1) The document summarizes a presentation on co-simulating plug-in electric vehicle (PEV) coordination schemes over a fiber-wireless (FiWi) smart grid communications infrastructure. 2) Simulation results showed that uncoordinated PEV charging can cause critical voltage fluctuations as penetration levels increase, while coordinated schemes using a proactive algorithm distributed the load better. 3) A reactive control algorithm was also proposed to quickly unplug PEVs and solve critical voltage fluctuations detected by sensors communicating with a distribution management system over the FiWi network.

2. 38th Annual Conference of the IEEE Industrial Electronics Society (IECON 2012), Montréal, QC, CA.
Co-Simulation of PEV Coordination Schemes
over a FiWi Smart Grid Communications
Infrastructure
Presented by:
Martin Lévesque
INRS (Québec, Canada)
PhD student
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3. Outline
• Introduction
• Impact study of uncoordinated PEV charging
• Proactive and reactive coordination schemes
• Communications and power distribution network co-simulation
• Co-simulation results
• Conclusions
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4. Introduction to Smart Grid Current electrical grid
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Current electrical grid:
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One-way flow of energy.
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Exchange of information from generators
to substations.
●
Cannot handle large-scale deployment of
distributed renewable energy ressources
and/or electric vehicles.
Smart Grid
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Smart Grid :
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Two-way flow of energy and information.
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Monitoring and control of the grid using
communications and sensor technologies.
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Sources: http://www.smartgrid.epri.com/Demo.aspx
http://www.incontext.indiana.edu/2010/july-aug/article3.asp

5. Is uncoordinated PEV charging a problem ?
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In some works [1], it was found that PEV charging can significantly
stress the distribution network on a local scale.
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While in some other distribution systems [2], little negative impact was
observed.
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Thus, we first look into uncoordinated PEV charging to verify their
findings.
[1]
[2]
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6. Configurations - Topology
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Widely used IEEE 13-Node
distribution test feeder.
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Substation steps down the
115kV transmission network
to 4.16kV.
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Each node in the feeder
aggregates one or more low
voltage residential
network(s).
●
Total number of 18 Fig. : Single line diagram of the
residential networks, totalling modified IEEE 13-Node network.
342 customer households. 6

7. Configurations – Base
load and PEV modeling
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Each residential node
follows the Fig. 1 profile and
+/- 1 hour time shifting to
create random behaviors.
Fig. 1: Base load profile [1].
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PEVs arrive according to a
distribution of last trip ending
time, Fig. 2. Fig. 2: Distribution of household last trip ending time.
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Nissan LEAF specifications Based on the driving pattern data extracted from the
are used: National Household Travel Survey (NHTS), 2001.
[1]
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Battery: 24 kWh.
Charging rate: 1.8 kW/hour,
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●
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(North American 15A/120V outlet)

8. Uncoordinated PEVs
charging results
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For both uniform and non-
uniform distributions: as the
penetration level (PL) increases,
the daily voltage fluctuation
becomes more severe and below
the permissible limit.
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For non-uniform, problems start
when the PL is higher than 20%.
●
Requiring more peaking
power → Increase generation
costs.
Fig. 1: Voltage deviation for different PEV penetrations for
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Thus, coordination is required. uniform case and non-uniform case.
Non-uniform: Clusters 634 & 675 have a PL
two times higher compared to other clusters. 8

9. Coordinated PEVs
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Coordination solutions can
be grouped into two
categories:
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Proactive scheduling: PEVs are
scheduled to avoid critical voltage
fluctuations.
●
Reactive control: Fix the
problem when it occurs.
Fig.: Coordinated and uncoordinated PEV control
solutions.
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10. Proactive algorithms
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First fit: Start time of PEV Constraints:
(1) Voltage contraint.
charging is the first available
slot that does not violate (2) Maximum power demand.
(1,2).
Parameters:
●
Smart load management
(SLM) [1]: Find the slot (3)
minimizing (3,4) without
violating (1,2). (4)
[1]
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11. Reactive control
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Each residential node sends
notification (voltage, load, etc.)
packets to a central system,
the distribution management
system (DMS).
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The DMS schedules
according to an historical load
profile for the future load.
●
Algorithm 1: When the DMS
finds a voltage problem, it
successively un-plug PEVs to
fix the problem.
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12. Reactive control – Sensor type
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The reactive control mechanism is influenced by the sensor
type being used.
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Two sensor types:
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Data rate based: Measurements are sent periodically. As the rate increases,
the probability that an information is outdated decreases.
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Event based: Send a measurement only when the difference between 2
measurements is higher than a certain threshold.
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13. Communications
perspective
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Smart Grid communications
over a broadband access
network.
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EPON: high capacity (> 1
Gbps), reliable, low latency.
For urban areas.
●
WLAN technologies for the
ubiquity to extend the PON
Fig.: Über-FiWi architecture composed of an
coverage. EPON, next-generation WLAN, and sensors.
●
For rural areas, WiMAX can
be used. 13

14. On Co-simulation
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OMNeT++ is used for the
FiWi simulator.
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A power system layer is also
created by calling OpenDSS
for voltage, power, losses, etc.,
according to the load at each
node in the network.
●
Each residential node is
mapped to either an ONU or
WLAN node. Fig.: Power distribution network and FiWi
co-simulator.
●
Thus, both perspectives work
as an integrated system. 14

15. Proactive co-simulation
results
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As expected, with random
charging, problems are
observed during peak hours.
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SLM fully distributes the
load and fills the valley,
whereby first fit can increase
the peak duration.
●
Only 1-2 Mbps of
throughput was required with
an end-to-end delay of 1-8
ms.
Fig.: Proactive co-simulation results. 15
The penetration level is set to 66%, uniform distribution.

16. Reactive co-simulation
results
●
As the DMS profile could
not match the real load, we
add some sudden high
loads to create a stress
scenario.
●
As expected, critical
voltage fluctuations are
observed during these Fig.: Reactive co-simulation results.
sudden high loads.
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17. Reactive co-simulation
results
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The reactive control algorithm
is tested with data rate based
sensors.
●
Thus, as the data rate of
sensors increases, the critical
voltage duration decreases.
●
In this example, to have a
critical voltage duration lower
than 1 second, one need to set Fig.: Critical voltage duration as a function of the data
rate of sensors.
the data rate to at least 4
packets per second.
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18. Conclusions
• Uncoordinated charging of PEVs can cause critical voltage
fluctuations and overload utility assets as the penetration level
increases.
• To overcome these issues, we used a converged broadband access
network to coordinate PEVs using a proactive algorithm at the DMS.
• However, the information available at the DMS can mismatch the
actual voltage and load in the network.
• We proposed a reactive control algorithm to fix and un-plug PEVs to
quickly solve critical voltage fluctuations.
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19. Future work
• Coordinate not only PEVs, but also renewable energy sources.
• The considered broadband access network was not loaded. The
communications must take into account triple-play traffic (video, voice,
data).
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20. Questions ?
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