By Yan Chen, University of South Carolina Learn more at www.opal-rt.com

1. Grid-connected Advanced Power
Electronic Systems
Real-time Volt/Var Optimization Scheme for
Distribution Systems with PV Integration
02-15-2017
Presenter Name: Yan Chen (On behalf of Dr. Benigni)

2. 2
2Date: 02/15/2017
Outline
 Impacts of PV Integration on Distribution Grids
 Solution: PV Inverter Control to Sustain High Quality of Service
 A Top-level Day-ahead Control that Optimizes Voltage Deviations
and Power Losses
 A Fast on-line Control that Compensates for PV Generation and
Load Variability
 Communication Network Aware Distributed Voltage Control
Algorithms
 Conclusion

3. 3
3Date: 02/15/2017
PV Impact On Distribution Grids
 Change in feeder voltage profiles, including voltage rise and unbalance
 Deteriorated power quality: PV-DG intermittency may lead to rapid fluctuations in
bus voltage magnitudes
 Frequent operation of voltage-control and regulation devices, such as on load tap
changers (OLTCs), line voltage regulators (VRs), and shunt capacitor banks
(SCBs)
 Change in electric losses, where relatively large reverse power flow may
increase power losses
0:00 8:00 16:00 24:00
Time
0.94
0.96
0.98
1
1.02
1.04
1.06
1.08
1.1
1.12
OptimalschedulingforTap1

4. 4
4Date: 02/15/2017
Day-ahead Coordinated Optimal Control
 Objectives: Determine how to optimally control the related electric elements
to minimize the voltage fluctuation and power losses with constraints on the
OLTC and SC operations.
 PV inverter
 On-load tap changer
 Shunt capacitor bank
 PV Inverter VAR control: When the PV generation is not at the maximum
level, the unused converter capability can be used for reactive
compensation.
( ) = ( ) − ( )
≤ ( )

5. 5
5Date: 02/15/2017
Optimal Control Problem
 Decision variables:
 Reactive power of PV inverter (continuous variables)
 OLTC tap position (discrete variables)
 SC switch state (Boolean variables)
 Objective function:
 Total voltage deviation
 Total power losses
 Constraints:
 Reactive power limit of PV inverter:
 Limit of node voltage magnitudes (ANSI C 84.1):
 Limit of tap positions of OLTC:
 Limit of the tap operations of OLTC within a day:
 Limit of the switch operations of shunt capacitor within a day:
[ , , ], 1,2,...t t t
pvQ Tap SC t T x
1 1 1
min ( (1 ) )
node brN NT
t t
i j
t i j
F w VD w PL
  
    
2 2 2 2
( ) ( )t t t
pv pv pv pv pvS P Q S P    
L t U
Tap Tap Tap 
max
TSC TSC
L t U
iV V V 
max
TTC TTC

6. 6
6Date: 02/15/2017
Overall Process
 Inputs:
 Forecasted PV Generation
 Forecasted Load Demand
 Distribution Network Information
 Optimization Process:
 Pattern Search Algorithm
 Genetic Algorithm
 Treated as a black-box model
 Outputs:
 Reactive power of PV inverters
 Tap position of OLTCs
 Switch State of Shunt Capacitors

7. 7
7Date: 02/15/2017
Case Study
IEEE 34 Node Test Feeder
Controlled Devices Location Decision variables
PV inverter Node 34
− − ≤ ≤ −
On-load tap changer Node7-8 , ±10 taps with 1% voltage regulation
per tap.
Shunt capacitor Node 27 , could be 0 (disconnected) or 1
(connected)

8. 8
8Date: 02/15/2017
Results and Discussions
Constraint function TTC=23
TSC=16
TTC=16
TSC=16
TTC=12
TSC=12
TTC=8
TSC=8
TTC=4
TSC=4
Objection function 49.32 52.70 56.51 75.69 85.71

9. 9
9Date: 02/15/2017
Discussion
 The performance of the day-ahead control method is affected by the forecast
errors.
 Solar PV output: errors caused by actual irradiance
 Cloud cover
 Aerosols and other atmospheric constituents
 Temperature
 Load demand:
 Temperature
 Random (stochastic) customer behavior
 Feeder outages

10. 10
10Date: 02/15/2017
Real-time Optimization
 We propose an online optimal reactive power control strategy to keep the total voltage
deviations and power losses to a minimum regardless of unpredicted changes.
 In order to reduce the additional “wear and tear” on the physical voltage control devices,
the tap position of the OLTC and the switch state of the SC are controlled according to the
day-ahead optimal control scheme.
 The reactive power of the PV is decided by the real-time system status.
Day D Day (D+1) t
Day-ahead scheduling for
OLTC, SC, and Qpv
PV output and load demand forecast
,
Real-time control of Qpv
Real-time system status

11. 11
11Date: 02/15/2017
Control Structure
Meter Meter
Meter
Meter
Meter
Meter
Measurements
(Pinj, Qinj, V, I)
Measurements
(Pinj, Qinj, V, I)
Control Center

12. 12
12Date: 02/15/2017
Control Structure
Meter Meter
Meter
Meter
Meter
Meter
Measurements
(Pinj, Qinj, V, I)
Measurements
(Pinj, Qinj, V, I)
State Estimation Optimization Algorithm

13. 13
13Date: 02/15/2017
Control Structure
Controller Controller
Controller
Controller
Controller
Controller
Control Signal
Control Signal
(Qpv)

14. 14
14Date: 02/15/2017
Control Structure
Meter Meter
Meter
Meter
Meter
Meter
Measurements
(Pinj, Qinj, V, I)
Measurements
(Pinj, Qinj, V, I)
Control Center

15. 15
15Date: 02/15/2017
Distributed Control Lab Instructure

16. 16
16Date: 02/15/2017
Controller Board
ODROID-U3+
Position Key Features
Upper layer • Low-cost, powerful computer
• Ease of programming
• Network capable
• ARM Quad-core 1.7 GHz CPU and 2GB
RAM. Xubuntu 13.10 Operation System
U3 I/O Shield
Position Key Features
Middle layer • 36 IO ports of GPIO/PWM/ADC
OPAL-U3-Shield
Position Key Features
Bottom layer • Contains level shift, amplification, and filter
circuitry for different signal requirements
between OPAL (-10V-10V) and U3 I/O Shield.
• Allows access to all IO ports on the U3 I/O
Shield

17. 17
17Date: 02/15/2017
Controller Board
ODROID-U3+
Position Key Features
Upper layer • Low-cost, powerful computer
• Ease of programming
• Network capable
• ARM Quad-core 1.7 GHz CPU and 2GB
RAM. Xubuntu 13.10 Operation System
U3 I/O Shield
Position Key Features
Middle layer • 36 IO ports of GPIO/PWM/ADC
OPAL-U3-Shield
Position Key Features
Bottom layer • Contains level shift, amplification, and filter
circuitry for different signal requirements
between OPAL (-10V-10V) and U3 I/O Shield.
• Allows access to all IO ports on the U3 I/O
Shield

18. 18
18Date: 02/15/2017
cRIO-9035 Embedded Controller
 Xilinx FPGA for rapid signal processing
 1.33 GHz Dual-Core allows wide range of computations
 Digital and analog I/O modules
 Analog I/O: 12-bit resolution bidirectional at 20 kS/s
 Digital I/O: 8 bidirectional channels at 10 MHz
 GPS module enables synchronous signal measurement
BA14

19. Slide 18
BA14 add a picture that show the full rack and add some detail on the IO modules
BENIGNI, ANDREA, 2/10/2017

20. 19
19Date: 02/15/2017
Network Emulator: Netropy N91
 Test the effect of WAN:
 Bandwidth
 Latency and jitter
 Loss
 Other impairment
 Congestion
 Corruption
 Queuing and Prioritization
 Applications:
 Throughput
 Responsiveness
 Quality

21. 20
20Date: 02/15/2017
Real-time Simulation of Distribution Grids
IEEE 34 Node Test Feeder
 4 SSN nodes, 5 subsystems
 Ts = 50us
IEEE 123 Node Test Feeder
 7 SSN nodes, 8 subsystems
 Ts = 50us

22. 21
21Date: 02/15/2017
Model Components
 RT-LAB overview
 ARTEMis State-Space Nodal (SSN)
The SSN algorithm creates virtual state-space partitions of the network that are
simultaneously solved using a nodal method at the partition points of connection. The
partitions can be solved in parallel on different cores of a PC without delays.

23. 22
22Date: 02/15/2017
Test Setup
HIL structure

24. 23
23Date: 02/15/2017
Case Study

25. 24
24Date: 02/15/2017
Case Study
Meter Meter
Meter
Meter
Meter
Meter
Measurements
(Pinj, Qinj, V, I)
Measurements
(Pinj, Qinj, V, I)
State Estimation Optimization Algorithm
Qpv

26. 25
25Date: 02/15/2017
Case Study
To evaluate the ANN approach, 17,520
samplings are generated from one
year’s historical record, and 15%, 15%,
10%, and 5% white noise is added to
the domestic load, commercial load,
industrial load and street light load,
respectively.
The comparison of the forecasted
results (in red curves) and the real
measurements (in blue curves) of the
PV output and the load demand.

27. 26
26Date: 02/15/2017
Real-time Qpv Control Results
The real-time reactive power control is applied to correct the forecast errors of PV
output and load demand. The overall objective function value is decreased from 102.30
to 89.82. The voltage magnitudes are more centralized to 1 PU. The power quality
improvement is apparent when there is dramatic uncertainty of the PV output.

28. 27
27Date: 02/15/2017
Distributed Control Algorithm
 A modified IEEE-123 power distribution system is simulated on OPAL-RT in
real time
 10 CompactRIO embedded controllers connected at various points in the
grid measure voltage phasors to determine reactive power flow
 By communicating with one another, these controllers attempt to minimize
transmission losses by injecting and absorbing reactive power
 The control algorithm adapts to the network conditions generated by the
network emulator; using either gossip-like or F-DORPF algorithm

29. 28
28Date: 02/15/2017
Distributed Control Algorithm

30. 29
29Date: 02/15/2017
Conclusions
 The increasing penetration of distributed and renewable energy
resources introduces challenges to the distribution systems operation
and control
 Real-time simulation (of power and communication networks) and
Hardware In the Loop simulation are fundamental tools for the design
and testing of innovative control solutions

31. 30
30Date: 02/15/2017
Thanks for your attention
Questions?
Dr. Andrea Benigni
Department of Electrical Engineering
benignia@cec.sc.edu
Yan Chen, Ph.D. student
Department of Electrical Engineering
yc2@email.sc.edu