This document outlines a real-time phasor simulation test-bed for secondary voltage control of power grids using wide-area measurements. It presents a model predictive control (MPC) approach to coordinate reactive power compensators and regulate voltages at sensitive buses in real-time. Simulation results on the IEEE 39-bus system show the MPC controller can effectively handle voltage regulation and tracking in the presence of disturbances like generator trips and line outages. Future work includes real-time validation of the control approach on larger scale networks using decentralized MPC to address computational limitations.

1. Real-time phasor simulation test-bed
for secondary voltage control of power
grids using wide-area measurements
By:
Arvin Morattab
Research Directors:
Prof. Ouassima Akhrif
Prof. Maarouf Saad

2. 2
Outline:
• Problem statement
• Secondary level Voltage Control
• Model Predictive Control
• Simulation test case
• Real-time testbed
• Simulation results
• Conclusion and future works
• Acknowledgement
• Q & A

3. 3
Overview of Hydro Quebec Network
Problem statement
* http://www.hydroquebec.com/fr/
Hydro Power Generation
39.5 GW
Wind Farms
659 MW
Load Area
• Large amount of
reactive power loss
• Volnurable to faults
and sudden load
changes
Long distance
between
generation and
load
• Based on off-line
calculations and pre-
defined schedules
• No coordination
between reactive
power compensators
Manual
operator based
voltage control
in secondary
level
Coordinated Secondary
Voltage Control

4. 4
Secondary level Voltage Control
• Voltage regulation and
tracking at sensitive buses
called pilot nodes using:
– Changing Vref of exciters on
the machines
– Changing Vref or Qref of
Static Var compensators
– Switching capacitor/inductor
banks
– …
• Consider constraints: voltage
of the buses and MVAR limits
• Time step of the controller:
10sec.
• Settling time (for 3%) in 1min

5. 5
Model Predictive Controller
1) State estimation based on measured output
and previous input/output set.
2) Use Identified LTI model to relate outputs for
next P future steps, to next M future values of
the inputs (M≤P). In this way we will have P
equation and M unknown. P and M are
prediction and control horizons respectively.
3) Solve Optimization problem with respect to
unknown inputs in presence of given constraints
4) Apply the first element of control signal
obtained from the optimization procedure.
5) Go to step 1 for next sampling time, k+1
MPC Algorithm:

6. 6
Simulation Test Case
• IEEE39 bus system
used as test case
• pilot buses to install
PMUs: buses 1,12 & 28
• Identify linear model of
the system: 12 states,
12 inputs, 3 outputs
• Controller designed
using MPC toolbox in
MATLAB Selected Pilot buses
Change of Vref
Change of Qref

7. 7
Real-time testbed
ePHASORsim
IEEE 39 bus
Network
ePHASORsim
IEEE 39 bus
Network
ePHASORsim
IEEE 39 bus
Network
++
MPC Controller
refQ
PNV
refV
refQ
PNV
ref
PNV
-
+
Code
Generation
C37.118SlaveC37.118Slave
C37.118MasterC37.118Master
RT
RT
RT
10 s 10 ms

8. 8
Simulation Results: Voltage Regulation
Trip generator: G3 is tripped at t=10s
0 50 100 150 200
0.85
0.9
0.95
1
1.05
V
PN
(p.u.)
time (sec.)
0 50 100 150 200
-0.01
0
0.01
0.02
0.03
V
ref
(p.u.)
time (sec.)
0 50 100 150 200
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
Q
ref
(p.u.)
time (sec.)

9. 9
Simulation Results: Voltage Regulation
Trip Line: Bus 8 to Bus 9
0 100 200 300 400 500
0.85
0.9
0.95
1
1.05
V
PN
(p.u.)
time (sec.)
0 100 200 300 400 500
-0.04
-0.02
0
0.02
0.04
V
ref
(p.u.)
time (sec.)
0 100 200 300 400 500
-0.2
-0.1
0
0.1
0.2
Q
ref
(p.u.)
time (sec.)

10. 10
Simulation Results: Voltage Tracking
Chang Vref of Bus12 from 0.923p.u to 0.94p.u
0 50 100 150 200 250 300
0.9
0.92
0.94
0.96
0.98
1
1.02
1.04
V
PN
(p.u.)
time (sec.)
0 50 100 150 200 250 300
-0.02
-0.01
0
0.01
0.02
0.03
0.04
V
ref
(p.u.)
time (sec.)
0 50 100 150 200 250 300
-0.3
-0.2
-0.1
0
0.1
Q
ref
(p.u.)
time (sec.)

11. 11
Conclusion and future works
• MPC controller can handle voltage regulation
and tracking at pilot buses in presence of
disturbances.
• A real-time validation is necessary for control
algorithms such as MPC who requires time for
calculations of the control input.
• For larger scale networks, Centralized MPC
computational burden may go beyond sample
time of the controller. Decentralized MPC
approach can be used as an alternative.

12. 12
Acknowledgement
• I would like to thank OPAL-RT technologies,
and specifically ePHASORsim team who
helped me to accomplish this project.
• THANK YOU ALL

13. 13
Question?