Role of Residential and Commercial Sectors in
Meeting California’s Climate Goal by 2050
Saleh Zakerinia, Chris Yang, Sonia Yeh
mzakerinia@ucdavis.edu
Institute of Transportation Studies
University of California, Davis
USAEE, Anchorage, AK, July 30 2013
Outline
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Background
Motivation
Research questions
Model description
Results and discussion
Future work
Background
• California is the leading state in the United States
to address climate change.
• In 2005: the executive order of S-3-05: Reducing
greenhouse gas (GHG) emissions to 80% below
the level of 1990 by 2050
• 2006: the Assembly Bill 32 (AB32): Reducing
GHG emissions back to the 1990 level by 2020
• Pressing need to fill the gap of planning for longterm goal
Motivation
• Why Residential and commercial sector?
California’s emission’s by sector
Source:(“California’s Greenhouse Gas Inventory Data- 2000 to 2010,” CARB)
Motivation
• Many projections of commercial and residential
based on fuel use trends at aggregate or
technology levels
• There is no bottom-up energy model (in California)
that projects behaviors of these sectors based on
endogenous technological change and economic
behaviors in response to policies and prices.
• All previous studies examine the mitigations of
residential and commercial sectors independent of
changes in other sectors.
Previous CA-TIMES Study
• Endogenously predict changes in transportation and
electric sector in response to deep GHG reduction
policies (80% reduction from 1990 level by 2050).
• Exogenously assume fuel use changes and fuel use
reductions of residential, commercial and industrial sectors
based on UCD work (BAU) and ETP analysis by the IEA
(deep GHG scenarios)
Source: McCollum, D, Yang, C., Yeh, S., & Ogden, J.
(2012). Deep greenhouse gas reduction scenarios for
California–Strategic implications from the CA-TIMES
energy-economic systems model. Energy Strategy
Reviews.
Research Questions
• What are the projected energy use, technology
adoption and GHG emissions for commercial and
residential sector to 2050?
• How these sectors may be affected by climate
policies? What would be the changes in
technology adoption, energy use, and GHG
emissions?
• What are the roles of efficiency improvement, fuel
switch, demand response to price changes and
electricity decarbonization in meeting 2050 goal?
The CA-TIMES Model
• Funded by the California Air Resources Board (2010 - 2012)
• CA-TIMES (The Integrated MARKAL-EFOM1 System)
model is an Energy–Economy–Environment (3E)
model for the California energy system.
• Model covers all sectors of the California energy system
(not Rest of World)
 Primary energy resource extraction,
imports/exports, electricity production, fuel
conversion (e.g., refineries), and the residential,
commercial, industrial, transportation, and
agricultural end-use sectors
The CA-TIMES Model
• Rich in “bottom-up” technological detail – describes in
detail technology operation, efficiency, availability, fuel
production/demand, retrofit, and retirement in flexible time slices.
 Hundreds to thousands of technologies and commodities
• Partial equilibrium model - assumes fully competitive
markets, perfect foresight and price elastic demands.
• Uses linear programming to minimize global cost of system
• Identifies most cost-effective pattern of resource use and
technology deployment over time
The CA-TIMES Model
Residential Sector
• Two housing types: single-family and multi-family
• 13 service demand
• Demand drivers: appliance saturation rate, housing size, and housing
heating/cooling coefficient
Fuel
Technology
Demand
Residential
Drivers
# of Households
(single vs multi- family)
Furnace
Housing size
Space heater
Wood stove
Space heating
Heat pump
Central/Room AC
Space cooling
Water Heater Furnace
Water heating
Incandescent/Fluorescent
Lighting
Cooking stove
Cooking
Refrigerator
Cloth washer
Refrigeration
Cloth washing
Cloth dryer
Cloth drying
Dish washer
Dish washing
Freezer
Freezers
Color TV
Color TV
Pool pumps
Misc Electric Appliance
Misc Other Appliance
Pool pump
Other uses
Housing heating/cooling
coefficient
Appliance saturation rate
Commercial Sector
• 9 service demand
• 12 building type
• Each commercial
building type has a
constant end-use
energy service
demand per unit of
floorspace
• Floorspace is
projected exogenously
to 2050 using
population growth
projection
Scenarios
Business as Usual Scenario
- Current
biofuel tax credits
- Current biofuel import tariffs
- Current transportation fuel taxes
- Electric vehicle subsidies
- Power plant electricity GHG standard
- Renewable fuel standard
- Renewable electricity production tax
- Credit, solar investment tax credit
- CAFE standards to 2016
Deep GHG Reduction
Scenario
(80% reduction from 1990
level by 2050)
BAU scenario plus
- Renewable portfolio standard
- Economy-wide GHG reduction targets
- CAFE standards to 2025
Service demands are fixed
and equal in both scenarios
Scenarios
• Elastic demand:
• Service demand is not fixed and it responds to
price changes
• For each service demand an elasticity number
defines.
Results
Effect of Elastic Demand
• We do not have any service demand change in
commercial sector with having elastic demand.
• 3% reduction in residential service demand
 Most of this reduction occurs in
Space cooling and space heating, they contribute to
40% and 16% of total reduction, respectively.
Final Energy by Fuel (Residential)
• 22% reduction in fuel consumption by 2050 in Deep
GHG relative to BAU
• Huge growth in solar consumption (8 times)
• LPG phases out
 Used for space heating in the BAU
Final Energy by Fuel (Residential)
• 35% reduction in fuel consumption relative to 2010
• 19% reduction in 2050 relative to Deep GHG and 38%
reduction relative to BAU
• Most reduction occurs in water heating (63% relative to
Deep GHG) following by refrigeration and space heating
Final Energy by End-use(Residential)
• Water heating and space cooling consume about 50% of
total consumption in 2050-BAU
• Efficiency increases significantly in these two service
demands-Deep GHG
Final Energy by Fuel (Commercial)
• 23% reduction in fuel consumption by 2050 in Deep GHG
relative to BAU
• 14% increase in natural gas consumption in BAU
• 13% reduction in natural gas consumption in Deep GHG
Final Energy by Fuel (Commercial)
• Almost no change in energy consumption in
Elastic demand
• Commercial sector demand is not sensitive to
price change.
Final Energy by End-use(Commercial)
• Lighting and space heating consume about 40% of total
consumption in BAU
• Efficiency increases significantly in these two service
demands-Deep GHG
Example of Technology Adoption
Efficiency
• Efficiency improvement is significant in Deep GHG
• Efficiency improves by 43% in residential sector and by
30% in commercial sector in 2050 relative to BAU
Emissions (Residential Sector)
• Electricity emissions shrinks in 2050 in Deep
GHG (8% relative to 64% in 2010)
• Electricity emissions contribute to 75% of
emissions in 2050 (BAU)
Emissions (Residential Sector)
• Elastic demand emissions is 10% less than Deep
GHG in 2050
• Deep GHG emissions in one forth of BAU emissions
Emissions (Commercial Sector)
• Electricity emissions reduction is significant in
Deep GHG
• Direct emissions increases by 14% in BAU- It
decreases by 25% in Deep GHG
Emissions (Commercial Sector)
• Elastic demand emissions is not very different from
Deep GHG
• BAU emissions is 3 times more than Deep GHG
Electricity Carbon Intensity
-Electricity’s role in meeting
2050 goal is crucial
-Almost zero carbon intensive
electricity in 2050!
Costs
-Investment cost is higher in Deep GHG
-Higher variable cost in BAU
-No significant change with elastic demand
Conclusions
• Fuel switching to near-zero carbon intensity
electricity is the single most important factor for
residential and commercial sectors contributing
to GHG mitigation target
• Sector-wide aggregated efficiency needs to
improve by 43% in residential sector and by 30%
in commercial sector in 2050 relative to BAU
(220% and 239% relative to 2010 for residential
and commercial sector, respectively)
• Water heating, space heating and space cooling
are the major end-uses that their efficiency
improvement plays a very important role
Future Work
• Adding demand side management for
incorporating smart grids for the future
• Improving the results for capturing more realistic
customer behavior (e.g. Doing sensitivity analysis
on hurdle rates)
• Developing other scenarios to measure the impact
of fuel price change
Thank you!
mzakerinia@ucdavis.edu
Supporting slides
Some slides for different end-uses like this
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