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Analysis of Projected Impact of Plug-in
Electric Vehicles on the Distribution Grid
Arindam Maitra
Electric Power Research Institute
INTRODUCTION
A new era of plug-in electric vehicles (PEVs) has begun. Nissan and General
Motors launched production PEVs in December 2010, and Ford, Mitsubishi,
Toyota, Tesla, and others have announced plans to introduce such vehicles to the
US market. With the rapidly approaching commercialization of plug-in hybrid
(PHEVs) and battery electric vehicles (BEVs) as well, utilities need to ensure that
they can support customers’ use of such vehicles by preparing for the installation
of residential, commercial, and private infrastructure in their service territories
and by managing the impact of these new loads on the electric distribution system.
In light of these developments and needs, the Electric Power Research Insti-
tute (EPRI) initiated a multiyear project (EPRI 2012; Maitra et al. 2009; Taylor et
al. 2009) with 19 utilities to understand PEV system impacts in the United States
and Canada. This paper provides an overview of the study, presents the results
relevant to the US analysis, and summarizes the conclusions.
STUDY METHODOLOGY AND GENERAL ANALYSIS FRAMEWORK
The study methodology was designed to capture potential near-term distribu-
tion system impacts in response to increased customer load. Assuming a near-term
planning horizon (1 to 5 years), only the characteristics of first-generation PEVs
are considered. Specifically, PEVs are modeled as simple loads whose character-
istics are mainly dictated by customer behavior; controlled dispatching or vehicle-
to-grid operations are not included (for a discussion of the latter, see Mangharam
2013). Growth in the base load is also not included because no particular planning
65
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66 FRONTIERS OF ENGINEERING
year is evaluated in any given scenario. Finally, only residential cusomers are
t
considered, as initial adopters are expected to charge at their residence.
The project included an assessment of PEV charging effects on specific cir-
cuits in a utility’s distribution system—typically one or two representative feeders
per utility—based on detailed simulations of distribution systems, customer load
characteristics, and potential electric vehicle (EV) penetration and charging
profiles. The results of the simulations were combined to develop summaries of
general concerns and to identify assets most likely to be at risk, conditions that
could require additional monitoring to avoid problems, and the impacts of differ-
ent charging profiles.
As part of a PEV distribution impact collaborative project, EPRI developed a
novel methodology to evaluate the impact of PEVs on distribution systems. The
study methodology was designed to capture potential distribution system impacts
in response to customer adoption of the new load type and was applied to 36
radial distribution feeders. The analytical framework was developed to evaluate
the impacts of PEVs on distribution system thermal loading, voltage regulation,
transformer loss of life, unbalance, distribution system losses, and harmonic dis-
tortion levels. These impacts are primarily determined by the assumed location of
PEVs throughout the distribution network, the time of day that PEVs are expected
to charge from the system, and the magnitude and duration of the charge cycle. In
order to determine both system-level and individual component–level impacts, the
framework provides for both deterministic and stochastic consideration of these
key spatial and temporal variables (Figure 1). Specifically, the analysis identifies
assets at risk of being affected and the likelihood and severity of impact.
• Asset deterministic analysis examines each asset’s capacity to serve
additional demand compared to the worst-case projected PEV demand
under the defined scenario. Each asset’s capacity is determined via the
circuit models and the projected PEV load is derived from probabilistic
evaluations of PEV characteristics and number of customers served from
each asset.
• Stochastic analysis projects likely impacts considering the full projected
diversity of the PEV charging through randomly generated system sce-
narios that model PEV charging and system response over a full calendar
year. PEV load location and temporal demands are randomly determined
using the PEV characteristic probability distributions discussed above.
Results from the simulation and analysis of hundreds of these randomly
generated cases provide indications of likely impacts and their severity.
MARKET PENETRATION AND CLUSTERING
The study is based on projected market penetration 1 to 5 years after PEV
commercialization. Although the total penetration is assumed to be small, possible
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IMPACT OF PLUG-IN ELECTRIC VEHICLES ON THE DISTRIBUTION GRID 67
Stochastic
Analysis
“System response given
full load PEV diversity”
I
Lik mpa
lih ct
d
eli ct
ke pa
oo
ho
Li Im
od
Component System
Deterministic
Sensitivities
Deterministic
“Asset abilities to “System response to
supply projected worst-case PEV
demand” charging scenarios”
FIGURE 1 System impact analysis framework.
high localized concentrations are possible. Using known distribution system circuit
information, PEV charge characteristics, and likely customer behaviors to construct
models of system conditions, the analysis framework 1
Maitra Figure considers the following
principal factors that define PEV loading on distribution systems: PEV market pen-
etration levels per utility customer class (residential, commercial); different PEV
charge spectrums (battery type, charger efficiency) and profiles; time profiles and
likely customer charging habits; and battery state of charge based on miles driven.
To evaluate circuits from 19 utility operating territories, PEV adoption levels
in the range of 2–25% were used. It’s important to note that, even for low overall
customer PEV adoption rates, based on system configuration and assumed cus-
tomer adoption probabilities PEV clusters will occur randomly throughout the
system, as shown in Figure 2. Each PEV is represented by a circle, and as PEVs
are introduced at the same location they are spaced like petals on a flower. Detailed
analysis from 36 circuits in 19 utility operating territories revealed a penetration
pattern that resembles sparse clusters that are nonuniform, centered on early
adopter neighborhoods. Several of these distribution system segments have older
homes and are capacity constrained. Higher penetration rates, of course, increase
the potential for larger and more numerous clusters. Although these clusters may
indicate an increased risk of higher than average loading levels, clustering alone
does not signify the likelihood of negative impact because other PEV load char-
acteristics must be taken into account.
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68 FRONTIERS OF ENGINEERING
Midwestern Circuit @ 4% Penetration
1,770 customers (13% Commercial, 87%
Residential) – Customers not shown
14.4 miles (above ground)
6.6 miles (underground)
r
302 Distribution Transformers
85 Charging PHEV/EVs
Substation
FIGURE 2 Sample daisy plots illustrating clustering at 4% penetration level. EV = electric
vehicle; PHEV = plug-in hybrid electric vehicle.
Mailtra Figure 2
CHARGING INFRASTRUCTURE
Bitmapped, eccept for legend
There are several ways to recharge PEVs at power levels ranging from less
than 1 kilowatt (kW) to as much as 250 kW and at charging times of less than
30 minutes to more than 24 hours. Most residential and public charging will occur
at power levels ranging from less than 1 kW to as much as 19.2 kW, with full
charge times of 3–8 hours.
Charging is grouped into two classifications based on whether the electric-
ity delivered is alternating current (AC) or direct current (DC). AC charging is
governed by SAE Recommended Practice J1772 and has two classification levels
in North America. Level 1 charging delivers 120 volts AC (VAC), and the electric
vehicle supply equipment (EVSE1) generally consists of a self-contained cordset
that terminates in a standard NEMA 5-15R plug compatible with any standard
120 volt household outlet. Level 2 charging delivers 208–240 VAC and requires
a permanently connected EVSE. Level 1 AC charging is generally limited to
1EVSE can be defined as “The conductors, including the ungrounded, grounded, and equipment
grounding conductors, the electric vehicle connectors, attachment plugs, and all other fittings, devices,
power outlets or apparatuses installed specifically for the purpose of delivering energy from the
premises wiring to the electric vehicle” (NEC 1996).
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IMPACT OF PLUG-IN ELECTRIC VEHICLES ON THE DISTRIBUTION GRID 69
1.44 kW, Level 2 can reach 19.2 kW, and most vehicles and installations use
3.3–6.6 kW. Both Level 1 and Level 2 charging use the same connector design
at the vehicle, and most vehicles can charge at either voltage through the same
charge port.
Instead of an onboard charge port, DC charging, often referred to as “fast
charging,” converts AC electricity to DC and directly charges the vehicle battery
through an offboard charging station. BEVs have been designed and tested for
DC charging at rates of 50–60 kW; the maximum charging power depends on the
battery chemistry and system design.
Most electric vehicles are expected to charge at power levels below 7 kW
(although the residential charging standard can reach levels of 19.2 kW, or
80 amps at 240 volts). PHEVs can easily recharge overnight at Level 1 (120 V,
1.2 kW) or Level 2 (240 V, 3.3 kW). The specific impacts on a feeder will depend
on the design and loading practices for various components of the feeder and
characteristics of PEVs in the area.
Charging Patterns
The timing of PEV charging can have either positive or negative impacts
on electric generation and transmission systems. A significant amount of PEV
charging coincident with the system peak would create a need for additional
generation, whereas charging performed consistently during off-peak hours could
reduce system costs.
Figure 3 compares the maximum charge powers for Level 1 (120 V) and
Level 2 (240 V) EVs to average peak summer demand for households in eight
US cities with different climates. Likely implementations of residential Level 2
charging range from a 15 amp circuit (12 amp continuous, 2.88 kW) to a 100 amp
circuit (80 amp continuous, 19.2 kW). Higher-capacity EVSE installations are
more likely to affect the local distribution system.
It is often assumed that EV charging could create a large load coincident with
the peak. However, according to data from the National Personal Transportation
Survey,2 vehicles do not all connect at the same time. Figure 4 shows the distri-
bution of home arrival times (on a 24-hour clock) for average American drivers.
Even during the peak hour of 5–6 PM (17–18 on the x-axis), only about 12% of
drivers arrive home.
Aggregate Feeder Loading Analysis
Characterization of PEV load diversity’s influence on the system requires
examination of the total additional loading expected at the substation (head of
2US Department of Transportation Federal Highway Administration, www.fhwa.dot.gov/ctpp/jtw/
contents.htm.
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70 FRONTIERS OF ENGINEERING
Average Peak Summer Demand Per Household (kW)
Tesla (240V@80A) 19.2
PEV (240V@32A) 7.7
PEV (240V@15A) 3.6
PEV (120V@12A) 1.4
San Francisco, CA
Feeders
3.0
Berkeley, CA 3.5
Hartford, CT 4.3
Dulles, VA 4.6
South Bend, IN 6.0
Fresno, CA 6.2
San Ramon, CA 6.5
Springdale, AR 7.7
FIGURE 3 Comparison of power consumption for AC levels 1 and 2 charging and for
average peak summer household demand in eight US cities. A, amp; kW, kilowatt; PEV,
plug-in electric vehicle; V, volt.
Maitra Figure 3
15.0% 100%
12.0% 80% Cumulative Frequency
Share of Sampled Vehicles
9.0% 60%
6.0% 40%
3.0% 20%
0.0% 0%
0 2 4 6 8 10 12 14 16 18 20 22
Home Arrival Hour
FIGURE 4 Home arrival time distribution.
Maitra Figure 4
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IMPACT OF PLUG-IN ELECTRIC VEHICLES ON THE DISTRIBUTION GRID 71
the feeder) for each circuit. There are uncertainties in the expected makeup of
PEVs, charging patterns served from each feeder, and customer habits, but they
can be reasonably bounded at the aggregate level for the substation transformer.
The study results showed that, based on typical daily driving statistics, the aver-
age energy delivered to a midsize sedan during a charge is 5–8 kWh and that for
different vehicle mixes the aggregate on-peak load will vary between 700 W and
1100 W per PEV.
Charging patterns at the aggregate level correlate with statistical driving
patterns, according to data from the National Household Transportation Survey
(NHTS; Vyas et al. 2009). Potential hours of PEV connection to the distribution
grid were derived from the likely residential customer home arrival times shown
in Figure 4. It is possible to estimate aggregate hourly demand on the substation
transformer by coupling NHTS statistics with daily customer driving distance pat-
terns, PEV types (e.g., Chevy Volt, Nissan Leaf, Ford Focus, Mitsubishi iMieV),
electrical charger characteristics, and different charging profiles that can be used
to control charging.
Figure 5 shows a plausible case for vehicle charging based on a fleet of
extended-range electric vehicles (E-REVs, as represented by the Chevy Volt;
30%), blended PEVs (represented by the Ford Escape; 50%), and BEVs (repre-
sented by the Nissan Leaf; 20%), all with 7.68 kW chargers that begin charging
at full power upon arriving home. Although the charging occurs at peak load,
C harge P ower P er Vehic le (kW)
1.40
Default Profile (Vehicle mix is 30% Chevy Volt, 50% Ford Escape, 20% Nissan Leaf)
Nissan Leaf
Chevy Volt
1.20
Charge Power Per Vehicle (KW)
1.00
0.80
0.60
0.40
0.20
0.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
FIGURE 5 Aggregate power demand for uncontrolled vehicle charging.
Maitra Figure 5
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72 FRONTIERS OF ENGINEERING
it uses about 0.7–1.0 kW per vehicle. Other vehicle mixes, with more PEVs or
lower-power chargers, will decrease the vehicle charging peak. Similarly, higher-
power chargers will increase the vehicle charging peak but the charging will
finish sooner.
SYSTEM IMPACTS
Correlating expected demand against asset capacity will provide a strong
indicator of the number and type of assets most at risk of exceeding their thermal
ratings due to PEV adoption. Peak capacity is determined from the peak load
power flow solution and each component’s specified thermal ratings. The EPRI
analysis shows that higher charging levels/rates (6.6 kW versus 3.3 kW versus
1.4 kW) have a greater impact on transformer capacity, as illustrated in Figure 6.
The calculated peak hour remaining capacities for an example circuit are
plotted in Figures 7 and 8 as a function of the number of customers served. Each
asset is evaluated against projected PEV demands and its remaining capacity
plotted as an individual point and sorted based on customers served and asset
class. Projected demands are superimposed as lines for the three market penetra-
tion levels (2%, 4%, or 20%) examined. The estimated maximum PEV demand
FIGURE 6 PEV charge levels have a stronger impact compared to charge time.
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IMPACT OF PLUG-IN ELECTRIC VEHICLES ON THE DISTRIBUTION GRID 73
1000
100
kVA/Customer
10
20%
1
8%
2%
Xfmr Laterals Primary
0.1
1 10 100 1000 10000
Number of Customers
FIGURE 7 Feeder asset thermal overload risk evaluation for 240V 30A PEV charging.
1000
Maitra Figure 7
100
kVA/Customer
10
240V 30A
1
120V 12A
Xfmr Laterals Primary
0.1
1 10 100 1000 10000
Number of Customers
FIGURE 8 Service transformer overload risk evaluation 120V 12A and 240V 30A PEV
charging.
Maitra Figure 8
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74 FRONTIERS OF ENGINEERING
is also plotted, permitting the ready identification of assets that are at risk of
impact. Assets whose remaining capacity falls above the projected demand are
unlikely to be affected by 2%, 4%, or 20% PEV market penetration. Given the
99.99% confidence level used for the P-test and the conservative construction of
the maximum projected demand lines, the probability of exceeding the thermal
ratings of these assets is less than 0.01%. Thus, assets above the maximum project
lines are unlikely to be impacted (where the asset’s remaining capacity exceeds
the projected PEV demand lines) and can be quickly identified for different PEV
penetration levels.
As PEV market penetration increases so does the potential for system impacts
(although such impacts cannot be discounted even for penetrations as low as 2%).
As expected, the number of assets that fall below the projected maximum PEV
demand line increases with the penetration level. Furthermore, the nature of the
asset capacities in relation to the maximum PEV demand lines clearly indicates
that the impact of PEV adoption will most likely first appear on service trans
formers. Not surprisingly, transformers with the lowest capacity per customer are
the most susceptible.
It is important to note that these circuit models, based on allocation of cus-
tomer load per transformer kVA, may limit the accuracy of the projections because
they do not capture innate variations in transformer loadings. Thus transformers
that may be heavily loaded in the field cannot be completely discounted from
being overloaded due to PEV charging. In the analysis described here, impact
likelihood is determined through stochastic simulations of the circuit operation
over a full calendar year for projected PEV penetration levels. In each case, PEVs
of specific types are randomly assigned to customer locations according to defined
probability distribution function (PDF) and an hourly demand profile for the year
is developed from the charge time and remaining charge PDFs. This process is
repeated for each penetration level and the simulated results are aggregated to
provide an indication of impact likelihood (the analysis also accounts for other
system impacts such as steady-state voltage changes and losses). The stochastic
analyses are also designed to enable identification of the particular system and
PEV conditions that result in a negative impact to the system or asset.
CONCLUSIONS
The results of the EPRI study show the following:
• The extent of system impacts depends on PEV penetration and charging
behaviors of PEV adopters.
• The expected aggregate addition to system peak loads is 700–1,000 watts
per PEV in a given utility territory.
• Short-term impacts for most utilities should be minimal and localized.
There is a possibility, however, of isolated and more severe impacts on
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IMPACT OF PLUG-IN ELECTRIC VEHICLES ON THE DISTRIBUTION GRID 75
some distribution transformers and secondary service lines, particularly
in neighborhoods with older distribution systems and underground
systems.
• PEV charge rate, or level, is the PEV characteristic that most influences
the overload risks posed to service transformers from PEV adoption.
Increased charge durations, due to larger battery sizes, can also impact
thermal aging.
• Each transformer’s remaining capacity per customer is one of the
s
trongest indicators of the potential risk that a transformer may exceed
its thermal ratings. This metric incorporates a number of key factors
including the transformer’s existing demand, thermal ratings, and the
number of customers served.
• Assets near the load are most susceptible to system overloads from PEV
clusters as the potential benefit of spatial diversity decreases.
• PEV clustering will occur randomly throughout a system. While it may
indicate an increased risk of higher than average loading levels, PEV
clustering alone does not signify the likelihood of negative impact as
other PEV load characteristics must also be taken into account.
• Transformers characterized by low capacity per customer are the most
likely to be affected by PEV adoption. In particular, transformers lower
than 25 kVA are expected to be the most susceptible to overloading as
they typically have lower amounts of capacity, which can be quickly
consumed by one or more PEVs.
REFERENCES
EPRI [Electric Power Research Institute]. 2012. Understanding the Grid Impacts of Plug-in Electric
Vehicles–Phase 1 Study. TR 1024101. December.
Maitra A, Kook K, Taylor J, Giumento A. 2009. Evaluation of PEV loading characteristics on Hydro-
Quebec’s distribution system operations. Electric Vehicle Symposium (EVS)24, Stavanger,
Norway, May 13-16. Available online at www.cars21.com/knowledge/papersView/47.
Mangharam R. 2013. The car and the cloud: Automotive architecture for 2020. In Frontiers of Engi-
neering: Reports on Leading-Edge Engineering from the 2012 Symposium. Washington, DC:
National Academies Press.
NEC [National Electrical Code]. 1996. NEC Handbook, NFPA 70. Article 625, Electrical Vehicle
Charging System Equipment, Section 625-2. Quincy MA: National Fire Protection Association.
Taylor J, Maitra A, Alexander M, Brooks D, Duvall M. 2009. Evaluation of the impact of PEV loading
on distribution system operations. IEEE Power Engineering Society, Calgary, July.
Vyas A, Wang M, Santini D, Elgowainy A. 2009. Analysis of the 2001 National Household Trans-
portation Survey in support of the PHEV project to evaluate impacts on electricity generation
and GHG emissions. Unpublished information.
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