CA-TIMES Greenhouse Gas Reduction Scenarios for California in … · 2016-10-12 · CA-TIMES...
Transcript of CA-TIMES Greenhouse Gas Reduction Scenarios for California in … · 2016-10-12 · CA-TIMES...
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!