1. An Approach To Improving The Physical And Cyber Security Of A Bulk Power System With FACTS
Natural
Faults
Mariesa Crow & Bruce McMillin
School of Materials, Energy & Earth Resources
Department of Computer Science
University of Missouri-Rolla
Stan Atcitty
Power Sources Development Department
Sandia National Laboratory
FACTS
FACTS
Physical Attack
Funded through the DOE
Energy Storage Program

2. Problem Motivation Prevent Cascading failures: Causes 2003 Blackout
Physical & Cyber contingencies
Deliberate disruption
Hackers
Terrorist Activity

3. Proposed Solution Flexible AC Transmission Systems (FACTS)
Power Electronic Controllers
Means to modify the power flow through a particular transmission corridor
Integration with energy storage systems

4. US FACTS Installations
AEP/ Unified Power Flow Controller /100 MVA/ EPRI
NYPA/ Convertible Static Compensator/ 200 MVA
Vermont Electric/ STATCOM/ 130 MVA/ Mitsubishi
San Diego G&E/
STATCOM/100 MVA
Mitsubishi
Northeast Utilities/ STATCOM/ 150 MVA/ Areva (Alstom)
TVA
STATCOM/ 100MVA
EPRI
Eagle Pass (Texas)
Back-to-back HVDC
37 MVA/ ABB
Austin Energy
STATCOM/ 100MVA
ABB
CSWS (Texas)
STATCOM/ 150 MVA / W-Siemens

5. Decentralized Infrastructures
Communication and coordination
Scheduling - Distributed Long-Term control
Interaction – Local Dynamic control
Vulnerabilities of the combined physical/ cyber system
Recovery and protection from physical faults and/or cyber attacks and/or human error

6. Identify cascading failure scenarios for test systems

7. Cascading Scenario Outage 48-49
West End
S.Tiffin
Howard 40 41 42
Cascading Scenario
Outage 48-49
NwLibrty 39
EastLima
WMVernon 37 44 38 43 34
N.Newark 54
Rockhill
S.Kenton 50 45
Zanesvll 51 48 35 36
Sterling
WLima
Philo 46 47 49 G
Summerfl 67
W.Lancst
Crooksvl 66 62
MuskngumN
Natrium G
Sargents 65
Trenton 73 G 64
MuskngumS
Kammer 23
CollCrnr 24 G 68
SpornE 69 72
Hillsbro
SpornW 71
NPortsmt
TannrsCk G
Portsmth
Portsmth 70
Bellefnt 74 75
SthPoint

8. Cascading Scenario Outage 48-49
West End
S.Tiffin
Howard 40 41 42
Cascading Scenario
Outage 48-49
NwLibrty 39
EastLima
WMVernon 37 44 38 43 34
N.Newark 54
Rockhill
S.Kenton 50 45
Zanesvll 51 48 35 36
Sterling
WLima
Philo 46 47 49 G
Summerfl 67
W.Lancst
Crooksvl 66 62
MuskngumN
Natrium G
Sargents 65
Trenton 73 G 64
MuskngumS
Kammer 23
CollCrnr 24 G 68
SpornE 69 72
Hillsbro
SpornW 71
NPortsmt
TannrsCk G
Portsmth
Portsmth 70
Bellefnt 74 75
SthPoint

9. Cascading Scenario Outage 48-49
West End
S.Tiffin
Howard 40 41 42
Cascading Scenario
Outage 48-49
NwLibrty 39
EastLima
WMVernon 37 44 38 43 34
N.Newark 54
Rockhill
S.Kenton 50 45
Zanesvll 51 48 35 36
Sterling
WLima
Philo 46 47 49 G
Summerfl 67
W.Lancst
Crooksvl 66 62
MuskngumN
Natrium G
Sargents 65
Trenton 73 G 64
MuskngumS
Kammer 23
CollCrnr 24 G 68
SpornE 69 72
Hillsbro
SpornW 71
NPortsmt
TannrsCk G
Portsmth
Portsmth 70
Bellefnt 74 75
SthPoint

10. Cascading Scenario Outage 48-49
West End
S.Tiffin
Howard 40 41 42
Cascading Scenario
Outage 48-49
NwLibrty 39
EastLima
WMVernon 37 44 38 43 34
N.Newark 54
Rockhill
S.Kenton 50 45
Zanesvll 51 48 35 36
Sterling
WLima
Philo 46 47 49 G
Summerfl 67
W.Lancst
Crooksvl 66 62
MuskngumN
Natrium G
Sargents 65
Trenton 73 G 64
MuskngumS
Kammer 23
CollCrnr 24 G 68
SpornE 69 72
Hillsbro
SpornW 71
NPortsmt
TannrsCk G
Portsmth
Portsmth 70
Bellefnt 74 75
SthPoint

11. FACTS Placement and Control

12. FACTS Control
Distributed Long-Term control algorithms for FACTS settings
Run by each processor in each FACTS
Alternatives
Max-flow algorithms
Local optimizations
Agent-based framework
Assessment
Reduction of Overloads
Computability

13. FACTS Placement Placement
Place few FACTS in a large network for maximum benefit
Evolutionary Algorithms (EAs) will be used to place FACTS devices in the network

14. Performance Index Metric
Gradient Descent on PI Metric vs. Maximum Flow

15. FACTS Interaction Laboratory (FIL)

16. FIL Overview Construct a Laboratory System to Study and Mitigate
Cascading Failures
Deleterious effects of interacting power control devices
Cyber Vulnerabilities
Hardware in the Loop (HIL)
Real-time Simulation Engine
Simulate Existing Power Systems
Inject Simulated Faults
Interconnected laboratory-scale UPFC FACTS Device
Measure actual device interaction

17. FACTS Interaction Laboratory Architecture33 32 31 30 35 80 78 74 79 66 75 77 76 72 82 81 86 83 84 85
156
157
161
162 v
167
165
158
159
155 44 45
160
166
163 5 11 6 8 9 18 17 4 3 7 14 12 13
138
139
147 15 19 16
112
114
115
118
119
103
107
108
110
102
104
109
142 37 64 63 56
153
145
151
152
136 49 48 47
146
154
150
149
143 42 43
141
140 50 57
230 kV
345 kV
500 kV 36 69
FACTS/ESS
A/D D/A
10 KVA
Simulation Engine (multiprocessor)
A/D D/A
A/D D/A
FACTS
FACTS/ESS
FACTS/ESS
Network

18. HIL Laboratory Interface
Machine 1
D/A output
FACTS1
A/D input
Controllable
Load
Machine 2
D/A output
FACTS2
A/D input
Controllable
Load
Machine 3
Power System
Simulation Engine
D/A output
FACTS3
Controllable
Load
A/D input

19. FACTS – Flexible AC Transmission System Prototype Device

20. FACTS Interaction Laboratory
UPFC
Simulation
Engine
HIL Line

21. Cyber Fault Detection

22. Fault Tolerance
Define correct operation of the power system with FACTS/ESS
Embed as executable constraints into each FACTS/ESS computer
FACTS/ESS check each other during operation of distributed control algorithms

23. Cyber Fault Injection Attempt to confuse the FACTS embedded computers
Attempt to disrupt the communication between FACTS embedded computers
Confuse the power system’s operation

24. Error Coverage of Distributed Executable Correctness Constraints (Maximum Flow Algorithm)

25. System Dynamic Control

26. Power Network Embedded With FACTS Devices
Tie-line flow
CONTROL
AREA A
CONTROL
AREA B
A decentralized power network embedded with FACTS devices can be viewed as a
hybrid dynamical system (Differential-algebraic-discrete-event).
While the FACTS devices offer improved controllability, their actions in a
decentralized power network can cause deleterious interactions among them.

27. Performance of FACTS controllers
with ideal observability
Uncontrollable modes in generator
speeds due to device interactions

28. Project Benchmarks Construction of HIL
Demonstration of Cascading Failures
Placement and Control
Hardware/Software Architecture
Cyber Fault Detection
Dynamic Control
Visualization

29. Special Thanks Imre Gyuk – DOE Energy Storage
Stan Atcitty – Sandia National Lab
John Boyes – Sandia National Lab