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

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

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

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

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

Identify cascading failure scenarios for test systems

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

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

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

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

FACTS Placement and Control

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

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

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

FACTS Interaction Laboratory (FIL)

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

FACTS Interaction Laboratory Architecture 33 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

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

FACTS – Flexible AC Transmission System Prototype Device

FACTS Interaction Laboratory UPFC Simulation Engine HIL Line

Cyber Fault Detection

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

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

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

System Dynamic Control

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.

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

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

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