H2H2 Geoff Morrison Anthony Eggert Sonia Yeh Raphael Isaac Christina Zapata Webinar: Inter-Model...
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Transcript of H2H2 Geoff Morrison Anthony Eggert Sonia Yeh Raphael Isaac Christina Zapata Webinar: Inter-Model...
H2
Geoff MorrisonAnthony EggertSonia YehRaphael IsaacChristina Zapata
Webinar: Inter-Model Comparison of California Energy Models
27 February, 2014UC Davis
California’s Goals: Reach 1990 levels by 2020 and 80% reduction by 2050
?
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100
200
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2000 2010 2020 2030 2040 2050
MM
T C
O2
e/yr
431 MMT CO2e/yr
86 MMT COe/yr
MMT CO2e = Million metric tonnes of carbon dioxide equivalent
1990 Levels80% below 1990 Levels
2/21
Model Questions
• How will California’s energy system evolve to 2030 & 2050:– Greenhouse Gas (GHG) trajectories?– Fuel mix and technology mix?– Infrastructure build rate?– Air quality?
• What assumptions drive these results?
• What are common insights across models? Where do they diverge?
3/21
• Update to AB 32 Scoping Plan (2014):“A mid-term statewide emission limit will ensure that the State stays on course to meet our long-term goal and continues the success it has achieved thus far in reducing emissions.” (CARB, 2014, p. 39)
• Governor’s Environmental Goals and Policy Report (2013):“…the state needs a mid-term emission reduction target to provide a goalpost to guide near-term investment and policy development. A mid-term target will allow us to gauge current actions relative to our climate goals and serve to provide a clear sign of the state’s commitment to achieving long-term climate stabilization. This commitment will send a strong signal of support for the innovators and entrepreneurs to drive technology and development to tackle the challenge of climate change.” (OPR, 2014, p. 6)
Need for Mid-term GHG Target
4/21
Why Do Inter-Model Comparisons?
• Sweeney, 1983– Model comparisons benefit the modeling community “through identification of
errors, clarification of disagreements, and guidance for model selection”
• Weyant, 2012– Understand Strength/weaknesses of existing methodologies– Identify high priority areas for development of new data, analyses, and
modeling methodologies
• Two levels of model comparisons:– Level 1: compare & contrast inputs & outputs (e.g. review article)– Level 2: standardize inputs, compare outputs (SRES, SSPs)
5/21
Model Group (lead)ARB VISION California Air Resources Board (CARB)BEAR UC Berkeley (Roland-Holst)CA-TIMES UC Davis (Yang, Yeh)CCST View to 2050 CCST (Long)CCST (Bioenergy) CCST (Youngs)E-DRAM UCB/CARB (Berck)Energy 2020 ICF/CRAGHGIS LBNL (Greenblatt)IEPR 2013/CED 2013 California Energy Commission (CEC)LEAP-SWITCH UC Berkeley/LBNL (Nelson, Wei)MRN-NEEM EPRI/CARBPATHWAYS E3/LBNL (Williams)Wind Water Solar (WWS) Stanford/UCD (Jacobson, Delucchi)
CA Energy Models/Reports Reviewed
6/21
Population Assumptions
BEAR – DOF (2013)
CA 2050 - U.S. Census (2005)
CA-TIMES - DOF (2013)
E-DRAM - DOF (2003)
Energy 2020 - IEPR (2009)
GHGIS - DOF (2013)
IEPR 2013 - IHS Global Insight for Mid projection LEAP-SWITCH - AEO (2011)
VISION - AEO (2011)
WWS - U.S. Census (2009)
8/21
25
30
35
40
45
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55
60
1990 2000 2010 2020 2030 2040 2050
Po
pu
lati
on
(M
il)
Wei et al., 2013
WWSIEPR 2013, mid
ICF/SSI, 2010
Berck et al., 2008
Roland-Holst, 2012Greenblatt, 2013
Nelson/Wei et al., 2013Yang et al., 2014
Williams et al., 2012 50.4
59.5
56.6
Business As Usual (BAU) Scenarios
9/21
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2000 2010 2020 2030 2040 2050
MM
T C
O2e
/yr
Yang et al., 2014
Williams et al., 2012
ARB Scoping Plan, 2008
Roland-Holst, 2012ARB Scoping Plan, 2014
Long et al., 2011
Nelson/Wei et al., 2013
80 in '50AB32 Target
Historic
Reaching 80 in ‘50 Goals
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100
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400
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600
700
800
900
1,000
2010 2020 2030 2040 2050
MM
T C
O2e
/yr
Linear Reduction to 80%
Constant Rate to 80%
Pathways (Hi Nuke)
Pathways (Hi renew)
CA TIMES (Line)
CA TIMES (CCS-C)
GHGIS (Case 2)
GHGIS (Case 3)
LEAP-SWITCH (Base)
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800
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1,000
2010 2020 2030 2040 2050
MM
T C
O2e
/yr
Linear Reduction to 80%
Constant Rate to 80%
10/21
Reaching 80 in ‘50 Goals
11/21
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400
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600
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900
1,000
2010 2020 2030 2040 2050
MM
T C
O2
e/yr
Linear Reduction to 80% Constant Rate to 80%
Williams et al., 2012 (Nuke) Williams et al., 2012 (Hi Renew)
Yang et al., 2014 (Line) Yang et al., 2014 (CCS)
GHGIS (Case 2) Greenblatt, 2013 (Case 3)
Nelson/Wei et al., 2013 (Base) Nelson/Wei et al., 2013 (-40% BioCCS)
Annual vs. Cumulative Emissions?
12/21
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2010 2020 2030 2040 2050
MM
T CO
2
Linear Reduction to 80% Constant Rate to 80%Williams et al., 2012 (Nuke) Williams et al., 2012 (Hi Renew)Yang et al., 2014 (Line) Yang et al., 2014 (CCS)GHGIS (Case 2) Greenblatt, 2013 (Case 3)Nelson/Wei et al., 2013 (Base) Nelson/Wei et al., 2013 (-40% BioCCS)
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50
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500
2010 2020 2030 2040 2050
MM
T CO
2/yr
Annual Cumulative
Annual vs. Cumulative Emissions?
0
100
200
300
400
500
2010 2020 2030 2040 2050
MM
T C
O2e
/yr
291
284
175
396
208
84
187
431
456
316
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2010 2020 2030 2040 2050M
MT
CO
2e
12,528
5,1498,473
10,3579,205
4,070
6,492
14,394
8,578
Annual Emissions Cumulative Emissions
8-52% Reduction
Large difference in Climate Impacts!
13/21
Light-Duty Vehicle Energy Use, 2030 & 2050
• In deep reduction scenarios, electricity and hydrogen provide 3-13% of Light Duty Vehicle (LDV) fuel in 2030 and 57-87% by 2050
• Total transportation energy drops by as much as 70% from 2010-2050 due to increased efficiency. • Vehicle Miles Traveled (VMT) assumptions range from 275 billion miles to 695 billion miles• Models differ dramatically in total energy use for LDVs and total transportation in 2050
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1000
1500
2000
2500
3000
3500
4000
LD
V E
ne
rgy
(P
J)
Hydrogen
Electric
Liquid Fuels
All Transport
2010 2030 2050 2030 2050 2030 2050 2030 2050 2030 2050 2030 2050 2050 2050
VISION VISION CA-TIMES CA-TIMES LEAP-SWITCH CCST PATHWAYS WWS(Case 3) (Case 2) (Hi Bio) (GHG-M) (Agg. Elect) (PEV+H2) (Mitigation)
LEGENDBars = LDV
energy use by source
Red triangles = total transport energy use
14/21
Electricity Generation and Renewable Fraction in 2030 & 2050
LEGENDBox plot = quartiles
(box) and max/mins (whiskers) across mitigation scenarios in given year
Red squares = individual scenarios
Percentages above boxes are percent renewable (non-hydro) across mitigation scenarios
• Renewable fraction (non-hydro) ranges from 30-51% in 2030 and 36-96% in 2050 (non-WWS)• Total generation goes from 306 TWh in 2013 to 290-990 in 2030 and 245-1380 in 2050• Implied renewable build rate is 0.2-4.2 Gigawatts per year (GW/yr) between today and 2030
and 1.5-10.4 GW/yr between 2030-2050 15/21
250
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550
650
750
2030 2050 2030 2050 2030 2050 GHGIS WWS CCST
Elec
tric
ity
Gen
erat
ion
(TW
h/yr
)
2013 2030 2050 2030 2050 2030 2050 2030 2050 2030 2050 2050 LEAP-SWITCH CA-TIMES PATHWAYS GHGIS WWS CCST
(Case 3)
20%
30-45%
38-74%
42-94%
38-55%
33-39%
38-81%
80% 100%
990 1380
51%
81%
36%
Liquid Biofuels are Important but Assumptions Matter!
• “Advanced” bio-liquids could power up to ~40% of transportation sector in 2050• Bioenergy goes to transportation, not to electricity• Large carbon savings from bioenergy+CCS (more modeling needed!)
16/21
Delivered Bioenergy in 2050
0 3 6 9 12 15
ARB, 2013; VISION
Yang et al., 2013, CA-TIMES
Long et al., 2011; CCST (Low)
Long et al., 2011; CCST (Hi)
Youngs, 2013; CCST-Bio (Base)
Youngs, 2013; CCST-Bio (Hi)
Greenblatt, 2013; GHGIS (Case 2)
Greenblatt, 2013; GHGIS (Case 3)
Neslon/Wei et al., 2013; LEAP-SWITCH
Williams et al., 2012; PATHWAYS
Billion Gallons Gasoline Equivalent (BGGE)
Unspecified
In-state (unspecified)
Out-of-state (unspecified)
Generic "energy crops"
In-state residues
Conventional
Herbaceous Energy Crops
Forest Residue
Landfill
Tallow/Grease
Ag Residue
Criteria Emissions
• Coordination needed between 2032 criteria emission goals and 2030/2050 climate goals• Including detailed criteria and GHG emissions in a single model can be very difficult• WWS estimates that a 100% renewable energy system would eliminate approximately
16,000 state air pollution deaths per year and avoid $131 billion per year in health care costs.
17/21
Observations
• Models built to examine pathways to 2050 not specifically focused on maximizing climate benefits by 2030 (except GHGIS)
• Many models lack economic indicators to consider economic feedback and benefits/costs of policy options
• Poor representation of uncertainty (version 2 of E3 model improves on this)• Criteria emissions not part of the optimization process• Modelers need to work with policy makers more closely to represent the details of
the policy design• Data availability and data/model transparency is absolutely essential.
18/21
Key Takeaways
• Annual emissions in deep reduction scenarios (i.e. 80 in 50): – 208-396 MMT CO2e/yr in 2030– 8-52% reduction by 2030 from 1990 levels– Cumulative emissions vary by as much as 40% in 2050– 30-50% renewable grid by 2030– 38-94% renewable grid by 2050
• Electrification of end uses and expansion of grid are key– Need to expand grid by 1.5-2.5 times its current capacity
• Need greater understanding about how to utilize biomass for energy and fuel– More modeling of bioenergy+CCS– More modeling of life cycle emissions and other sustainability factors
• Better long-term modeling of policies and technologies addressing non-energy related GHG emissions– BAU scenarios have non-energy GHG emissions >2050 target
• Coordination is key!
19/21