Laboratory Scale FACTS Controller Development
Mariesa CrowUniversity of Missouri-Rolla
Funded in part by the Energy Storage Systems Program of the U.S. Department Of Energy (DOE/ESS) through Sandia National Laboratories (SNL
Issues• Hardware-software co-design• Device placement and control
– Decentralized– Steady-state– Dynamic
• Cyber security• Reliability
33
vv
Transmission LineGenerationFACTS
Wind Power
Energy Storage
Solar Power
Energy Storage
FACTS Device
Distributed Decisions
Power Electronics
Communications
Sensing and monitoring Inputs
Power Electronics
Power Electronics
Distributed controland fault/attack detection
FACTS Interaction Laboratory
HIL Line
UPFC
Simulation
Engine
FACTS Power System Model
FACTS Power System
num_generatorsnum_lines
num_FACTSnum_busestotal_power
supply_power()reconfigure()
run_FACTS_placement()
Voltage stability, no overloads, flow balance,
availability
Service Provider(Utility)
providesinitiates
Contin-gency
typenamecause
affects : Event
Eve
nt R
ecep
tor
controls : Event
Attributes
Methods
Constraints
First Decomposition
FACTS Device
manipulates
setpoint
change_setpoint(newSetpoint)control_power_line()reconfigure()
flow balance
Neighbors limits, monitors
places
Placement
locations
compute_locations()
Placement is optimized
Power Transmission System
flowscapacitiesgenerationsloads
AG[For each line-capacity <= flow <= capacity{checked by FACTS}]------------------------------AG[For each bussum of lines.flow is 0.{checked by FACTS}]------------------------------AG[For each loadload is greater than or equal to 0.{checked by FACTS}]
supply_power()
affects : Event
uses
provides
initiates
affects :Event
senses
Eve
nt R
ecep
tor
FACTS Device
Power Transmission System
Placement AlgorithmG
G
ys ca p e
Hardware: physical buses & lines
SimulatedPowersystem
Unified Power Flow Controller (UPFC) FACTS
DSP Board
UPFC Power Electronics
change_switches()inject_voltage()inject_current()read_sensors()
line_powerline_voltage
power < max_limit
manipulates, sensesreads
controls
limits, monitors
Embedded Computer
CAN Bus
Interface board
controlsreads
Interfacesetpoint
capture_sensor_data()synchronize()get_line_power()change_setpoint(newSetpoint)compute_next_level_setpoint()DSP Board
EmbeddedComputer
UPFC PowerElectronics
Power Supply Sensor Board Current Sensors
DSP Board(Under the data cable)
Interface board
Simulated Power Transmission System
Simulation Engine(Load Flow) HIL Line
sensor_datapower_system_config
configure_simulation()accept_contingency()send_sensor_data()
set_HIL_line()compute_power()
generation_setpoint
change_HIL_Line_flow()
sets
affects : Event
sensesuses
manipulateslimits, monitors
Simulation Engine Hardware in the LoopLine
3332
31 30
35
80
78
747966 7
5
77
7672
8281
8683
8485
156 157 161162
vv
167165
15815915544
45160
166
163
5 11
6
8
9
1817
43
7
14
12 13
138139
15
19
16
112
114
115
118
119
103
107
108
110
102
104
109
142
376463
56153 145151
15213649
4847
146154
150149
143
4243
141140
50
57
230 kV345 kV500 kV
36
69
Simulation Engine
(multiprocessor)
UPFC
UPFCUPFC
Machine 1
D/A output
A/D input
UPFC 1
Programmable load
Machine 2
Machine 3
Power System Simulation Engine
Programmable load
Programmable load
UPFC 2
UPFC 3
D/A output
A/D input
D/A output
A/D input
Manual power flow control
Manual power flow control
Placement for steady-state performance
Placement for dynamic performance
Long term control (MF & SQP)
agent based long term control
Line overload cascading failure
scenarios
dynamic cascading failure scenarios
Closed-loop long term control
Cascading failure scenarios
Closed loop dynamic control
Closed loop multi-device
Dynamic nonlinear control
Visualization
prev
ious
wor
k
IEEE 118 Bus Test System
Manual Power Flow Control
4 5 6 7 8 9 10-460
-440
-420
-400
-380
-360
-340
-320
-300
-280
-260
time (seconds)
P1 a
nd P
2 (W)
10 11 12 13 14 15-0.75
-0.7
-0.65
-0.6
-0.55
-0.5
-0.45
time (secods)
P1 a
nd P
2 (pu)
10 11 12 13 14 150.9595
0.96
0.9605
0.961
0.9615
0.962
0.9625
0.963
0.9635
0.964
time (seconds)
bus
volta
ge (p
u)
10 11 12 13 14 15-0.08
-0.075
-0.07
-0.065
-0.06
-0.055
-0.05
time (seconds)
bus
angl
e (ra
d)
actual UPFC power flows
measured andfiltered intosimulation
simulatedbus voltages
& angles
Note induced low frequency oscillations
Manual power flow control
Placement for steady-state performance
Placement for dynamic performance
Long term control (MF & SQP)
agent based long term control
Line overload cascading failure
scenarios
dynamic cascading failure scenarios
Closed-loop long term control
Cascading failure scenarios
Closed loop dynamic control
Closed loop multi-device
Dynamic nonlinear control
Visualization
prev
ious
wor
k
Closed-loop long term control
• Which placements and settings yield the lowest PI over all possible contingencies?
Performance Index
∑ ∑
=
SLC Linesall i
i
SSPI
2
max
Si – Power flow on line (MVA)
Simax – Rating of the line
SLC – Single Line Contingency
PI distributes line loadings as higher loadings incur heavier penalties than lower loadings
-2-1012
-10
1
55
60
65
70
75
Facts 1 SettingsFacts 2 Settings
PI F
itnes
sOptimal Setting for a Single Contingency
Degrees of Freedom:
• Number of FACTS devices• Settings• Placements
across the set of all contingencies
24 6
24.8
25
25.2
25.4
25.6
25.8
26
Tota
l Ove
rload
ed P
ower
EA + SQPH + SQP
Evolutionary Algorithm vs.Pruned Brute Force (Heuristic)
Manual power flow control
Placement for steady-state performance
Placement for dynamic performance
Long term control (MF & SQP)
agent based long term control
Line overload cascading failure
scenarios
dynamic cascading failure scenarios
Closed-loop long term control
Cascading failure scenarios
Closed loop dynamic control
Closed loop multi-device
Dynamic nonlinear control
Visualization
prev
ious
wor
k
Cascading ScenarioOutage 48-49
45
46
W.Lancst Crooksvl
47
G69
68
G
G
49
66
65
Zanesvll48
50
Philo
MuskngumN
MuskngumS
67
G
Natrium
Kammer
44WMVernon
N.Newark
SpornW
Summerfl
62
64
37
34
36
NwLibrty39
40 41 42
43
S.Kenton
38
S.TiffinWest End Howard
Rockhill
EastLima
Sterling
Portsmth
Bellefnt 74 75SthPoint
CollCrnr23
GTannrsCk
Trenton
24
Portsmth
Hillsbro72
70
71
Sargents73
NPortsmt
WLima35
SpornE
54
51
Cascading ScenarioOutage 48-49
45
46
W.Lancst Crooksvl
47
G69
68
G
G
49
66
65
Zanesvll48
50
Philo
MuskngumN
MuskngumS
67
G
Natrium
Kammer
44WMVernon
N.Newark
SpornW
Summerfl
62
64
37
34
36
NwLibrty39
40 41 42
43
S.Kenton
38
S.TiffinWest End Howard
Rockhill
EastLima
Sterling
Portsmth
Bellefnt 74 75SthPoint
CollCrnr23
GTannrsCk
Trenton
24
Portsmth
Hillsbro72
70
71
Sargents73
NPortsmt
WLima35
SpornE
54
51
Cascading ScenarioOutage 48-49
45
46
W.Lancst Crooksvl
47
G69
68
G
G
49
66
65
Zanesvll48
50
Philo
MuskngumN
MuskngumS
67
G
Natrium
Kammer
44WMVernon
N.Newark
SpornW
Summerfl
62
64
37
34
36
NwLibrty39
40 41 42
43
S.Kenton
38
S.TiffinWest End Howard
Rockhill
EastLima
Sterling
Portsmth
Bellefnt 74 75SthPoint
CollCrnr23
GTannrsCk
Trenton
24
Portsmth
Hillsbro72
70
71
Sargents73
NPortsmt
WLima35
SpornE
54
51
Cascading ScenarioOutage 48-49
45
46
W.Lancst Crooksvl
47
G69
68
G
G
49
66
65
Zanesvll48
50
Philo
MuskngumN
MuskngumS
67
G
Natrium
Kammer
44WMVernon
N.Newark
SpornW
Summerfl
62
64
37
34
36
NwLibrty39
40 41 42
43
S.Kenton
38
S.TiffinWest End Howard
Rockhill
EastLima
Sterling
Portsmth
Bellefnt 74 75SthPoint
CollCrnr23
GTannrsCk
Trenton
24
Portsmth
Hillsbro72
70
71
Sargents73
NPortsmt
WLima35
SpornE
54
51
Manual power flow control
Placement for steady-state performance
Placement for dynamic performance
Long term control (MF & SQP)
agent based long term control
Line overload cascading failure
scenarios
dynamic cascading failure scenarios
Closed-loop long term control
Cascading failure scenarios
Closed loop dynamic control
Closed loop multi-device
Dynamic nonlinear control
Visualization
prev
ious
wor
k
0 5 10376
376.5
377
377.5ω 5
0 5 10376.4376.6376.8
377377.2377.4
ω 6
0 5 10376.4376.6376.8
377377.2377.4
ω 7
0 5 10376
376.5
377
377.5
378
ω 8
time (sec)time (sec)
(rad
/sec
)(r
ad/s
ec)
(rad
/sec
)(r
ad/s
ec)
cntrduncntrd
0 5 10376.4376.6376.8
377377.2377.4
cntrduncntrd
0 5 10376.4376.6376.8
377377.2377.4
376 4376.6376.8
377377.2377.4
376 4376.6376.8
377377.2377.4
ω 1(r
ad/s
ec)
ω 3(r
ad/s
ec)
ω 2(r
ad/s
ec)
ω 4(r
ad/s
ec)
Two devices – uncoordinated control design – local information only
Two devices – coordinated control design – local information only
0 5 10376.4
376.6
376.8
377
377.2
377.4
ω 1 (rad/s
ec)
cntrduncntrd
0 5 10376.4
376.6
376.8
377
377.2
377.4
ω 2 (rad/s
ec)0 5 10
376.4
376.6
376.8
377
377.2
377.4
ω 3 (rad/s
ec)
tiime (sec)0 5 10
376.4
376.6
376.8
377
377.2
377.4
ω 4 (rad/s
ec)time (sec)
Two devices – H∞ uncoordinated control design – tie line information only
Manual power flow control
Placement for steady-state performance
Placement for dynamic performance
Long term control (MF & SQP)
agent based long term control
Line overload cascading failure
scenarios
dynamic cascading failure scenarios
Closed-loop long term control
Cascading failure scenarios
Closed loop dynamic control
Closed loop multi-device
Dynamic nonlinear control
Visualization
prev
ious
wor
k
Seed Physical and Logical Intrusions
• Assertions describe the correctness of the control algorithms
• Software and hardware errors will be seeded into the FACTS network and the fault tolerance will be reported
• This behavior will be used to develop security policies for FACTS power systems
Visualization
Special Thanks
• Imre Gyuk - DOE Energy Storage Program• Stan Atcitty - Sandia National Laboratories• John Boyes - Sandia National Laboratories