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Plug- in hybrid vehicle GHG impacts in California: Integrating consumer-informed
recharge profiles with an electricity- dispatch model
Jonn Axsen a*, Kenneth S. Kurani a , Ryan McCarthy a and Christopher Yang a
a Institute of Transportation Studies, University of California at Davis, 2028
Academic Surge, One Shields Avenue, Davis, CA, 95616, U. S. A.
* Corresponding author. Tel.: 1 530 574 2150, Fax: 1 530 752 6572, E- mail address:
jaxsen@ ucdavis. edu
Abstract: Estimating greenhouse gas ( GHG) emissions of plug- in hybrid vehicles
( PHEVs) is challenging because PHEVs are powered by gasoline and grid
electricity— in a variety of proportions across individual consumers. Previous GHG
estimates emissions postulate consumer behavior and simplify interactions with the
electricity grid. We construct PHEV emissions scenarios to address inherent
relationships between vehicle design, driving and recharging behaviors, seasonal and
time- of- day variation in GHG- intensity of electricity, and total GHG emissions. From
a survey of 877 California new vehicle buyers we elicit driving patterns, time of day
recharge access, and PHEV design interests. The elicited data differ substantially
from those used in previous analyses— including substantial interest in PHEVs with
no true all- electric driving. We construct electricity demand profiles scaled to one
million PHEVs and input them into an hourly California electricity supply model to
simulate GHG emissions scenarios. Compared to conventional vehicles, consumer-designed
PHEVs cut marginal ( incremental) GHG emissions by more than one third
in current California energy scenarios and by a quarter in future energy scenarios—
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reductions similar to those simulated for all- electric PHEV designs. Across the
emissions scenarios realization of long- term GHG reductions depends on reducing the
carbon intensity of the grid.
1. Background
This paper explores the conditions under which plug- in hybrid vehicles
( PHEVs) may reduce greenhouse gas ( GHG) emissions from the light- duty
transportation sector in California. The two primary advances of this analysis are its
incorporation of 1) explicit measures of consumer interest in and potential use of
different types of PHEVs and 2) a model of the California electricity grid capable of
differentiating hourly and seasonal GHG emissions by generation source.
By combining a heat engine powered by gasoline and an electric motor
powered at least in part by electricity from the electric grid, PHEVs both directly
displace gasoline with electricity and reduce gasoline use through the efficiency gains
of a hybrid powertrain. Vehicle electrification improves total energy efficiency of the
vehicle ( MJ/ mile) and may allow society to more easily lower the carbon intensity of
the energy used in vehicles ( gCO2/ MJ) over time. Policymakers are increasingly
turning attention to PHEVs to meet transportation environmental and energy goals
( Service, 2009). For instance, President Obama set a national target of 1 million
PHEVs on the road by 2015 ( Revkin, 2008), and as of the beginning of 2009, a
federal tax credit is offered for the first 250,000 PHEVs sold ( U. S. Congress, 2009).
However, determining the environmental and societal impacts of PHEVs is complex
and the benefits are uncertain; they are new technology with a wide diversity of
possible designs, driving and recharge patterns, and electricity sources.
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A PHEV operates in one of two modes: charge depleting ( CD) or charge
sustaining ( CS) mode ( Fig. 1). During CD mode, driving the PHEV depletes the
battery’s state of charge ( SOC), and CD range is the distance a fully charged PHEV
can be driven before depleting its battery and switching to CS mode. Over its CD
range a PHEV can be designed for either all- electric operation ( AE), i. e., using only
electricity from the battery, or for blended ( B) operation, i. e., using both electricity
and gasoline in almost any proportions. We identify CD range and operation with the
following notation: AE- X or B- X, where X is the CD range in miles ( where 1 mile =
1.61 km). Fig. 1 depicts the battery discharge pattern of a hypothetical AE- X ( top
graph) and B- X ( bottom graph) measured as SOC on the left axis. Holding CD range
constant, an AE- X design requires more battery energy and power capacity and is thus
costlier than a B- X design ( for the same X). Further, at any distance cumulative
gasoline use ( on the right axis) will be higher in the B- X design for any vehicle trips
that include a portion of CD driving. In all PHEVs, CS mode relies solely on gasoline
energy as with a conventional hybrid- electric vehicle ( HEV); the gasoline energy
maintains battery state of charge— but the vehicle does not use grid electricity until
recharged. See Axsen et al. ( 2008) for a more complete description of PHEV
operation and battery considerations.
In this paper we analyze potential PHEV GHG impacts in California. We first
review the assumptions of previous estimates, which ignore or oversimplify the
complexity and diversity of plausible PHEV consumer interests and behaviors and
PHEVs’ interactions with the grid. We address behavioral complexity using survey
responses from 877 new vehicle buying households in California collected in
December 2007 ( Axsen and Kurani, 2010). To improve the representation of
electricity supply, we employ a dispatch model of the electrical grid in California
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( McCarthy and Yang, 2010). Our results indicate how PHEVs may reduce GHGs
across a diverse set of buyer interests, driving patterns and recharge access. We do not
present a full lifecycle analysis— we account for “ source- to- wheel” GHG emissions
associated with PHEV fuel use, but do not consider the GHG implications of vehicle
manufacturing or disposal.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Distance
Battery State of Charge ( SOC)
Cumulative Grid Electricity or Gasoline Use
Electricity
Gasoline
SOC
Charge Depleting
( CD) mode - AE
Charge Sustaining
( CS) mode
All Electric ( AE) CD Operation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Distance
Battery State of Charge ( SOC)
Cumulative Grid Electricity or Gasoline Use
Electricity
SOC Gasoline
Charge Depleting
( CD) mode - B
Charge Sustaining
( CS) mode
Blended ( B) CD Operation
Fig. 1 Illustration of the discharge pattern of a PHEV battery (~ 65% depth of
discharge, adapted from ( Kromer and Heywood, 2007)).
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2. Literature review
Previous studies of PHEV GHG impacts ( Duvall et al., 2007; Hadley and
Tsvetkova, 2008; NAS, 2009; Samaras and Meisterling, 2008; Silva et al., 2009;
Stephan and Sullivan, 2008) and other energy impacts ( Kang and Recker, 2009;
Lemoine et al., 2008; Sioshansi and Denholm, 2009) utilize a wide array of input
assumptions, which we place into five categories in Table A1. First are the baseline
vehicles that are compared with PHEVs: previous analyses typically assume internal
combustion engine vehicles ( ICEVs) or HEVs, or both, and baseline fuel economies
can be based on past, present or future models. Second, assumptions also vary by
PHEV design, where most studies assume some variant of an AE- X and neglect the
potential for B- X. Third, assumptions of driving behavior have been based on
disaggregated details drawn from travel or activity diaries, an aggregated metric
calculated from such diaries ( e. g. a utility factor), or an assumption that all vehicles
are driven the same distance daily. Fourth, recharge behavior assumptions have been
based on travel or activity diary data indicating when drivers are parked, as a block of
time where all vehicles recharge concurrently, or following some defined off- peak
distribution— or in some cases time of day recharging is not addressed. Fifth, prior
PHEV GHG emissions estimates also vary as to how the electricity to recharge the
vehicle is generated. A more sophisticated approach uses some form of dispatch
model, representing the various power plants that are used for different demand loads
on a daily and seasonal basis. Other studies use representations of previous demand
patterns or forecasts. Simpler estimates apply an annual average rate of carbon
intensity for all electricity demanded— not accounting for interactions between
vehicle use and hourly, seasonal, or regional variations in electricity generation.
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To illustrate the effects of various combinations of these assumptions, Fig. 2
depicts GHG reductions estimated for PHEVs by three of the most influential U. S.-
based studies ( Duvall et al., 2007; Samaras and Meisterling, 2008; Stephan and
Sullivan, 2008). All three conclude that PHEVs can reduce GHG emissions relative to
ICEVs, though estimated reductions range from 15 to 65 percent. Fig. 2 depicts how
assumptions differ by PHEV design ( AE- 10, 19, 20, 38, 40, or 56), the carbon
intensity of electricity used in the vehicles ( 200 to 1100 gCO2/ kWh), and the selected
baseline ( ICEV or HEV). The Electric Power Research Institute’s ( EPRI) ( Duvall et
al., 2007) findings of relatively optimistic GHG reductions from PHEVs result largely
from assumptions of a low- carbon electricity grid in the year 2050, with greater
reductions from longer CD ranges. Samaras and Meisterling ( S& M) ( 2008) consider a
wider range of electricity carbon intensities, finding that at higher intensities, PHEVs
with shorter CD ranges may reduce more GHG emissions than PHEVs with longer
CD ranges recharged in higher carbon- intensive grids. Stephen and Sullivan ( S& S)
( 2008) find greater GHG reductions at higher carbon intensities of electricity
production, largely because they assume a less efficient ICEV baseline ( 19 MPG) and
100 percent CD driving for PHEVs, that is, they model AE- 40s that never deplete
their batteries and thus never use gasoline.
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0%
10%
20%
30%
40%
50%
60%
70%
0 200 400 600 800 1000
Decrease CO2 from ICEV (%)
S& M AE- 19 S& M AE- 38 S& M AE- 56
EPRI AE- 10 EPRI AE- 20 EPRI AE- 40
S& S AE- 40
- 30%
- 20%
- 10%
0%
10%
20%
30%
40%
50%
60%
70%
0 200 400 600 800 1000
Carbon Intensity of Electricity Used in PHEVs ( gCO2/ kWh)
Decrease CO2 from HEV (%)
A
B
Fig. 2. Comparing CO2 emissions results of previous studies according to electricity
carbon intensity ( A: reductions relative to CVs; B: reductions relative to HEVs)
On the demand side, the present study departs from previous research efforts
by eliciting distributions of PHEV designs, driving behavior and recharge potential
directly from plausible PHEV buyers— that is, collecting all three types of data from
the same buyer. On the supply side, we meet the modeled demand for electricity with
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a dispatch model of the California electrical grid capable of differentiated GHG
estimations.
3. Representing the demand- side: A survey of California new car buyers
We used a consumer survey to consult car buyers about their potential interest
in, design of, and use of PHEVs. With this data we constructed consumer- informed
recharge profiles, that is, representations of time of day electricity demand from the
PHEVs they designed. Conceptually, an aggregate vehicle recharge profile is the
product of four data components or assumptions in the absence of data: 1) type or
distribution of PHEV design( s), 2) PHEV driving patterns, 3) recharge behaviors of
PHEV owners— when they plug in, where, for how long and how often, and 4) market
penetration of PHEVs. The first three components determine unique energy use and
GHG emissions profiles for each driver/ vehicle. We use the survey data to inform the
first three, but leave the fourth to future studies. Here, for simplicity and to ease
comparison, we assume a market of one million PHEVs (~ 3.6 percent of California’s
light- duty vehicles) in every scenario we construct ( scaling up from the plausible
PHEV buyers identified in our survey respondents).
Survey data were collected from a sub- sample of 877 California new vehicle
buyers in December, 2007. The full survey included a representative sample of over
2,200 U. S. new vehicle buyers, with nationwide results reported elsewhere ( Axsen
and Kurani, 2009). We deem the weighted California sub- sample to be generally
representative of California new car buyers ( Axsen and Kurani, 2010). Respondents
completed a sequential multi- part questionnaire over the course of several days,
including a 24- hour diary of driving and vehicle recharging potential by time of day
and parking location as identified by the respondents. We elicited respondent interests
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in PHEV designs through a series of design games. Of the 877 total respondents, here
we focus exclusively on those we deem to represent the plausible early market— the
282 respondents that satisfied two conditions: 1) at their home they parked within 25
feet of an electrical outlet at least once during their 24- hour diary day, and 2) they
opted to design and ( hypothetically) purchase that PHEV in the higher price design
game. In other words, we focus on the one third of California respondents that
demonstrate both easy access to home recharge infrastructure and substantial interest
in owning a PHEV. The next four sections briefly summarize these respondents’
PHEV designs, driving behavior, and recharge potential, and how we used this data to
construct aggregate recharge profiles. Further information about this survey are
detailed in Axsen and Kurani ( 2009), with California recharge profiles constructed in
Axsen and Kurani ( 2010)
3.1. Consumer- informed PHEV designs
After receiving a “ PHEV buyers’ guide” describing basic PHEV design
options, respondents completed an online PHEV design game allowing them to
upgrade ( or not) their next anticipated vehicle purchase to a PHEV, manipulating CD
type ( AE or B), CD- range ( X), recharge time, and CS fuel economy for incremental
price increases ( Table 1). Although respondents were free to specify any vehicle
model as their likely next new vehicle purchase, to represent energy use we simplify
their PHEV designs into either 1) cars ( and car- like vehicles) or 2) trucks ( and truck-like
vehicles).
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Table 1. PHEV purchase design game options and prices ( Axsen and Kurani, 2009)
Attributes Attribute level Car Truck
Base premium over conventional $ 3,000 $ 4,000
Added premiums:
Recharge time 8 hours
4 hours
2 hours
1 hour
0
+$ 500
+$ 1,000
+$ 1,500
0
+$ 1,000
+$ 2,000
+$ 3,000
CD mpg and type a
Blended ( B- X)
75 mpg
100 mpg
125 mpg
All- electric ( AE- X)
0
+$ 1,000
+$ 2,000
+$ 4,000
0
+$ 2,000
+$ 4,000
+$ 8,000
CD range
10 miles
20 miles
40 miles
0
+$ 2,000
+$ 4,000
0
+$ 4,000
+$ 8,000
CS mpg Conventional mpg
+ 10
Conventional mpg
+ 20
Conventional mpg
+ 30
0
+$ 500
+$ 1,000
0
+$ 1,000
+$ 2,000
a Metric conversions: 75 mpg = 3.14 L/ 100km, 100 mpg = 2.35 L/ 100km, 125 mpg = 1.88 L/ 100km,
and all- electric = 0.00 L/ 100km.
The resulting designs sharply contrast with previous PHEV design
assumptions. Fig. 3 portrays the distribution of selected PHEV designs according to
CD type and range. Most plausible early market respondents opted to maintain the
lowest CD options offered in the design game; 67.7 percent selected the least
ambitious CD type ( B- X, 75 MPG) and 80.6 percent selected the least ambitious CD
range ( 10 miles). CS fuel economy ( not shown in Fig. 3) was the most frequently
selected upgrade— only 46.5 percent of respondents stayed with the base increase of
10 MPG over the conventional vehicle they specified as their likely next new vehicle,
while 24.2 and 29.3 percent opted for the 20 and 30 MPG increases, respectively.
Although 69.9 percent selected eight hour recharge time, for the construction of
recharge profiles we adjusted recharge times to better match the battery size actually
required for the respondents selected CD type and range ( Table 2), as estimated in
Axsen et al. ( 2010). Thus, 79 percent of respondents’ designs require less than two
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hours to fully recharge, 18 percent require two to four hours, and 3 percent require
more than four hours.
- 0.002
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0 10 20 30 40
CD Range ( miles)
CD Type ( gallons per mile)
B- X ( 75 MPG)
B- X ( 100 MPG)
B- X ( 125 MPG)
AE- X ( EPRI)
( EPRI,
S& M)
( EPRI,
S& M,
S& S)
Fig. 3. Comparing the distribution of consumer- designed PHEVs ( User) with the
AE- X designs assumed in previous analyses ( area of each circle indicates the
proportion of survey respondents selecting a given PHEV design; arrows point to the
PHEV designs assumed in previous analyses).
Table 2. Assumed PHEV energy use ( kWh/ mile) and required battery capacity
( kWh)
CD mpg Car Truck
75 MPG CD electricity use
10 mile capacity
20 mile capacity
40 mile capacity
0.12 kWh/ mile
1.2 kWh
2.3 kWh
4.6 kWh
0.15 kWh/ mile
1.5 kWh
3.0 kWh
5.9 kWh
100 MPG CD electricity use
10 mile capacity
20 mile capacity
40 mile capacity
0.14 kWh/ mile
1.4 kWh
2.7 kWh
8.0 kWh
0.17 kWh/ mile
1.7 kWh
3.5 kWh
7.0 kWh
125 MPG CD electricity use
10 mile capacity
20 mile capacity
40 mile capacity
0.18 kWh/ mile
1.8 kWh
3.6 kWh
7.3 kWh
0.23 kWh/ mile
2.3 kWh
4.7 kWh
9.3 kWh
All electric CD electricity use
10 mile capacity
20 mile capacity
40 mile capacity
0.30 kWh/ mile
3.0 kWh
6.0 kWh
12.0 kWh
0.38 kWh/ mile
3.8 kWh
7.7 kWh
15.4 kWh
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3.2 Consumer- informed PHEV driving behavior
Each survey respondent also completed a 24- hour driving diary for one of
their new vehicles. They were randomly assigned a day of the week, and starting with
the first trip of that day, they recorded the time of departure, duration, and distance of
each trip made in their vehicle for the next 24 hours. The present study’s distribution
of travel behavior does not differ significantly from relevant sub- samples drawn from
previous travel diary studies ( Duvall et al., 2007; Samaras and Meisterling, 2008;
USDOT, 2004).
3.3 Consumer- informed PHEV recharge potential
Respondents reported the start time and duration of each parking episode, and
the distance to the nearest electrical outlet from the vehicle allowing us to construct a
24- hour profile of recharge potential for each respondent’s vehicle for a given outlet
distance. We assume an outlet is available for recharging if it was reported to be
within 25 feet of the parked car regardless of who owns the outlet. On average for
weekdays, over 95 percent of our plausible early market respondents are parked
within 25 feet of an electric outlet from midnight to 5am; this reduces to a minimum
of 23 percent at midday. The full recharge potential distribution for California
respondents is portrayed in Axsen and Kurani ( 2010).
3.4 Constructing consumer- informed PHEV recharge profiles
PHEV energy use results from the interaction between vehicle designs, travel,
and recharge events. For example, shorter cumulative distance between recharge
events and longer CD ranges result in a higher proportion of CD driving miles ( what
is commonly referred to as the utility factor), increased electricity usage, and
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decreased gasoline usage. We construct distributions of driving and recharging
behaviors by matching the disaggregated, temporally explicit data from each
respondent’s 24- hour diary to their PHEV design.
To further explore the effects of recharging on GHG emissions, we construct
three PHEV design conditions of time of day electricity demand:
1. “ User” design represents the distribution of PHEV designs as elicited from
the plausible early market ( Fig. 3).
2. “ AE- 20” replaces each respondent’s selected PHEV design with an AE- 20.
In this scenario, all PHEVs are assumed to recharge at 1 kW ( 1 kWh per
hour— attainable with a 110- volt outlet), resulting in a total of 6.0 hours to
fully recharge a car and 7.7 a truck. Any CS driving is assumed to be done
at a fuel economy 15 MPG higher than the respondent’s selected
conventional vehicle ( which was the basis for their PHEV design).
3. “ AE- 40” in which CD type and CS fuel economy are similar to the AE- 20,
but with a 40- mile CD range and a faster recharge rate of 2 kW ( attainable
with a 220- volt outlet or a higher amperage 110- volt outlet). This faster
recharge rate is allows a higher proportion of drivers to fully recharge the
PHEV at night— at only 1 kW, a depleted AE- 40 would require 12 to 15.4
hours to fully recharge. For this AE- 40 design scenario, we assume any
identified 110- volt outlet can recharge the vehicle at the 2kW.
We add these AE- 20 and AE- 40 design conditions to facilitate comparison
with prior PHEV studies, as well as to explore the potential GHG impacts of a world
where consumers more strongly value AE range. For each of the three PHEV design
conditions, we construct three different recharge conditions that not only affect timing
of vehicle charging but also total amount of electricity used ( illustrated in Fig. 4):
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1. “ Plug and play”: Drivers are assumed to plug in and begin recharging
immediately whenever they park within 25 feet of an electrical outlet.
There are no pricing mechanisms, e. g., time of use electricity tariffs, or
technologies, e. g., smart charging mechanisms, to divert recharging to off-peak.
2. “ Universal workplace access”: In addition to whatever recharging they do
in “ plug and play,” all drivers ( who commute to a workplace) can and do
recharge if and when they park at their workplace.
3. “ Off- peak only”: No PHEV recharging is allowed during daytime peak
hours ( 6am to 8pm). The timing of electricity use over the off- peak period
is represented as a constant load between 8pm and 6am. In reality, a
particular electric utility would not desire a constant load, but would
instead seek to vary the recharge profile according to their particular
demands and needs, e. g. “ valley filling.” For the present purpose of
calculating GHG emissions, however, a constant load is sufficient to
generally represent an off- peak scenario.
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0%
3%
6%
9%
12%
15%
12am 4am 8am 12pm 4pm 8pm 12am
Time of Day
PHEV Recharge Demand (%)
Plug and play
Universal workplace
Off - peak only
EPRI profile
Fig. 4. Comparing consumer- informed recharge profiles ( User designs, weekdays
only) with EPRI’s recharge profile ( Duvall et al., 2007).
We use these three PHEV design conditions and three recharge conditions to
construct nine recharge profiles for weekday and weekends using a spreadsheet
model— thus a total of 18 24- hour recharge profiles. For each recharge profile, we
assume that vehicle recharging is 83.3 percent efficient ( following Lemoine et al.,
2008) and that PHEVs will be driven precisely as were the respondents’ vehicles as
recorded on their diary day. Further assumptions used in the construction of these
recharge profiles are detailed in Axsen and Kurani ( 2010).
The resulting 18 recharge profiles are portrayed in Table A2 at hourly
intervals, as scaled to one million PHEVs. Total daily electricity demand is three to
five times higher in the AE- 20 and AE- 40 PHEV design conditions relative to the
User condition. Relative to the plug and play condition ( and for all vehicle design
conditions), the universal workplace access condition increases total daily electricity
demand by 15 to 30 percent, while the off- peak only condition reduces total daily
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electricity demand by 10 to 25 percent. Increasing vehicle access to charging ( from
off- peak only to plug and play to universal workplace) increases higher average
battery SOC, thus miles driven in CD mode and grid electricity used. Fig. 4 illustrates
recharge profiles based on the User design condition and the three recharge
conditions, where uncontrolled recharge conditions can result in very different
profiles than those assumed by previous studies ( e. g. Duvall et al., 2007). If PHEV
buyers plug in when they can within the current infrastructure and there is no effort or
ability to defer demand, the majority of PHEV recharging will occur during present
peak electricity demand hours.
4. Representing the supply- side: Modeling electricity and gasoline GHG impacts
4.1. Simulating electricity GHG emissions: California dispatch models
The carbon intensity of electricity generation is determined by the mix of
power plants that are operating, which in turn is influenced by total electricity demand
which varies by time of day and time of year. There are three approaches that are
frequently used to represent GHG emissions from electricity used by PHEVs:
1. Apply an annual average carbon intensity ( gCO2/ kWh) for all electricity
used ( e. g. Samaras and Meisterling, 2008);
2. Apply an hourly average emissions rate to represent emissions by time of
day and year, averaged across the grid mix of power plants; or
3. Represent hourly marginal emissions rates to account for incremental
GHG emissions from power plants operating during vehicle recharging
that would not be running otherwise ( e. g. Duvall et al., 2007; Stephan and
Sullivan, 2008).
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We estimate both hourly average and hourly marginal GHG emissions to
recharge PHEVs. Both hourly allocation methods require modeling the mix of power
plants generating electricity over time. The marginal generation mix in California
consists of fossil- fired power plants, usually natural gas. The hourly average mix
accounts for all electricity generation in a given hour, including hydro, nuclear, and
renewable resources— about 35 percent from power plants with almost zero
operational GHG emissions. Thus, assuming hourly marginal rather than hourly
average rates in California places a disproportionately larger burden for GHG
emissions on PHEVs than on all pre- existing electricity uses ( though this isn’t the
case for regions with high fractions of coal- fired generation). On the other hand,
assigning the hourly average emissions rate does not emphasize the incremental
impacts of recharging PHEVs. We leave it to readers and encourage policymakers to
consider societal issues in choosing which emissions to assign to new demand.
We simulate average and marginal emissions rates using the Electricity
Dispatch model for Greenhouse Gas Emissions in California ( EDGE- CA) to represent
a present energy scenario and the long- term version ( LEDGE- CA) to simulate the
planned 2020 California grid ( Table 3). EDGE- CA is a spreadsheet- based accounting
tool that represents supply, demand, and energy transfers among three regions in
California as well as imported power from out of state. EDGE- CA represents
variations in power plant availability based on hourly, daily, and seasonal factors. To
calculate hourly marginal GHG emissions, the EDGE- CA model tracks the last power
plant dispatched. The data sources, decision rules and supply curves for EDGE- CA
are discussed further in McCarthy and Yang ( 2010).
The LEDGE- CA model is more appropriate for long- term analysis ( McCarthy,
2009). LEDGE- CA includes power plant retirements and capacity expansion, but
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more simply represents dispatch by defining California as a single region and ignoring
imports of electricity ( though it does include imports from coal- fired power plants
from contracts expected to be held by California in 2020). In this way, the costs of all
new capacity and generation supplying California electricity demand is attributed to
its ratepayers, regardless of where the power plants are located. LEDGE- CA
calculates an optimal distribution of new capacity from fossil- fired power plants to
complement supply scenarios that dictate the level of hydro, nuclear, and renewable
generation in the state. In this analysis, LEDGE- CA is applied to simulate electricity
capacity and generation for a “ future” energy scenario ( year 2020), assuming a 33
percent Renewable Portfolio Standard ( RPS) is implemented in California ( Douglas et
al., 2009). Power plants are dispatched in similar fashion as in the EDGE- CA model.
Table 3. California electricity supply composition in 2010 ( EDGE- CA) and 2020
( LEDGE- CA).
2010 grid ( EDGE- CA model results) e 2020 grid ( LEDGE- CA model results) e
Total Generation
( Used for average
emissions rates)
Marginal Generation
( Used for marginal
emissions rates)
Total Generation
( Used for average
emissions rates)
Marginal Generation
( Used for marginal
emissions rates)
Annual
gen.
( GWh)
GHG rate
( gCO2e/
kWh)
Annual
gen.
( GWh)
GHG rate
( gCO2e/
kWh))
Annual
gen.
( GWh)
GHG rate
( gCO2e/
kWh)
Annual
gen.
( GWh)
GHG rate
( gCO2e/
kWh)
Nuclear 46,150 16 - - 36,085 16 - -
Renewables 25,554 0 - - 107,424 0 - -
Coal 39,149 1,154 - - 48,896 866 - -
Hydro 53,196 0 - - 37,557 0 - -
NGCC & CHP a, b 127,221 504 505 690 111,125 486 746 549
NGST & NGCT c, d 9,221 760 487 760 5,033 653 247 601
Other 2,556 1,176 2 794 - - - -
GHG rate averaged
over one year
403 724 290 562
a CHP = ( Natural gas) Combined heat and power;
b NGCC = Natural gas combined cycle;
c NGCT = Natural gas combustion turbine;
d NGST = Natural gas dsteam turbine
e The LEDGE- CA model excludes imports except for coal- fired power plant import contracts expected
to be held by California load serving entities in 2020. Thus, while in- state nuclear and hydro generation
are constant in both cases ( equal to the 2020 values), generation from those power plants is higher in
2010 because it includes imported hydro and nuclear power from out of state that supplies California
electricity demand in the near term
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4.2 Modeling gasoline use
For the 2010 baseline of conventional vehicles, we model gasoline use
according the respondents’ driving day recorded in their diaries. Each survey
respondent input an MPG estimate for their anticipated next vehicle purchase. This
estimate is applied to each mile travelled during their diary day. In other words, if the
vehicle is rated at 20 MPG, we assume a constant rate of fuel use for each mile driven
( neglecting potential for varying drive patterns within a trip, among trips, or across
drivers, and potential inaccuracies in actual drive cycle). We consider two additional
baselines of gasoline- using vehicles: 1) 2010 hybrid- electric vehicles ( HEVs) with 53
percent greater fuel economy than conventional vehicles ( ANL, 2009), and 2) a 2020
fleet of new vehicles required to meet future fleet average fuel economy standards of
35 MPG ( NHTSA, 2009).
GHG emissions from gasoline use are estimated using a flat rate per liter of
gasoline. For the 2010 energy condition, we start with 67 gCO2/ MJ ( EPA, 2006) and
add an upstream emissions rate of 19 gCO2/ MJ from the GREET model ( Wang,
2001), totaling 86 gCO2/ MJ. This is the value also used by Samaras and Meisterling
( 2008). For the 2020 energy scenario, we account for California’s Low Carbon Fuel
Standard ( LCFS), which requires a 10 percent reduction of lifecycle carbon intensity
across all on- road transportation fuels ( including electricity) used in California by
2020 ( Farrell and Sperling, 2007). For the 2020 energy condition we assume gasoline
carbon intensity is reduced from its 2010 value by 10 percent, presumably by
blending in a low- carbon biofuel. Because of the low carbon intensity of electricity in
LCFS calculations, increased vehicle electricity use will offset some of the need to
reduce the carbon intensity of gasoline and reductions in “ gasoline” carbon intensity
are slightly lower in the PHEV conditions ( but still very close to 10 percent).
- 20-
5. Results and discussion
5.1 Comparing User and AE- 40 GHG emissions profiles
In Fig. 5 we compare marginal hourly GHG emissions for one million PHEVs
distributed as the survey respondents’ designs ( User) and AE- 40 designs under
present California energy conditions for the median GHG emissions day modeled for
2010: June 12th. These GHG profiles illustrate the differences among modeled PHEV
scenarios. For each graph, the area under the dotted line depicts the day’s baseline,
that is, total GHG emissions from one million conventional vehicles ( with emissions
peaks corresponding with peak travel times)— this baseline is identical in all six
graphs. The day’s total PHEV GHG emissions is the sum of the light grey ( gasoline
emissions) and dark grey ( electricity emissions) areas for each graph.
Because the User PHEV design distribution is dominated by B- 10 vehicles,
the majority of User miles ( 56 to 74 percent) are driven in CS mode, and gasoline
accounts for the majority of driving energy use ( 88 to 93 percent) and GHG emissions
( 78 to 89 percent). In contrast, imposing AE- 40 designs onto respondents’ driving and
the recharge patterns results in less CS driving ( 21 to 38 percent of miles), a lower
proportion of gasoline energy use ( 34 to 54 percent) and a lower proportion of GHG
emissions from gasoline ( 19 to 40 percent).
Despite these differences, total GHG emissions are similar between the
scenarios— in fact slightly favoring the User designs— because of two countervailing
components of total vehicle carbon intensity ( gCO2/ mile): total vehicle energy
consumption ( MJ/ mile) and total fuel carbon intensity ( gCO2/ MJ). Because electricity
is used more efficiently in vehicles than gasoline is, the AE- 40s’ relatively high use of
electricity results in 25 percent lower energy consumption on average ( 1.89 MJ/ mile)
than User designs ( 2.50 MJ/ mile) in the plug- and- play conditions in Fig. 5. However,
- 21-
because the fuel carbon intensity of gasoline is lower than for marginal electricity
calculated by EDGE- CA in 2010 ( Table 4), User designs’ fuel mix has 29 percent
lower total fuel carbon intensity ( 103 gCO2/ MJ) than AE- 40 designs ( 146 gCO2/ MJ).
As a result, User designs emit 6 percent less GHG emissions than AE- 40 designs on
the modeled day, and 9 percent less averaged across the year.
0
500
1000
1500
2000
2500 PHEV electricity GHGs
PHEV gas GHGs
CV gas GHGs
0
500
1000
1500
2000
2500
12am 4am 8am 12pm 4pm 8pm 12am
0
500
1000
1500
2000
2500
12am 4am 8am 12pm 4pm 8pm 12am
CO2 per million PHEVs ( Metric Tonnes)
“ User” ( Consumer- designed distribution) AE- 40 ( All- electric, 40 mile CD range)
A
B
C
D
E
F
PHEV Total =
11,838
Tonnes
11,891
Tonnes
11,548
Tonnes
12,637
Tonnes
12,865
Tonnes
11,434
Tonnes
CV Total =
18,650 Tonnes
“ Off- peak Only” “ Universal Workplace” “ Plug and Play”
Fig. 5 Time of day GHG emissions from 1 million PHEVs, including gasoline and
marginal electricity in 2010 energy scenario ( median GHG emissions day, weekday,
June 12 th )
- 22-
5.2 Annual marginal and average emissions
Annual PHEV emissions for each scenario are based on seasonally varying
hourly marginal and average electricity emissions rates ( summarized for plug and
play condition in Tables A3 and A4). Fig. 6 only depicts GHG impacts according to
hourly marginal emissions rates, while Table 4 also includes hourly average emissions
rates. Applying hourly marginal electricity emissions rates from the current California
energy scenario ( EDGE- CA, no LCFS), we calculate 37 to 38 percent GHG
reductions for User designs, and 30 to 35 percent reductions for both AE designs.
These reductions do not substantially differ from an assumed fleet of today’s HEVs;
User designs emit three to five percent less than the modeled HEVs, while AE designs
emit one percent less to seven percent more. Under the future energy scenario
( LEDGE- CA, with LCFS), User and AE designs reduce marginal GHGs by 20 to 24
percent when compared with a future fleet of higher efficiency conventional vehicles
mandated by federal fuel economy ( CAFE) standards ( which may include a
significant proportion of HEVs).
- 23-
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
CVs only
Generic HEV
Plug and Play
Workplace
Off- peak only
Plug and Play
Workplace
Off- peak only
Plug and Play
Workplace
Off- peak only
35 MPG Fleet
Plug and Play
Workplace
Off- peak only
Plug and Play
Workplace
Off- peak only
Plug and Play
Workplace
Off- peak only
CO2 Per Million Vehicles ( Annual Million Tonnes)
Gasoline Home Recharge Work Recharge Other Recharge
User Designs AE- 20 AE- 40 User Designs AE- 20 AE- 40
Current Energy Scenario
( 2010 California)
Future Energy Scenario
( 2020 California)
Base Base
Fig. 6. Annual GHG emissions from 1 million PHEVs, including gasoline and
marginal electricity by recharge location: home, work, or other.
Table 4 details several key vehicle and fuel metrics that govern energy use and
GHG emissions. CS operation of User designs is more efficient ( MJ/ mile) than the
AE designs, while the opposite is true in CD operation. In 2010 and 2020, the User
designs have a lower carbon intensity ( gCO2/ mile) that AE designs in both CS and
CD modes, when considering marginal emissions. However, when considering
average emissions, a more electrified PHEV design ( AE- 20 or AE- 40) is almost
always more desirable from a GHG standpoint than the User designs. Further, every
PHEV scenario results in lower emissions than the HEV scenario. Thus, when
considering hourly average electricity emissions, the use of PHEVs becomes more
desirable from a GHG perspective, particularly AE designs that use more electricity.
Although our results portray totals and averages across respondents, all
scenarios are based on a wide variety of underlying individual consumer interests and
- 24-
behaviors. For instance, while the first PHEV scenario ( User design, plug and play,
2010 grid, marginal emissions rate) indicates an annual average of 256 gCO2/ mile, the
distribution of rates modeled for each survey respondent ( and their unique
combination of PHEV design, driving behavior and recharge potential) can range
from one- half below to two- thirds above this value.
Table 4. Energy use and GHG emissions among vehicle scenarios ( 15,949 million
vehicle miles travelled by the distribution of 1 million vehicles over one year of
simulation)
Total energy intensity
( MJ/ mile)
Total fuel
carbon intensity
( gCO2/ MJ)
% GHG
reductions
from base CV
% GHG
reductions from
HEV
CD CS Total Mar. Avg. Mar. Avg. Mar. Avg.
2010 Energy
CVs only 4.31 93.7 93.7
HEVs 2.82 93.7 93.7
User designs
Plug and play 2.05 2.70 2.47 103.4 95.3 37% 42% 3% 11%
Uni. workplace 2.05 2.71 2.43 105.7 95.6 36% 43% 3% 12%
Off- peak only 2.04 2.69 2.51 99.4 95.0 38% 41% 5% 10%
AE- 20 designs
Plug and play 1.45 2.81 2.20 123.7 99.1 33% 46% - 3% 18%
Uni. workplace 1.45 2.81 2.06 133.6 100.8 32% 49% - 4% 22%
Off- peak only 1.45 2.80 2.27 115.1 98.7 35% 45% 1% 15%
AE- 40 designs
Plug and play 1.45 2.87 1.87 148.3 103.8 31% 52% - 5% 26%
Uni. workplace 1.45 2.86 1.76 160.5 105.7 30% 54% - 7% 30%
Off- peak only 1.45 2.83 1.95 134.3 103.2 35% 50% 1% 24%
2020 Energy
35 MPG fleet 3.43 84.4 84.4
User designs
Plug and play 2.05 2.70 2.47 99.4 92.5 22% 28%
Uni. workplace 2.05 2.71 2.43 100.6 92.2 22% 29%
Off- peak only 2.04 2.69 2.51 97.1 93.0 23% 27%
AE- 20 designs
Plug and play 1.45 2.81 2.20 111.1 90.1 20% 36%
Uni. workplace 1.45 2.81 2.06 117.0 88.8 21% 41%
Off- peak only 1.45 2.80 2.27 106.3 91.2 22% 34%
AE- 40 designs
Plug and play 1.45 2.87 1.87 125.0 87.2 22% 46%
Uni. workplace 1.45 2.86 1.76 131.9 85.5 22% 50%
Off- peak only 1.45 2.83 1.95 117.6 89.1 24% 43%
5.3 Comparing recharge conditions
When considering hourly marginal electricity emissions rates in California
( Fig. 6), “ off- peak only” recharging results in slightly larger reductions in GHG
- 25-
emissions for each vehicle design in present and future energy conditions. In other
words, within the range of conditions explored here, to reduce GHG emissions it is
better to constrain PHEV recharging to off- peak hours, even if it results in less
recharging and electricity use overall. In contrast, applying hourly average electricity
emissions rates ( Table 4) encourages additional daytime recharging, i. e. universal
workplace access, resulting in slightly larger GHG reductions across vehicle design
and recharge conditions. However, these variations are slight; overall GHG reductions
vary by only one to seven percentage points across recharge conditions for each
combination of PHEV and energy condition.
5.4. Sensitivity to electricity and gasoline carbon intensity
We depict the sensitivity of GHG emission reductions to the carbon intensity
of electricity supply which varies across regions, with future developments, and with
the assumption of hourly marginal versus hourly average emissions rates ( Fig. 7).
Graph 7A presents a current gasoline scenario, assuming no LCFS is in place and
depicting only 2010 baselines ( current conventional vehicles and HEVs) for
comparison. Graph 7B is a future gasoline scenario, assuming the LCFS is in place,
and depicting a future fleet meeting the 35 MPG CAFE standard. The ranking of the
three PHEV emissions scenarios relative to each other and to the three vehicle
baselines clearly depends on electricity and gasoline carbon intensity. The vertical
lines in each graph depict the aggregated electricity carbon intensity applied in each
California energy scenario explored above ( using marginal and average emissions
rates), as well as the present U. S. annual average rate used by Samaras and
Meisterling ( 2008).
- 26-
0
50
100
150
200
250
300
350
400 CVs only ( 27.7 MPG)
HEV ( 42.5 MPG)
2010 CA
Hourly
Avg.
2010 CA
Hourly
Marg.
Present
U. S.
Annual
Avg. ( S& S)
0
50
100
150
200
250
300
350
400
0 100 200 300 400 500 600 700 800 900 1000
Carbon Intensity of Electricity Used in PHEVs ( gCO2/ kWh)
2020 CAFE Fleet ( 35 MPG)
2020 CA
Hourly
Avg.
2020 CA
Hourly
Marg.
Driving Carbon Intensity ( gCO 2 / mile)
A
B
User
AE- 20
AE- 40
User
AE- 20
AE- 40
Fig. 7. Comparing GHG emissions across electricity carbon intensity rates under plug
and play recharge conditions ( A: 2010 energy scenario; B: 2020 energy scenario)
With current gasoline emissions intensity ( Fig. 7A), an important pivot point
is ~ 600 gCO2/ kWh ( typical of a natural gas- fired combustion turbine), above which
User designs result in deeper GHG reductions than AE designs. Another pivot point is
~ 850 gCO2/ kWh, below which User designs result in deeper reductions than our
selected HEV baseline. Of the vehicles we represent here, User designs result in the
- 27-
lowest GHG emissions between these pivot points ( a range that includes current
marginal CA emissions and the U. S. annual average), though differences between
consumers’ PHEV designs and HEVs are slight. More dramatic GHG reductions can
be realized below 600 gCO2/ kWh, particularly for AE designs. With less carbon
intensive gasoline under an LCFS or similar policy ( Fig. 7B), User designs produce
the most reductions with electricity sources above 550 gCO2/ kWh and have less GHG
emissions than a fleet of 35 MPG vehicles even under highly carbon intensive coal-based
electricity sources ( over 1000 gCO2/ kWh).
6. Conclusions
6.1 Summary of results
Previous analyses of PHEV GHG impacts rely on simplistic representations of
the demand side ( consumer interests and behaviors), and too simple representations of
the supply side ( GHG emissions impacts of energy demanded). The present study
improves upon previous efforts regarding the former and at least matches the best
previous efforts regarding the latter. We highlight several key results.
• Consumer- designed ( User) PHEVs— which mainly consist of blended, low
CD range designs— can reduce “ source to wheel” GHG emissions
compared to conventional vehicles in all the recharge and energy
conditions we simulated.
• User- designed PHEVs can also reduce GHG emissions relative to AE- 20 or
AE- 40 designs when electricity is generated by sources with emissions
above 600 gCO2/ kWh, e. g., most present- day natural gas or coal plants.
- 28-
• AE- X designs may yield deeper GHG emissions reductions than User
designs as the carbon intensity of electricity supply falls ( below 600
gCO2/ kWh).
• Constraining recharging to off- peak times results in deeper GHG reductions
when using more carbon- intensive electricity sources; in contrast, less
carbon- intensive electricity may warrant measures to facilitate increases in
daytime recharging, e. g. via workplace recharge infrastructure.
Our estimates of GHG emissions reductions are comparable to previous
studies, but our assumptions differ in important ways ( Table A1). For instance, EPRI
estimates larger reductions due to assumptions of a less carbon- intensive electricity
grid, only AE- X designs, and primarily off- peak recharging ( Duvall et al., 2007).
Stephen and Sullivan ( 2008) estimate even larger reductions due to a focus only on
AE- 40 designs driven only in CD mode— thus using zero gasoline. In contrast, the
present analysis elicits consumer data to construct scenarios with more gasoline-intensive
PHEV use, and more carbon- intensive electricity sources. Thus, our
estimated reductions tend to be slightly lower than previous studies.
Further, our general findings are robust to a range conditions, and can be
extended beyond the California context. Although we use representations of
California consumers and energy supply, elicited distributions of consumer interests
in PHEVs, driving behaviors, and access to recharging are similar to those of a
nationwide sample ( Axsen and Kurani, 2009), and we depict a range of electricity
carbon intensities that could approximate other regions ( Fig. 7). Of course, we caution
that the specific details of each region will differ due to unique interactions between
consumer design priorities, driving and recharge patterns, and time of day electricity
carbon intensity.
- 29-
Important limitations remain. Increasing the complexity in our model to
represent present consumer interests and behaviors and the present energy system
operation does not guarantee a more accurate depiction of the future. Our three
recharging conditions span many, but not all, possible conditions and the adoption of
PHEVs may change buyers’ driving and parking behavior and availability of vehicle
charging. Also, the PHEV design exercises risk being more adaptable to individual
desires than the actual vehicle market may be, and our fuel economy ( MPG) and
electricity use ( Wh/ mile) assumptions do not account for variations in driving
behavior. However, we feel that are general conclusions are robust to a variety of
conditions.
6.2 Implications of results
Implications of our findings can be framed from two perspectives. A shorter-term
focus on GHG emissions reductions suggests that consumer- designed PHEVs
can reduce GHG emissions relative to conventional vehicles, but similar ( marginal)
reductions are also attainable by HEVs. Thus, if one assumes that all the one- million
vehicle scenarios explored here are equally probable, HEVs or other high- efficiency
gasoline vehicles ( averaging 42.5 mpg) may prove a more effective GHG abatement
strategy in the short term, say over the next decade, rather than PHEVs.
In contrast, a longer- term perspective suggests a plausible trajectory for
achieving deeper GHG reductions from PHEVs beyond the next decade. A logical
starting point is to provide consumers with the PHEV designs they presently want ( B-X,
shorter CD range, higher fuel economy in CS mode). This starting point of cheaper
B- X designs could set the stage for future commercialization of AE- X designs by
increasing consumer experience with, and exposure to, PHEV technology, increasing
- 30-
consumer valuation of AE- X capabilities and reducing battery and drivetrain costs
due to increased manufacturing experience. With the emergence of less- carbon
intensive electricity sources, a transition from B- X to AE- X designs could lead to
deeper long- term GHG reductions than a strategy that focuses only on HEVs or AE- X
designs.
We offer several key conclusions.
• Even if PHEVs do not currently offer larger incremental GHG reductions
relative to HEVs, they could with future, less- carbon intensive electricity
sources.
• Policymakers and researchers should not overlook the cheaper, lower
battery- capacity B- X PHEVs that consumers presently design. Such
designs are not only likely to be easier to sell to more consumers than AE-X
variants, but we estimate they may also initially yield similar or larger
GHG reductions.
• PHEV impact analyses can be improved by explicitly consulting potential
users. Survey respondents designed vastly different PHEVs than the AE- X
designs assumed by previous studies. Even if the near- to mid- term market
for PHEVs contains less variety than the distribution of User designs,
uniform assumptions regarding PHEV designs contradict the PHEV
designs of our respondents and the forces that create variation across
vehicle makes and models.
• Empirically observed recharge potential suggests that without substantial
policy intervention, actual recharge behavior is likely to follow a much
different and more diffuse recharge profile than previously assumed.
- 31-
Finally, we acknowledge that our study focuses only on GHG emissions.
Consideration of other potential PHEV benefits such as energy security, air quality
and promotion of renewable energy are left to other research. In any case, it strikes us
as compelling to begin with the PHEV designs consumers want to buy as the starting
point of a trajectory toward achieving these benefits.
Acknowledgements: The authors thank the California Energy Commission, the
Social Sciences and Humanities Research Council of Canada, the 877 California
households who completed our multi- day questionnaire, CH2M Hill, and the sponsors
of the Hydrogen Pathways Program and the Sustainable Transportation Energy
Pathways Program at the Institute of Transportation Studies at UC Davis.
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Appendix
Table A1. Summary of PHEV impacts literature
Recharge profile inputs
Study
Base
vehicle
( MPG)
PHEV type
( CD kWh/ mile)
Driving patterns
( utility factor)
Recharge
patterns
Electricity
( gCO2e/ kWh)
GHG reductions
from ICEV baseline
( from HEV base)
Studies with GHG emissions analysis
EPRI ( Duvall et al.,
2007)
ICEV ( 30)
HEV ( 46.3)
AE- 10, 20, 40
17 models
( 0.26- 0.31)
U. S. VMT
distribution
( 12- 66%)
EPRI profile
( 74%
off- peak)
Marginal, 2050 U. S.
dispatch model:
( 300- 430)
38- 65% less
( 4- 46% less)
Samaras and
Meisterling ( 2008)
ICEV ( 30) AE- 19, 38, 56;
Toyota Prius
( 0.32)
2001 NHTS
distribution
( 47- 76%)
Daily
recharge
Annual avg,, U. S.
( 200- 950)
15- 51% less
( 47% less to
18% more)
Stephen and Sullivan
( 2008)
ICEV ( 19)
HEV ( 27)
AE- 40;
RAV- 4 SUV
( 0.41)
CD only
( 100%)
Nightly
recharge
( valley- filling)
Marginal, U. S.
elasticities
( 692- 1072)
59% less
( 40% less)
Hadley and Tsvetkova
( 2008)
HEV ( 40) AE- 20;
Sedan ( 0.26- 0.30)
SUV ( 0.39- 0.47)
CD only
( 100%)
Evening/
nightly ( 120
or 220 V)
Marginal, 2020- 30
U. S. dispatch
model ( 600- 690)
( 3% less to
10% more)
Silva et al ( 2009) Series ( 54)
Parallel ( 49)
15 kWh vehicles
( 0.12 to 0.20)
Drive cycle
simulations
( U. S., EU,)
Daily
recharge
Annual average,
U. S. ( 543)
EU ( 387)
( 30- 50% less)
NAS ( 2009) ICEV ( 32- 41)
HEV ( 45- 60);
B- 10, AE- 40;
sedan;
( 0.08, 0.21)
U. S. VMT
distribution
( 23- 63%)
Nightly
recharge
Annual average, 2050
U. S EIA ( 520)
EPRI ( 210)
( small
reductions)
Present Study User CVs
( 28 avg.)
User distribution
( see Figs. 3 and 4,
Table 2)
User diary
distributions
( see Table 4)
User informed
scenarios
( see Fig. 5)
Marginal and avgerage,
CA dispatch model
( see Table 3)
2010: 27- 50% less
2020: 35- 61% less
Studies without GHG emissions analysis ( GHGs omitted) ( Other results)
Lemoine et al. ( 2008) CV ( 37.7)
HEV ( 49.4)
AE- 20,
compact car
( 0.25)
CD only Scenarios CA on peak day,
Aug 3 rd , 1999
Grid can
accommodate
1 million PHEVs
Sioshansi and Denholm
( 2009)
n/ a AE- 22; car
( 0.30)
St. Louis
Travel Survey
Whenever
parked ( 120
or 240 V)
Texas dispatch
model,
time of day
V2G can offset
grid GHG increases
Kang and Recker
( 2009)
n/ a A- 20, 60;
compact ( 0.21),
SUV ( 0.32- 0.37)
2001
California
Travel Survey
Scenarios:
( 120 or 240 V)
Hourly California
ISO data
Home charging can
power 40- 80% for
PHEV
- 34-
Table A2. Recharge profiles for 1 million PHEVs, by hour ( MW) and total ( MWh), for weekdays ( WD) and weekends ( WE)
USER Designs AE- 20 AE- 40
Plug/ Play Workplace Off- peak Plug/ Play Workplace Off- peak Plug/ Play Workplace Off- peak
Time
WD WE WD WE WD WE WD WE WD WE WD WE WD WE WD WE WD WE
0: 00 49 95 50 93 212 194 470 412 437 418 703 660 758 711 487 698 1,142 1,066
1: 00 63 32 60 31 212 194 373 294 343 302 703 660 614 487 426 427 1,142 1,066
2: 00 6 0 6 0 212 194 269 280 237 288 703 660 411 430 337 326 1,142 1,066
3: 00 0 0 3 0 212 194 169 204 167 204 703 660 287 322 254 229 1,142 1,066
4: 00 0 3 3 3 212 194 124 113 120 108 703 660 176 208 141 163 1,142 1,066
5: 00 1 12 15 11 212 194 52 71 68 70 703 660 68 105 82 104 1,142 1,066
6: 00 33 12 83 11 0 0 50 31 111 31 0 0 80 62 187 61 0 0
7: 00 49 13 212 17 0 0 74 21 289 25 0 0 142 42 538 49 0 0
8: 00 88 72 266 117 0 0 87 99 432 147 0 0 138 173 639 269 0 0
9: 00 87 56 258 92 0 0 131 167 493 213 0 0 240 127 797 222 0 0
10: 00 69 53 151 54 0 0 167 99 520 148 0 0 236 104 711 203 0 0
11: 00 59 101 126 101 0 0 161 115 495 166 0 0 234 178 674 280 0 0
12: 00 72 164 126 169 0 0 143 200 422 267 0 0 210 324 602 407 0 0
13: 00 137 198 198 197 0 0 219 296 472 348 0 0 356 520 678 521 0 0
14: 00 152 182 219 179 0 0 242 385 471 434 0 0 416 598 652 593 0 0
15: 00 197 158 208 155 0 0 334 432 501 460 0 0 565 621 714 615 0 0
16: 00 237 185 198 184 0 0 434 467 539 464 0 0 643 677 703 677 0 0
17: 00 259 265 248 262 0 0 543 571 540 568 0 0 784 823 772 822 0 0
18: 00 357 258 346 251 0 0 649 571 621 564 0 0 1,011 781 947 771 0 0
19: 00 351 127 293 118 0 0 726 591 700 581 0 0 1,244 815 1,107 790 0 0
20: 00 198 125 176 125 212 194 720 550 657 529 703 660 1,144 875 966 867 1,142 1,066
21: 00 148 131 140 144 212 194 716 525 639 536 703 660 1,061 980 853 994 1,142 1,066
22: 00 123 129 120 139 212 194 707 524 640 529 703 660 985 914 758 916 1,142 1,066
23: 00 94 86 85 96 212 194 607 485 563 484 703 660 890 792 613 801 1,142 1,066
Total 2,828 2,453 3,590 2,550 2,115 1,936 8,165 7,501 10,477 7,881 7,026 6,605 12,691 11,669 14,638 11,803 11,416 10,660
- 35-
Table A3. Time of day marginal emissions for User scenario, plug and play, averaged on a
monthly basis from 2010 EDGE- CA ( gCO2e/ kWh).
Hour J F M A M J J A S O N D Year
0: 00 595 566 573 540 522 575 588 717 714 604 566 610 579
1: 00 586 566 566 516 549 572 540 717 595 609 566 597 566
2: 00 569 566 540 516 516 544 534 566 544 566 566 553 546
3: 00 566 566 566 516 516 535 534 566 566 595 595 553 553
4: 00 -- -- -- -- -- -- -- -- -- -- -- -- --
5: 00 717 595 717 566 542 566 626 717 717 717 717 717 629
6: 00 717 717 717 591 553 626 717 726 726 717 717 717 717
7: 00 717 717 717 717 626 717 726 731 720 717 717 717 717
8: 00 717 717 717 730 717 726 763 766 763 717 717 717 717
9: 00 717 717 717 726 722 726 766 766 763 717 717 717 725
10: 00 717 717 726 722 726 763 763 745 763 722 717 717 726
11: 00 717 717 726 726 763 763 766 808 763 742 726 717 726
12: 00 717 717 726 726 763 740 766 811 763 746 717 722 726
13: 00 717 717 723 726 763 745 811 812 766 744 717 723 739
14: 00 717 717 722 724 763 763 811 812 766 726 717 723 738
15: 00 717 717 726 717 735 745 811 811 763 729 717 726 729
16: 00 717 717 722 726 726 745 811 848 763 722 721 726 726
17: 00 726 726 717 717 722 740 766 812 748 726 717 725 726
18: 00 726 726 718 720 723 763 766 766 745 753 723 726 729
19: 00 726 725 726 722 745 739 766 766 763 739 717 726 726
20: 00 725 723 726 722 763 763 766 766 763 717 717 717 726
21: 00 717 717 717 725 717 739 766 766 726 717 717 718 723
22: 00 717 605 711 575 563 717 726 763 743 717 717 725 717
23: 00 626 566 717 540 516 623 726 721 717 717 610 717 626
Avg 714 693 710 696 702 736 763 778 750 722 708 718 724
Table A4, Time of day marginal emissions for User scenario, plug and play, averaged on a
monthly basis from 2020 LEDGE- CA ( gCO2e/ kWh).
Hour J F M A M J J A S O N D Year
0: 00 530 515 482 461 442 432 474 540 530 505 514 524 498
1: 00 519 505 471 443 435 421 462 527 511 494 505 522 490
2: 00 512 488 434 418 407 382 434 502 482 486 496 496 456
3: 00 516 482 448 442 407 369 436 501 495 485 498 501 459
4: 00 -- -- -- -- -- -- -- -- -- -- -- -- --
5: 00 570 528 498 470 403 395 455 535 567 528 546 552 515
6: 00 586 562 500 465 432 407 476 528 558 539 566 574 520
7: 00 589 564 500 476 440 440 503 566 569 533 544 571 515
8: 00 576 572 511 483 463 467 528 581 589 542 563 560 531
9: 00 585 564 522 501 496 493 563 591 597 566 571 564 552
10: 00 580 564 526 513 537 522 597 632 615 584 576 564 558
11: 00 580 565 528 509 543 550 614 643 611 604 558 565 572
12: 00 574 570 512 507 549 556 630 659 633 596 568 567 571
13: 00 565 567 520 510 556 567 638 685 597 600 566 543 573
14: 00 560 570 510 502 541 563 630 673 606 593 557 541 567
15: 00 556 558 519 501 551 564 613 682 627 590 556 546 565
16: 00 573 570 509 492 516 549 604 674 619 599 608 578 571
17: 00 592 571 516 491 508 562 633 656 619 596 586 613 580
18: 00 595 604 513 482 493 514 581 643 622 595 593 601 581
19: 00 604 595 539 537 506 529 554 624 623 583 592 610 580
20: 00 612 606 561 542 549 521 581 627 624 597 592 612 592
21: 00 605 595 527 525 538 554 581 609 612 606 620 610 587
22: 00 595 559 504 479 488 493 535 612 584 557 567 589 548
23: 00 546 528 489 458 452 441 501 581 545 523 529 548 513
Avg 577 568 521 496 515 520 585 626 607 582 569 582 562