
10.0
0.0
NC 8%
TN 8%
GA 8%
NC+TN+GA 8%
NOx reducts cost ($M)
18.5
7.2
6.9
27.5
SO2 reducts cost ($M)
23.5
13.7
1.9
31.7
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
450.0
400.0
Total Avoided Cost ($M)
350.0
Beyond 300k Annual Tons
SO2 Reduction: $5,000/ton*
NOx Allowance: $5,000/ton**
300.0
250.0
200.0
150.0
100.0
50.0
0.0
NC 8%
TN 8%
GA 8%
NC+TN+GA 8%
NOx reducts cost ($M)
80.5
31.5
29.8
119.7
SO2 reducts cost ($M)
215.3
126.1
17.3
290.4
*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
160.0
140.0
SO2 Reduction: $2,115/ton*
NOx Allowance: $3,000/ton**
Total Avoided Cost ($M)
120.0
100.0
80.0
60.0
40.0
20.0
0.0
NC 6%
TN 6%
GA 6%
NC+TN+GA 6%
NOx reducts cost ($M)
36.8
13.7
13.8
55.5
SO2 reducts cost ($M)
69.4
39.2
5.2
93.3
*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
NC 8%
Scenario
TN 8%
Scenario
GA 8%
Scenario
Joint
Action 8%
Scenario
SO2
Emission
Reductions
43.1
25.3
3.2
NOx
Emission
Reductions
16.0
6.3
5.9
Projected Avoided Costs for Joint
Action Scenario ($M)
Current Mkt
Prices
8% Scen
Future
High Case
8% Scen
58.2
$31.7
$291.0
$93.3
24.0
$27.6
$138.0
$55.5
$59.3
$429.0
$148.8
total costs avoided
Future
Interm
Case
6% Scen
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Conclusion: Linkage Between Energy Modeling and Air
Quality Modeling with AEB
Intersection is
Sensitivity Matrix (SM)
Energy Modeling
and Policy Development
SM
Air Quality Modeling
and Policy Development
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.
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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)
[email protected]
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