2017 Atlanta Regional User Seminar - Using OPAL-RT Real-Time Simulation and HIL System in Power and energy Systems Research

Opal-RT Regional User Seminar
Using Opal-RT Real-Time Simulation and
HIL System in Power and Energy
Systems Research
Shuhui Li
Department of Electrical & Computer Engineering
The University of Alabama
Presented on
February 15, 2017
Atlanta, GA

Contents
1. Opal-RT system at UA
2. Solar energy conversion, generation and grid integration
3. Charging stations and transportation integration
4. Microgrid control and management
5. IPM motor control: EV and automation
6. NSF I/UCRC Center for efficient vehicles and sustainable
transportation systems (EV-STS)

Renewable Energy Systems Laboratory (RESyL)
RESyL
Science & Engineering Quad
Science and Engineering Quad

Opal-RT HIL and compatible hardware facilities
Target
computer #1
Target
computer #2
Target
computer #3
Target
computer #4
Hardware
interface #1
Hardware
Facilities

PCs connected to Opal-RT system
High-performance PC
Local area network
Opal-RT system

Contents
1. Opal-RT system at UA
2. Solar energy conversion, generation and grid integration
3. Charging stations and transportation integration
4. Microgrid control and management
5. IPM motor control: EV and automation
6. NSF I/UCR Center for efficient vehicles and sustainable
transportation systems (EV-STS)

Solar Photovoltaic Power Generation Systems
Grid connected PV system

Problems- Uneven Solar Irradiation Conditions
Clouds will cause a
shading problem

Central dc/ac and dc/dc converters
Overall
0 100 200 300 400 500
0
5
10
15
20
Vs (V)
Power(kW)
None
50%
100%
0 100 200 300 400 500
-300
-200
-100
0
100
Vs (V)
Power(W)
None
50%
100%
Shaded cell

String converter based PV system
Central dc/ac inverter and string dc/dc converters
String inverter configuration

Micro converter based PV system
dc/dc optimizers per module and a central inverter
Microinverter PV system

PV Module with Bypass Diode
Vs
Is
0 100 200 300 400 500
0
5
10
15
20
Vs (V)
Power(kW)
full-sun
n=1
n=2
n=3
n=4
n=6
n=9
n=12
n=18
n=36

Computational and Hardware Experiments
0 4 8 12 16 20 24
50
60
70
80
90
Temperature(F)
Time (Hour)
0 4 8 12 16 20 24
0
200
400
600
800
1000
SolarIrradiation(W/m2)
Temp
Irra
0.5 1 1.5 2 2.5
0
5
10
15
20
Time(s)
OutputPower(kW)
Max IC SF S-PI
CPU 1
CPU 3 CPU 2CPU 4

Grid-Connected PV and Energy Storage System
Pref
Qref

Artificial Neural Network for Control and Grid
Integration of Residential PV Systems
MPPT Control for dc/dc converter
ANN Control for dc/ac inverter

0 0.5 1 1.5 2 2.5
200
220
240
260
280
300
320
Time (s)
(a)dc-linkvoltage(V)
Vdc
0 0.5 1 1.5 2 2.5 3
Time (s)
Vdc
2.66 2.68 2.7 2.72 2.74
-20
0
20
40
Time (s)
(b)gridcurrent(A)
Igrid
2.66 2.68 2.7 2.72 2.74
Time (s)
Igrid
0 0.5 1 1.5 2 2.5
0
1k
2k
3k
Time (s)
(e)PVpower(W)
Ppv
0 0.5 1 1.5 2 2.5 3
Time (s)
Ppv
0 5 10 15
0
5
10
Harmonic order
(f)Mag(%ofFundamental)
THD=4.67%
0 5 10 15
Harmonic order
T HD=12.98%

Energy Storage for Grid Power Leveling
Help make energy
sources, whose power
output cannot be
controlled, smooth and
dispatchable.

Grid Integration: EV characteristics
Three different kinds of vehicles make up the EV fleet:
• Plug-in Hybrid Electric Vehicles (PHEVs)
– Hybrid vehicles that run on an internal combustion engine with batteries
that can be recharged by connecting a plug to an external power source.
– Larger batteries than traditional hybrid vehicles (e.g., 5-22 kWh).
– Unlimited driving range because of hybrid engines
• Extended Range Electric Vehicles (EREVs)
– Electric vehicles with relatively large batteries (e.g., 16-27 kWh)
– capable of relatively long all electric ranges (e.g., 40-60 miles).
– An on-board internal combustion engine provides an unlimited driving
range by recharging the battery when needed.
• Battery Electric Vehicles (BEVs)
– Pure electric vehicles with no internal combustion engine
– Require recharging at the end of their designed driving range.
– Have the highest all-electric range (e.g., 60-300 miles) and the largest
battery capacity (e.g., 25-35 kWh)

Grid Integration: Driving Characteristics
• Transportation data for U.S. driving patterns indicates
– 60% of domestic average daily driving is 30 miles or less
– Approximately 70% of driving is 40 miles or less.
– Upcoming EREVs:
• designed to drive 40 miles in all-electric mode.
• could accommodate 70% of driving in all-electric mode with a single
over-night charge.
• daytime charging using public charging or at-work charging
obviously extends vehicles’ effective all-electric driving ranges.
– BEVs have a limited driving range before extended charging is
required (e.g., a 40-60 mile battery, or even a 100-mile battery),
urban and close-in suburban areas are the ideal target market.

Grid Integration: Charging Characteristics
Charge level Utility Service Charge Power
(kW)
Time to
charge
AC Level 1 120V, 20A 1.44 > 8 hours
AC Level 2 240V, 15-30A 3.3 4 hours
DC Level 3 480V, 167A 50-70 20-50 min
• The total energy required to charge a battery, and the average energy required per
day, depend on the miles driven and the vehicle energy consumption per mile.
• Additional power may be required for accessories and air conditioning during
summer months.
• EVs will have onboard communications, computing capabilities, and the other
functionality in the near term that will enable them to be "smarter" than most
end-use loads.
3 levels charging schemes

Charging Stations with Other Renewables

Charging Stations with Built-in Energy Storage
• Lower power loss caused by converters
• Lower cost
• Efficient energy management

Real time simulation implementation
 Real-time model structure of the EDV charging station

Simulation results (1)
4 6 8 10 12 14 16 18 20
-50
-30
-10
10
30
50
Time (s)
Iref/Ibatt(A)
Iref
Ibatt
4 6 8 10 12 14 16 18 20
200
400
600
Time (s)
Vref/Vbatt(V)
Vref
Vbatt
4 6 8 10 12 14 16 18 20
69.5
70
70.5
Time (s)
StateofCharge(%)

Smart Transportation Grid Integration
• Battery remaining
capacity
• Charging station
locations
• Price information
• How much energy
charging station can
provide.

Microgrid in Power Distribution System
• A typical microgrid:
– a low-voltage distribution network
– distributed generation (DG) units
– distributed storage (DS) units
– controllable loads.
– Grid-tied mode, islanded mode
• Control and management of a
renewable-based microgrid:
– a renewable source level
– a microgrid central control
(MGCC) level
– a utility distribution management
system (DMS) level.

ANN-ADP vector controller at DG Level
 ADP: approximate dynamic programming
 ANN: artificial neural network trained to implement ADP-based
optimal control
 ANN-ADP: has potential to integrate optimal, predictive, PI, and PR
control advantages together

Types of DER (distributed energy resources)
inverters
• Grid-following inverter:
– PQ inverter DER: operates by injecting active and reactive power into
the microgrid
– PV inverter DER: operates by injecting active power into the microgrid
while simultaneously maintaining the PCC bus voltage at a desired value
• Grid-forming inverter:
– V-f inverter DER: Operates based on the conventional droop control
concept, which is a necessary requirement in the microgrid islanding
operating condition.
– Droop control:
   0 0 0 0,s s f ac ac ac ac V ac acf f r P P V V r Q Q     

A benchmark LV network with microgrid
• Grid connected
• Islanding

Tracking variable reference commends
(Ts=1ms)
4 6 8 10 12 14 16 18 20
4
6
8
10
12
WindSpeed(m/s)
Time (s)
0 2 4 6 8 10 12 14 16
-400
-200
0
200
Currents(A)
Time (sec)
Id Iq Id* Iq*

Connecting to the grid without synchronization
control
0.95 0.975 1 1.025 1.051.05
-300
-200
-100
0
100
200
300
abccurrents(A)
Time (sec)
1.95 1.975 2 2.025 2.05
-200
-100
0
100
200
abccurrents(A)
Time (sec)

Hardware Experiment System

Hardware Experiment Results
Grid d-axis current waveform dc link voltage
Grid q-axis current waveform Three-phase PCC voltage
0 20 40 60 80 100 120 140 160 180 200
30
40
50
60
70
Time (sec)
Voltage(V)
0 20 40 60 80 100 120 140 160 180 200
-0.5
0
0.5
1
1.5
Time (sec)
d-axiscurrent(A)
Id Id-ref
0 20 40 60 80 100 120 140 160 180 200
-2
-1
0
1
Time (sec)
q-axiscurrent(A)
Iq
Iq-ref
100.3 100.32 100.34 100.36 100.38 100.4100.4
-20
-10
0
10
20
Time (sec)
Voltage(V)

PM motor control with standard three-leg
inverter in EVs
Speed or torque commend

PI
e
*
sqv *
, ,a b cv
*
di
*
qi
di
qi




, ,a b ci
PI *
sdv
PI


*
r
r
0
0
1
0
d
sd sd sd sdq
s e e PM
sq sq sq sqd
q
d
Lv i i iLdt
R
v i i iLd
L
dt
 
            
              
          
  
 
Issues: Conventional Standard PMSM Control
Motor Controller

+
-
Vdc
PWM
PI
+
+
+
-
-
-
e
*
1v
*
1v
_sq refi
mech
*
mech
+
+
+
NN structure
Motor
Encoder
/d dt
mech
sai
sbi
sci
ej
e 
ej
e 
2/3
*
1, 1, 1a b cv
savsbvscv
2/3
,i 
PI
PI
mech *
rd
mech *
rd +
+
rd
PI
_sd refi-
+
+
_sd compv
_sq compv


+
-
-
Input
Hidden
Output
+
+
_sd refi
_sq refi
sde
sqe
sds
sqs
*
sdv
*
sqvsdi
Conventional standard
current-loop control
Outer speed-loop
control
Outer rotor flux control
P
sqi
sqi
sdi
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
tanh
1/Gain2
1/Gain
Input Preprocess
Output layer
V*sd
V*sqsde
sqe
sds
sqs
Hidden layer
NNstructure
NN controller
-
*
sqv
*
sdv
2/3
How to address the issues: ANN-ADP Solution
o Replace the conventional
controller by a Neural Network
Motor Controller (NNMC)
o Our NNMC uses Artificial
Intelligence techniques that
adapt quickly and efficiently in
real-time

Simulation for IPM Operating in Linear Over modulation
Conditions (d- and q-axis currents)

HIL Dyno System for Motor Control Evaluation

Operation of IPM Motors in Linear and Over
Modulation Regions
0 10 20 30 40 50 60 70 80 90 100
-10
-5
0
5
10
15
20
Time (A)
Current(A)
Ref
ADP
Conv
0 10 20 30 40 50 60 70 80 90 100
-10
-5
0
5
10
Time (Sec)
Current(A)
Ref ADP Conv
0 10 20 30 40 50 60 70 80 90 100
0.5
1
1.5
2
Time (Sec)
ModulationIndex
ADP
Conv
(a)
(b)
(c)

Investigate Applying Real-Time Simulation
in Robotics and Automation
From Solidworks to Simulink to Opal-RT

Focusing Areas of NSF I/UCRC Center
The Grid

2017 Atlanta Regional User Seminar - Using OPAL-RT Real-Time Simulation and HIL System in Power and energy Systems Research

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