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© 2019 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m
Alison O’Connell Project Engineer Distribution Operations and Planning E-mobility Integration Symposium October 14, 2019
Integrating Electric Vehicles to the Distribution Grid
© 2019 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m 2
Our Mission
Advancing safe, reliable, affordable and environmentally responsible electricity for society through global collaboration, thought leadership
and science & technology innovation
Who are EPRI?
Independent Objective, scientifically based results address reliability, efficiency, affordability, health, safety, and the environment
Nonprofit Chartered to serve the public benefit
Collaborative Bring together scientists, engineers, academic researchers, and industry experts
Key Aspects
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Incorporation in System Planning
© 2019 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m 4
Addressing EV in the Distribution Planning Process The process doesn’t change but becomes more complicated
Deployment
Alternative Evaluation & Selection
Alternative Identification
System Assessment
Study Definition
• Study objective • Assessment type • EV models
• Controlled charging • TOU
• Benefit-cost assessment • Reliability • Operational lifespan
• Forecasted adoption or profiles • Location in the system
LO 6.1
© 2019 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m 5
EV Demand and Profile Characterization
© 2019 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m 6
EV Example Single residence demand profile with and without electric vehicle charging
LO 6.2
EPRI Report: 1024101
How can this demand potentially impact the system? What factors can use to model this demand?
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Characterization of the New Load Type Demand and consumption characteristics
EV charge durations and magnitudes
LO 6.2
EPRI Report: 1024101
0
1
2
3
4
5
6
7
8
0 2 4 6 8 10 12
Char
ging
Dem
and
(kW
)
Hour
240 V - 30 A
240 V - 15 A
120 V - 12 A
Charger
Type #1 Type #3 Type #4
Battery Capacity (kWh)
4 9.6 9.6
Connection Voltage (V)
120 240 240
Max Charging Current (A) 12 15 30
Energy Consumption Rate (Wh/km) 100 124 124
Charging Efficiency (%)
90 90 90 What's going to drive when the charging will occur during the day?
© 2019 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m 8
0.0…
20.…
40.…
60.…
80.…
>100
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
02
46
810
1214
1618
2022
Miles Driven
Probability
Arrival Time
Characterization of Customer Usage
LO 6.2
EPRI Report: 1024101
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 4 8 12 16 20 24
KW/V
ehicl
e
Hour
Home Work Shopping
© 2019 Electric Power Research Institute, Inc. All rights reserved. w w w . e p r i . c o m 9
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 4 8 12 16 20 24
KW/V
ehicl
e
Hour
Home Work Shopping
Influence on System Demands
Customer Level (System Edge)
• EV load is highly variable and uncertain
• Assets typically reactively managed
Fast Chargers
• Defined load
• Interconnection study
EPRI Report: 1024101
LO 6.2
Substation and Primary
• Diversity in the EV load
• Longer mitigation lead times and higher mitigation costs
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Customer Level Impacts
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Voltage Impacts
0
20
40
60
80
100
120
200
205
210
215
220
225
07:00 11:00 15:00 19:00 23:00 03:00
Tota
l Fee
der
Char
ging
Dem
and
(kW
h)
Cust
omer
Vol
tage
(V)
Time
Voltage
Power
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Service Transformer Assessment
Impact Risk Drivers Remaining capacity Number of connected customers EV forecast EV fleet makeup
Fleet Assessment (EPRI Hotspotter) Individual transformer data & assessment Transformer fleet statistics
EPRI Report: 1024101
LO 6.3
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Example Fleet Impact Analysis Results Examination of risk factors and mitigation options
Probability of impact for each transformer ranked in descending order
LO 6.3
Assessment • Quantification of likely impacts • Translation into costs • Understand key impact drivers
Alternative Evaluation • Service design changes • Asset management • Control options • Proactive replacement
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Feeder Impact Assessment
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Feeder (Primary) Assessment
Assessment objective
• Peak Hour Load Flow • Deterministic “Worst-case”
expansion planning
• Quasi-Static Time Series Load Flow • Voltage regulation impacts • Energy and losses • Control design & evaluation
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 4 8 12 16 20 24
KW/V
ehicl
e
Hour
Home Work Shopping
LO 6.4
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Vehicle Fleet and DC Fast Charging Integration
Example illustration using EPRI’s DRIVE tool to identify distribution system’s ability to accommodate charging stations
Value in guiding new demands to areas with sufficient capacity?
LO 6.4
Interconnection studies
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Change in Planning Process and Analytics: Substation Transformer Thermal Aging
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Transformer Thermal Aging
Assessments using ANSI/IEEE Standard C57.91
• Evaluate winding temperatures
Data Needs
• Thermal Time Constants • Ambient Temperature • Losses (Loading)
+- θApLOSS
+
θHS
-
+
θΤΟ
−
RWO ROA
CW CO
0
10
20
30
40
50
60
70
80
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
Tem
pera
ture
(Cel
cius
)
Tran
sfor
mer
Los
ses
Time
Load Ambient Hot Spot Temperature
LO 6.5
What’s the impact of doing nothing? If we can change the EV loadshape does the benefit justify
the cost?
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0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
0 4 8 12 16 20 24
Tran
sfor
mer
Loa
ding
(pu)
Time (hours)
Thermal Aging with Increased Load
Baseline for comparing other profile changes
LO 6.5
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Transformer Thermal Aging Sensitivity
Uncontrolled Charging Controlled Charging
LO 6.5
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
0 4 8 12 16 20 24
Tran
sfor
mer
Loa
ding
(pu)
Time (hours)
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1 5 9 13 17 21
Tran
sfor
mer
Loa
ding
(pu)
Time (Hours)
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0,00
0,10
0,20
0,30
0,40
0,50
0,60
0 2 4 6 8 10 12 14 16 18 20
Cum
mul
ativ
e Lo
ss o
f Life
(%)
Year
Uncontrolled
Controlled
Potential Deferment Benefits
Benefit of upgrade deferment
Vs.
Cost to alter EV charging behavior
LO 6.5
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Need to Expand Beyond a Peak Demand Mindset
Electrification of all forms is altering load profiles Many traditional planning metrics and analysis assume traditional
shapes or changes
LO 6.5
𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐹𝐹𝑈𝑈𝐹𝐹𝑈𝑈𝑈𝑈𝐹𝐹 = 𝑚𝑚𝑈𝑈𝑚𝑚𝑈𝑈𝑚𝑚𝑚𝑚𝑚𝑚 𝑑𝑑𝑑𝑑𝑚𝑚𝑈𝑈𝑈𝑈𝑑𝑑
𝐹𝐹𝑈𝑈𝑈𝑈𝑑𝑑𝑑𝑑 𝑠𝑠𝑠𝑠𝑠𝑠𝑈𝑈𝑑𝑑𝑚𝑚 𝐹𝐹𝑈𝑈𝑐𝑐𝑈𝑈𝐹𝐹𝑈𝑈𝑈𝑈𝑠𝑠
𝑈𝑈𝑠𝑠𝑑𝑑 𝐹𝐹𝑈𝑈𝐹𝐹𝑈𝑈𝑈𝑈𝐹𝐹 = 𝑈𝑈𝑈𝑈𝑈𝑈𝑚𝑚𝑈𝑈𝑈𝑈 𝑑𝑑𝑈𝑈𝑑𝑑𝐹𝐹𝑒𝑒𝑠𝑠 𝑈𝑈𝑡𝐹𝐹𝑈𝑈𝑚𝑚𝑒𝑒𝑡𝑐𝑐𝑚𝑚𝑈𝑈 𝐹𝐹𝑈𝑈𝑈𝑈𝑑𝑑𝑑𝑑 𝐹𝐹𝑈𝑈𝑐𝑐𝑈𝑈𝐹𝐹𝑈𝑈𝑈𝑈𝑠𝑠 𝑚𝑚 8760
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Key Takeaways
EVs will not change the distribution planning process but may add more complexity at certain levels EV load models vary depending on the type of EV and level of the
distribution system being assessed EV impacts and necessary assessments vary between customer
level, feeder level and substation thermal loading Planning beyond the typical worst case peak demand snapshot
will likely be necessary
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Together…Shaping the Future of Electricity