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NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking
Deck in a Smart Grid Environment
Preetika Kulshrestha, Student Member, IEEE, Lei Wang, Student Member, IEEE,
Mo-Yuen Chow, Fellow, IEEE and Srdjan Lukic, Member, IEEE
North Carolina State University
Raleigh, NC
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 2
Outline
� Introduction
� System architecture
� Component description
� System simulator
� Sample system simulation
� Future work
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 3
Emergence of the “Smart Grid”
Optimization of power delivery - Capacity to deliver efficiently,
reliably and intelligently
Features
� Decentralization of control
� Services customized to user’s needs
� Use of energy efficient systems
� Rapid reconfiguration
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 4
Introduction to PHEVs
� HEV with larger battery pack
� Can be charged from standard wall outlet
� 40 mile all-electric range (Chevy Volt)
Benefits
� Reduction in GHG emissions
� Reduction on oil dependence
Time of Day
MW
Lo
ad
Off peak charging
Load leveling during extreme load events
Source: Prometheus Institute Time of Day
MW
Lo
ad
Off peak charging
Load leveling during extreme load events
Source: Prometheus Institute
• Load leveling
• Lower cost
Potential Synergistic
Relation
A cluster of vehicles is
a controllable load for the grid
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 5
Opportunities and Challenges
Opportunities
• US fleet’s 176 million light vehicles = power capacity of 19.5TW= 24 x power
capacity of the electric generation system.
• PHEV penetration by 2050 – 62% of the US fleet (EPRI prediction)
Challenges
• Potential load of 1000 cars => 4 MW load
• Potential dangers => Voltage instability and blackouts
• Infrastructure
• Need of an underlying framework to enable PHEV integration
A Solution
Intelligent Energy Management at a Municipal Parking Deck
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 6
Related Work
� J. Tomic and W. Kempton, “Using fleets of electric-drive vehicles for grid support”, J. Power Sources, vol. 168, issue 2, 2007
� M. Duvall and E. Knipping, “Environmental assessment of Plug-in Hybrid Electric vehicles”, EPRI, July 2007
� Hutson, G. K. Venayagamoorthy, K. A. Corzine, “Intelligent Scheduling of Hybrid and Electric Vehicle Storage Capacity in a Parking Lot for Profit Maximization in Grid Power Transactions”, in proc. IEEE Energy2030, Atlanta, GA, 2008
� S. B. Pollack et al, patent title “User interface and user control in a power aggregation system for distributed electric resources”, IPC8 Class: AG01R2106FI, USPC Class: 702 62
� GridPoint: http://www.gridpoint.com/
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 7
System Architecture
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 8
Information Flow
• Power Allocated
• Rate of charge
• Other control messages
iEMSOptimization
Pricing
Available power
Utility
• Time of availability
• Type of charge
• Pricing preferences
• Current state of charge
• Power consumed
• …..
Data Acquisition
(DSP, FPGA etc.)
Communication Medium: Wi-Fi, Bluetooth, Satellite ZigBee etc.
User Profile Entry
Vehicle 1
Vehicle 2
Vehicle 3
.
.
.
.
Vehicle n
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 9
System - Component Description
States
� Idle: There is no activity, the load may be waiting for a control action or it may have completed charging the battery.
� Data Acquisition: The data is acquired from the battery and the user.
� Communication: Data is communicated to controller.
� Charging: Battery charging is in progress.
� Error: There is an error in the system and system operation is halted until error is resolved. •A - Idle
•B - Data Acquisition
•C - Communication
•D - Charging
•E - StopState transitions
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 10
States
• Communication: Inform the loads of power allocated/listen for signals
• Optimize: Calculation of power allocation when:
• There is a change in utility power.
• A load has plugged-in/out.
• Periodically, after sampling the instantaneous power consumed by loads.
System - Component Description
iEMS flow chart
A
B
Plug- in/ Update
Request,
Tsample
•A - Communicate
•B- OptimizeOptimize on power
subject to constraints
Data from LoadsTime of Availability
User Preferences
Initial State of
Charge
Information
from UtilityTotal Power
Available
Pricing
Allocate Power
to loads
Data Acquisition
Sample
instantaneous power
and SOC of load
T=Tsample or
request received
for update or new
load has plugged
in?
Yes
Initialize
State transitions
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 11
System - Component Description
Functions
� Periodically inform the iEMS about the power available and pricing information
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 12
iEMS - System Simulator
powergui
Discrete,
Ts = 5 s.
iEMS
Input from car I
Input from car II
Input from car III
Input from car IV
Input from car V
Power
Car I
Car II
Car III
Car IV
Car V
Utility
Power
Station V
Input Rate and Allocated Power
Notify Plug in
Vehicle Information
Completed
VE
UpdateV
Station IV
Input Rate and Allocated Power
Notify Plug in
Vehicle Information
Completed
VD
UpdateIV
Station III
Input Rate and Allocated Power
Notify Plug in
Vehicle Information
Completed
VC
UpdateIII
Station II
Input Rate and Allocated Power
Notify Plug in
Vehicle Information
Completed
VB
UpdateII
Station I
Input Rate and Allocated Power
Notify Plug in
Vehicle Information
Completed
VA
UpdateI
Simulation Time
time
Clock
Continuous Dynamics
Discrete
Hybrid System
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 13
Objective function:
wi(k): the priority assigned for to vehicle i at time step k
Priorities are assigned based on capacity required
and time remaining
Sample System Simulation
( )= +∑∑max ( ) ( )i ip
j i
J k w k SoC k j
12:00 am11:00 am10:00 am9:00 am
Tplug out = 11:36 am
SoCin = 40%
D
Tplug out = 11:22 am
SoCin = 10%
B
Tplug out = 1:04 pm
SoCin = 50%
E
Tplug out = 10:40 am
SoCin = 70%
A
Tplug out = 10:17 amSoCin = 0%
C
0 0.5 1 1.5 2 2.5 3
x 104
-2000
0
2000
4000
6000
8000
10000
12000Plot of power consumption of five cars
Time (s)
Pow
er
(W)
PowerA
PowerB
PowerC
PowerD
PowerE
Total Power
Available Power
8:00 am 1:00 pm
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 14
Monte Carlo Simulation
Simulation Parameters
State of Charge at plug-in: Uniform random number between 10% and 75%
Time of Availability: Uniform random number between 0.5 and 2 hours
Time of Plug-in: Uniform random number between 0 and 2 hours
Simulation Run Time: 4 hours
Battery Capacity: Uniformly distributed between 6 Ah and 15 Ah
Number of times the simulation was run for each algorithm: 100
-0.2 0 0.2 0.4 0.6 0.8 1 1.20
0.05
0.1
0.15
0.2
0.25
0.3
0.35
SOC at Plug out
Perc
enta
ge o
f V
ehic
les (
%)
State of Charge at Plug out - Optimal Allocation for SoC Maximization
40
8.0%
32
6.4%
2
0.4%
Number of vehicles leaving with SoC 35% or lower
67.6%
(339)
69.6%
(348)
81.8%
(409)
Percentage of vehicles leaving with SoC 55% or higher
Equal Priority Allocation
Dynamic Priority Allocation
Optimal Allocation for SoC Maximization
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 15
Conclusions
� This paper proposes an iEMS for managing power at a parking deck
� System components, functions and behavior are outlined
� A simulator (test-bed) is developed to simulate the real world scenario
� The simulator will contribute towards evaluation of varied scenarios and iEMS algorithms
� Optimization on a chosen objective is formulated and simulation results presented
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 16
Future Work
• Exploration of different objectives for optimization
• Extension of the problem to multi-objective optimization and incorporation
of additional constraints
• Network in the Loop iEMS – performance evaluation with communication delay, packet drop, and signal strength
• Decision on optimal sampling time
• Extension of the concept to distributed control
• Real world implementation and demonstration of the iEMS
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 17
Acknowledgement
This work was partially supported by the National Science
Foundation (NSF) under Award Number EEC-08212121
NC STATE UNIVERSITY
Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 18
THANK YOU!