Grid Integration of Electric Vehicles
and Demand Response with
Customer Choices
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S. Shao, M. Pipattanasomporn, and S. Rahman, “Grid Integration of
Electric Vehicles and Demand Response with Customer Choices,”
IEEE Transactions on Smart Grid, vol. 3, no. 1, Mar. 2012.
S. Shao, M. Pipattanasomporn, and S. Rahman, “Demand Response
as a Load Shaping Tool in an Intelligent Grid with Electric Vehicles,”
IEEE Transactions on Smart Grid, vol. 2, no. 4, Dec. 2011.
BBCR Smart Grid Research Group Meeting
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Contributions
• Propose a demand response (DR) strategy to
accommodate electric vehicle (EV) charging
on a residential distribution circuit
• Goal
– Keep the peak demand (before EV penetration)
unchanged
• Consumer comfort indices are introduced
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Outline
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Motivations
Modeling of circuit load and EV charge profiles
Demand response strategy design
Consumer comfort indices
Case study
Concluding remarks
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Motivations
• The impact of EVs on electric power systems
cannot be overlooked.
• EV penetration may bring higher peak demand
• Majority of previous work regarding the
impact of EV penetration on electric power
systems focuses at the transmission level
• Recent research started to turn to the
distribution level
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Introduction
Transmission level
Distribution level
This picture is obtained from Wikipedia
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Motivations (cont.)
• Analysis of EV penetration into the
distribution network is quite extensive
• There is still a need to
– Take into consideration the vehicle driving
patterns
– Develop a demand response strategy that will
accommodate EV fleets
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Motivations (cont.)
• Demand response applications in industrial
and commercial sectors have been well
studied
• Residential demand response strategy taking
into account the consumer comfort still needs
an in-depth study
• There is the lack of indices to measure the
impacts of demand response on consumer
convenience
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Modeling of Distribution Circuit Load
• Hourly load curves of an average household
are based on the RELOAD [1] database
• Residential loads are classified by nine types:
– Space cooling, space heating, water heating, cloth
drying, cooking, refrigeration, freezer, lighting,
others
[1] RELOAD Database Documentation and Evaluation and Use in NEMS [Online].
Available: http://www.onlocationinc.com/LoadShapes-Reload2001.pdf
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Modeling of Distribution Circuit Load
(cont.)
• In the papers, the residential loads are
classified into two categories:
– Controllable: the loads that can be controlled
without noticeable impacts on consumer’s life
style
• Space cooling/heating, water heating, cloth drying
– Critical: the loads that are either very important
or cannot be controlled
• Others
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Modeling of EV Fleet Charge
• A survey [2] indicates that the EV plug-in time
(coming home time) is close to a normal
distribution curve
• The papers use a normal probability
distribution function to describe the EV fleet
plug-in time
[2] J. Taylor, A. Maitra, M. Alexander, D. Brooks, and M. Duvall, “Evaluation of the
impact of plug-in electric vehicle loading on distribution system operations,” in Proc.
IEEE PES Gen. Meet., Jul. 26–30, 2009.
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Modeling of EV Fleet Charge (cont.)
(EV plug in time)
Mean at 6pm and the variance of 1 h
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Modeling of EV Fleet Charge (cont.)
• A study [3] uses the Monte Carlo method to
simulate the daily driving distance for each EV
in the distribution circuit
[3] National Household Travel Survey Oakridge National Laboratory, 2001 [Online].
Available: http://nhts.ornl.gov/index.shtml
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Modeling of EV Fleet Charge (cont.)
• Battery usable capacities and charging power
requirements (kW)
* Recommended charging rate
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Demand Response Strategy Design
• The proposed DR strategy is designed in two
layers
Neighborhood Area Network (NAN)
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Home Area Network (HAN)
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Demand Limit Allocation for Each
House at NAN
• Original peak load (before EV penetration)
should be set as the demand limit for whole
circuit
1. NAN control center sorts all reported demand
within a distribution circuit
2. Household demand limit (DLi) is set at the point
where the summation of all household to be
served is equal to or less than the original peak
load
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Demand Limit Allocation for Each
House at NAN (cont.)
(Original peak load)
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Optimization Problem
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Demand Response Strategy in HAN
Step1) Customers set the load priority for each
appliance
Step2) Customers perform preference settings
for each appliance
(highest)
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Demand Response Strategy in HAN
(cont.)
• Step3) HAN control center will compare the
total household power consumption (ph,i) with
the demand limit (DLi)
– If ph,i > DLi, demand response will be performed
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Consumer Comfort Indices
• Evaluate DR impacts on consumer comfort
levels
• Indices are defined based on severity, scale,
and duration of convenience violations for
each controllable appliance
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Severity Indices
• Measure how severely the consumer comfort
levels are violated
• Based on the maximum percentage deviation
from the original settings
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Severity Indices (cont.)
• Severity indices for HVACs
– The largest temperature deviation in percentage
taking into account all homes in a distribution
circuit
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Severity Indices (cont.)
• Severity indices for water heater (temperature)
• Severity indices for clothes dryer (time delay)
• Severity indices for electric vehicles (time delay)
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Scale Indices
• Measure the number of consumers whose
comfort levels are violated as a percentage of a
total household
• Scale indices for HVACs
– nHVAC: the number of homes with the temperature out
of preset comfort ranges in each time slot
– N: the total number of consumers
– ORAC: ownership rate of HVACs
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Scale Indices (cont.)
• Scale indices for water heaters
• Scale indices for clothes dryers
• Scale indices for electric vehicles
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Duration Indices
• Describe the length of the inconvenient period
for HVAC and water heater
• Duration indices for HVACs
• Duration indices for water heaters
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Case Study
• Circuit 9 in the Virginia
Tech Electric Service
(VTES) area is taken as a
case study
• 780 homes
• 780*1.9 = 1482 vehicles
• EV penetration levels:
– 50 (3.3%) EVs and 100
(6.6%) EVs
• EV charging model
– Mix of 40% Chevy Volt,
40% Nissan LEAF, and 20%
Tesla Roadster
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Results
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Summer
load profiles
50 EVs
100 EVs
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50 EVs
Winter
load profiles
100 EVs
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Consumer Comfort Indices (Summer)
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Consumer Comfort Indices (Winter)
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Concluding Remarks
• EV fleet charge profiles are modeled based on
driving distances and battery sizes
• DR strategy provides the utility with unchanged
peak demand to avoid distribution circuit
upgrade
• Maintain the same peak load with higher EV
penetration levels may impact the consumer’s
convenience
• Utilities can use the consumer comfort indices to
estimate the capability of demand response
programs
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Thank you!
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