CA-TIMES Greenhouse Gas Reduction

Scenarios for California in 2030

Christopher Yang STEPS Symposium

December 10, 2015

www.steps.ucdavis.edu

NextSTEPS (Sustainable Transportation Energy Pathways)

Research Team and Talk Overview

• Research Team

– Saleh Zakerinia

– Kalai Ramea

– Alan Jenn, Ph.D

– Prof. David Bunch

– Sonia Yeh, Ph.D

• CA-TIMES description

• New policies

• Scenario results

What is CA-TIMES?

• An integrated model of California’s energy system that tracks energy

flows, economic costs and environmental emissions

– Projections of energy service demands to 2050

– Energy demand sectors (transportation, buildings, industry)

– Energy supply sectors (resource extraction, fuel production, electricity

generation)

– Integration of demand and supply sectors

• It is used to design the state’s future energy systems to meet the

demand for energy services

– Outputs include technology investments, and operational decisions

– Constrain these decisions based upon policy considerations

• Emissions, technology or sector specific policies

– Key metrics:

• End-use and supply technology mix, technology costs, resource utilization,

emissions, mitigation costs, etc. . .

CA-TIMES Energy Sectors

• Value of integrated model of California’s energy system

– Resource supplies – used across many sectors

– Electricity – used across many sectors, timing

– Understanding tradeoffs and minimizing mitigation costs

– Economy wide policies

End use demand

Fuel demand

Residential technologies

Commercial technologies

Transportation VMT

Energy Carriers

Energy Resources

Electricity

Fuels

Transportation technologies

Fuel and Electricity Production

Electric Power Plants

Petroleum Refineries

H2 Production Plants

Renewables

Fossil Fuels

Nuclear

Biomass

Synfuel Plants

Bio-Refineries

Legend

Exogenously specified resource

(supply curve)

Model Determined

Technology Mix

Model Determined

Commodity Mix

Exogenously specified demand

Residential energy

services

Commercial energy

services

Industrial demand by

fuel

Agricultural demand by

fuel

Out-of-State Energy

Resources

Interstate/International Aviation and

Marine

Understanding modeling approach and key caveats

• Linear economic optimization (minimize system costs) to 2050

• Model requires specification of all service demands, technology and

resource options to 2050

– Quantity, availability, cost, efficiency, lifetime

• Optimization and adoption of technologies is based primarily on a

life-cycle economic analysis of various alternatives

• Policies are modeled as system constraints

• Markets are not represented

– Decisions are made on providing fuels/electricity/services at lowest cost

• Single, global decision-maker with perfect foresight

– Tradeoffs are made across sectors

Model scenarios and results are illustrative of important trends

and can highlight insights into which technologies and options

could be used to mitigate GHG emissions

California Policy Targets

• 2005-2006 – Gov. Schwarzenegger sets 80% reduction target for

2050 and AB32 passed (2020 target)

– Other important policies include Pavley/CAFE, RPS, LCFS, ZEV

• Recent Policy Targets for 2030 (2015)

– Gov. Brown: 40% below 1990 levels

– 50% petroleum reduction target

– 50% renewable portfolio standard (RPS)

– Doubling of building efficiency

• Focus of modeling is to understand how California can achieve long-

term GHG reduction targets and implications of near-term policies

on technology adoption and costs

• Modeling work is still ongoing so results shown are not finalized or

published yet and are meant to be illustrative

Scenario Outputs

•

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Milli

on

VM

T

All Light-Duty Car and Truck Activity (Million VMT)

Fuel Cell Vehicle (FCV)

Battery Electric (BEV)

Plug-in Hybrid-Electric

Vehicle (PHEV)

NG/LPG vehicle

Hybrid-Electric Vehicle

(HEV)

Internal Combustion

Engine (ICE)

0

100

200

300

400

500

2010 2015 2020 2025 2030 2035 2040 2045 2050

GHGEmissions(M

tCO2-eq)

CA-Combus onIncludedGHGEmissionsbySector(SupplyandElectricityEmissionsAllocatedtoEnd-UseSectors)

Transporta on

Industrial

Residen al

Commercial

Agriculture

TOTAL

GHG Emissions

LDV Technology Mix

ICE

BEV

FCV

PHEV

-

100,000

200,000

300,000

400,000

500,000

600,000

700,000

2010 2015 2020 2025 2030 2035 2040 2045 2050

An

nu

al E

lec

tric

ity

Ge

ne

rati

on

(G

Wh

) Other

Oil

NG w/CCS

NG

Wind

Solar

Ren

Hydro

Tidal

Bio CCS

Bio

Geo

Coal

Nuclear

Electricity Generation Mix

Natural Gas

Wind

Solar

Hydro

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Peta

jou

les (

PJ)

Transportation Fuels Consumption by Fuel Type Bio-derived Avia. Gas.

Bio-derived Jet Fuel

Bio-derived RFO

Hydrogen

Electricity

Biodiesel

Bio-derived Gasoline

Ethanol

Aviation Gasoline

Natural Gas

Jet Fuel

Residual Fuel Oil

LPG

Diesel

Gasoline

Gasoline

Diesel

Jet

NG

H2 Elec BioDsl

Biogasoline

Ethanol

BioJet

Transportation Fuels

40% Reduction in GHG emissions by 2030

-

50

100

150

200

250

300

350

400

450

2010 2030 2050 2030 2050 2030 2050 2030 2050

BAU 2030 Cap Only Linear to 2050 2030+2050

GH

G e

mis

sio

ns

(M

MT

CO

2e

)

Transportation Supply Residential Industrial Electricity Commercial Agriculture

CA Energy System Emissions (MtCO2e) % below 1990

1990 390

2030 Target 234 40%

2030 BAU 327 16%

2050 BAU 305 22%

Linear trajectory (2020-2050) in 2030 287 26%

Trans

Elec

Sup

Res Ind

Com -

50

100

150

200

250

300

350

400

450

2010 2030 2050 2030 2050 2030 2050 2030 2050

BAU 2030 Cap Only Linear to 2050 2030+2050

GH

G e

mis

sio

ns

(M

MT

CO

2e

)

Transportation Supply Residential Industrial Electricity Commercial Agriculture

Electricity sector is the lowest cost

option for meeting the 2030 target

2030 GHG Target

• Significant change in cumulative emissions vs linear reduction

– 10.2 vs 10.9 GtCO2 is 700 MMTCo2 or ~2x current annual emissions

– vs 13.8 GtCO2 in BAU (2010-2050)

• Electricity sector contributes significantly to meeting 2030 goal

– Significant increase in wind and solar by 2030 vs BAU (60% vs 34%)

and double the renewable generation

– Carbon intensity of electricity is 44 g/kWh vs 206 in BAU

• Transportation sector also contributes significant reductions

– Primarily through biofuels usage (2.5 billion gge additional biofuels)

– Small changes in H2 and electricity use

• Increases mitigation costs

2050 Linear

2030 and 2050

Cap

Discounted Incremental cost vs BAU ($Billion)

cumulative to 2030 19.5 35

cumulative to 2050 323 382

Cost/HH/yr ($/yr)

cumulative to 2030 87 155

cumulative to 2050 574 678

Petroleum Reduction Target

• Petroleum reduction target (2030) is binding without 2030 cap

– The target is met primarily via biofuels (additional 2.3 billion gge)

– Natural gas contributes as well (~1 billion gge)

– Electricity and hydrogen do not play a large additional role in 2030

– Petroleum target induces additional 20 MtCO2 emissions reduction in

2030

– Reduces cumulative emissions to 2050 by 162 MtCO2 vs BAU

– $8B total or $35/HH/yr cumulative cost to 2030 (PV)

• Combined with GHG emissions caps

– With 2030 GHG target, petroleum reduction target is not binding

– No change between these model runs with and without petroleum target

– With relaxed 2030 target (linear 26% reduction), we see 1.5 billion gge

additional biofuels and 0.6 billion gge additional natural gas usage

Main Takeaways

• The CA-TIMES model is used to analyze future scenarios of

decarbonization and the impacts of broader policy targets,

technology/resource costs and constraints

• Provides insights into the broader context for the transportation

sector as well as tradeoffs and synergies in mitigation options

across sectors

• 40% 2030 GHG target is met through decarbonization in the

electricity sector and biofuels in transportation sector

– 40% in 2030 does not appear to be necessary to achieve the 2050 goal,

nor the lowest cost trajectory, but will lower cumulative emissions

– Costs are reasonable (PV $382B or ~$700/HH/yr to 2050)

• Impact of petroleum reduction target

– Binding without 40% GHG target in 2030 (BAU or Linear GHG cap)

– Mostly met through biofuels and natural gas rather than significant

increases in hydrogen or electricity

Ongoing work to be completed in 2016

• Incorporating learning-by-doing

• Understanding the interactions between different policies and

targets

• GHG reductions from increasing stringency of existing policies

(RPS, CAFE, ZEV, LCFS, Petroleum, etc) vs emissions caps

• Incorporation of consumer heterogeneity and choice into the model

• Parameter uncertainty for model inputs

• More detailed analysis of interactions between electric sector and

transportation fuels production

Thank you!

ccyang@ucdavis.edu