H2

Geoff MorrisonAnthony EggertSonia YehRaphael IsaacChristina Zapata

Webinar: Inter-Model Comparison of

California Energy Models

27 February, 2014

UC Davis

California’s Goals:

Reach 1990 levels by 2020 and 80% reduction by 2050

?

0

100

200

300

400

500

600

700

800

900

1000

2000 2010 2020 2030 2040 2050

MM

T C

O2

e/y

r

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?

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• 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)

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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/CRA

GHGIS LBNL (Greenblatt)

IEPR 2013/CED 2013 California Energy Commission (CEC)

LEAP-SWITCH UC Berkeley/LBNL (Nelson, Wei)

MRN-NEEM EPRI/CARB

PATHWAYS E3/LBNL (Williams)

Wind Water Solar (WWS) Stanford/UCD (Jacobson, Delucchi)

CA Energy Models/Reports Reviewed

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Qualitative Comparison

Yes/Represented

Limited

None/Not represented

7/21

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)

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25

30

35

40

45

50

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

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0

100

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300

400

500

600

700

800

900

1000

2000 2010 2020 2030 2040 2050

MM

T C

O2

e/

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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200

300

400

500

600

700

800

900

1,000

2010 2020 2030 2040 2050

MM

T C

O2

e/

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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200

300

400

500

600

700

800

900

1,000

2010 2020 2030 2040 2050

MM

T C

O2

e/

yr

Linear Reduction to 80%

Constant Rate to 80%

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Reaching 80 in ‘50 Goals

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-

100

200

300

400

500

600

700

800

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?

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0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2010 2020 2030 2040 2050

MM

T C

O2

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

100

150

200

250

300

350

400

450

500

2010 2020 2030 2040 2050

MM

T C

O2

/y

r

Annual Cumulative

Annual vs. Cumulative Emissions?

0

100

200

300

400

500

2010 2020 2030 2040 2050

MM

T C

O2

e/

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!

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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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1500

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2500

3000

3500

4000

LDV

En

ergy

(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)

LEGEND

Bars = LDV energy use by source

Red triangles = total transport energy use

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Electricity Generation and Renewable Fraction in 2030 & 2050

LEGEND

Box 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

350

450

550

650

750

2030 2050 2030 2050 2030 2050 GHGIS WWS CCST

Ele

ctri

city

Ge

ne

rati

on

(T

Wh

/y

r)

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!)

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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.

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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

Thank you!

Please see our CCPM summary document and forthcoming white paper here: http://policyinstitute.ucdavis.edu/initiatives/ccpm/

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Extra Slides

21/21

• US (Copenhagen Accord) – 33% below 1990 levels by 2030

• Euro Union – 40% below 1990 levels by 2030 (under negotiation)

• Denmark – 40% below 1990 levels by 2030

• Netherlands, UK – 50% between 2022-2027

• Germany – 55% below 1990 by 2030

• Scotland – 42% below 1990/5 levels by 2020 and 80% by 2050

– Expects to make 60% reduction target in 2030

Emission targets in developed world

Coordination is Key in Meeting 2030 and 2050 Goals

• Between state agencies and with other state goals:

– Air quality targets for San Joaquin and South Coast regions

– Water use/quality

– Health goals

• Between Western states:

– WECC targets need to be aligned to avoid leakage, expand market for low-carbon technology, provide least cost mitigation measures

• Between modelers and policy makers

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Income Assumptions

CA 1980-2010 - Personal

income (BEA, 2013)

CA 1997-2010 - GDP

(BEA, 2013)

CA 2010-2015 – Personal

income (DOF, 2013)

E-DRAM - Personal income

(DOF, 2003)

CA 2050 - GDP

(BEA, 2009) + Regressions

Energy 2020 - Personal income

(IEPR, 2009)

BEAR – Per capita GDP

(AEO, 2011)

GHGIS - Personal income

(from VISION/IEPR, 2013)

VISION - Personal income

(AEO, 2011)

IEPR 2013 - Personal income

(IHS 2013; Moody's, 2011)

*Some models adjusted from nominal to real growth rates.

** Some models use personal income while other use GDP

H2

Biomass in CA-TIMES

Feb 2014

Biomass Supply

• Biomass supply is represented as a series of location-independent supply curves for a number of different biomass resources. This data is from Parker (2010)

0

5

10

15

20

25

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Bio

ma

ss

Co

st

($/G

J)

Cumulative Biomass Supply (PJ)

Forest Residue MSW Pulp Ag residue Energy Crops Orchard waste Yellow Grease Tallow Corn All BIOMASS

Biomass Supply

• Biomass supply is split between in-state resources and a portion of biomass in the Western region

– 30% of Western region biomass, CA is ~30% of western US pop

• Also assume annual supply increase of 1% per year

– 2050 supply is 28% greater than 2025 supply

Biomass Supply

• Majority of biomass comes from out-of-state western region

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1,200

1,400

1,600

1,800

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2010 2012 2015 2017 2020 2025 2030 2035 2040 2045 2050 2055

Bio

mas

s S

up

ply

(P

J)

BIOENCRUS

BIOAGRRUS

BIOPULPRUS

BIOOVWRUS

BIOMSWYRUS

BIOMSWWRUS

BIOMSWPRUS

BIOMSWMRUS

BIOFORRUS

BIOCRNCA

BIOTALCA

BIOYGRCA

BIOENCCA

BIOAGRCA

BIOPULPCA

BIOOVWCA

BIOMSWYCA

BIOMSWWCA

BIOMSWPCA

OutofstateBiomass

InstateBiomass

Biomass Supply representation

• Biomass is split into several categories

1. Woody (Forest, woody MSW, Orchard/Vineyard, Pulp)

2. Herbaceous (Agr. Residues, Energy crops)

3. MSW (Paper, Yard and Mixed)

4. Yellow grease

5. Tallow

• Conversion technologies

– Biochemical cellulosic ethanol production (1,2,3)

– Thermochemical cellulosic ethanol production (1,2,3)

– Renewable diesel production (4,5)

– Pyrolysis bio-oil production (1,2,3)

– FT conversion – drop in fuels (1,2,3) with or without CCS

Some general results

• In our scenarios, oil price is based on AEO Reference case, and rises to $250/bbl in 2050

– At these prices biomass is fully utilized to make biofuels for transport, even in absence of carbon constraint.

• In GHG scenarios, bio-CCS is used in all cases where the technology is available

– bioCCS from FT conversion of biomass to liquid biofuels can displace emissions from 2 gallons of petroleum fuel per gallon of biofuel produced

• All biomass is used in the production of liquid transportation fuels by 2050

– After 2030, no biomass is used for electricity production. Relative value of biomass for emissions reduction is higher for transportation (can displace petroleum in transport, displace NGCC in electricity). Even greater value if CCS is available.

GHG-STEP scenario

• Most biofuels are drop in fuels (jet, diesel and gasoline) with some cellulosic ethanol as well

Transportation Fuel

• CA-TIMES Transportation fuels in 2050 –GHG-STEP

– Lots of aviation and marine fuels (i.e. liquid fuels)

– Even with GHG constraints, lots of petroleum fuels

On-Roadgasoline+Subs tutes

16%

On-Roaddiesel+subsi tutes

21%

NG5%Avia onfuel

28%

MarineFuels16%

Electricity8%

H26%

Biofuels34%

PetroleumFuels46%

Other20%

Future work

• Updates to 2010 Biomass supply curves

• Revisit costs, performance of biofuel conversion facilities

Factors that Influence Model Outputs

Source: D. Manley, 2013

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