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Speaker: Yee Wei Law Collaborators: Umith Dharmaratna, Jiong Jin, Slaven Marusic, Marimuthu Palaniswami 1

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Introduction to the grid Introduction to the grid sensors Motivation for the Smart Grid Smart Grid components Wide-area Monitoring System (WAMS) Distribution Automation (DA) Conclusion 2

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3 AS 60038- 2000 Standard voltages > 110kV 66kV, 33kV < 33kV

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For conductor Temperature For insulator, transmission line surge arrester Leakage current 4 Ice build-up RF temperature sensor RF leakage current sensor

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For transformers Detection of hydrogen in oil For on-load tap changers Detection of gas in oil (symptom of overheating) For bushings Leakage current 5 Internally mounted tap changer 15 kV242 kV69 kV Metal insulated semiconducting (MIS) sensor for detecting hydrogen

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6 Ref: EPRI, Sensor Technologies for a Smart Transmission System, white paper, Dec 2009. MIS sensor

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Rating: maximum value of parameter (e.g. power, current) Dynamic rating vs nominal rating increases capacity by 5-15% The primary limitation on power flow is thermal 7

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Transmission-line robots Developed by Tokyo-based HiBot Able to navigate around obstacle Laser-based sensors for detecting scratches, corrosion, changes in cable diameter HD camera for recording images of bolts and spacers up close Energy is a constraint 8

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Unmanned airborne vehicles aerial snapshot E.g. SP AusNet to automate conductor localization and spacer detection [Li 10] Line detection: template matching Spacer detection: Gabor filtering 9

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10 Ageing hardware + population growth = equipments at limits Market deregulation Advances in communications infrastructure Climate change Government initiatives (USA, Europe, China, Japan, Australia..) Renewable energy and distributed generation ($652m fund) Cost of outages in USA in 2002: $79B

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11 Smart grid = envisioned next-gen power grid that is [DOE, USA]: Intelligent (senses overload, rerouting) Intelligent (senses overload, rerouting) Efficient (meets demand without more cost) Efficient (meets demand without more cost) Accommo- dating (renewable energy) Accommo- dating (renewable energy) Motivating (demand response) Motivating (demand response) Quality- focused (minimal disturbances, interruptions) Quality- focused (minimal disturbances, interruptions) Resilient (to attacks, disasters) Resilient (to attacks, disasters) Green (minimal environment impact) Green (minimal environment impact)

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Generation Distributed generation Microgrid Transmission Wide-area monitoring system Distribution Distribution automation Consumption Demand response 12

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Remotely and efficiently identify and resolve system problems Alleviates overload conditions, and enables computer- optimized load shifting Reconfigures the system after disturbances or interruptions Facilitates coordination with customer services such as time- of-use pricing, load management and DERs 13 Control center Substation Distribution network

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Auto-recloser: circuit breaker that re-closes after interrupting short-circuit current Voltage regulator: usually at the supply end, but also near customers with heavy load Switched capacitor bank: switched in when load is heavy, switched out when otherwise 14 Recloser Voltage regulator Switched capacitor bank

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EPRI proposed advanced DA complete automation of controllable equipment Two critical technologies identified: Open communication architecture Redeveloped power system for component interoperability Urban networks: fiber optics Rural networks: wireless 15

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16 NAN = Neighborhood Area Network; FAN = Field Area Network HAN/BAN/IAN = Home/Building/Industry Area Network WAN standard is TCP/IP

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17 SecureMesh

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18 CDMA2000GE-MDS 900MHzSilver Spring Networks Wi-Fi/IEEE 802.11WiMAX/IEEE 802.16 Interoper- ability Open standardProprietary Open standard Capacity76.8 kbps (80-ms frame) 153.6 kbps (40-ms frame) 307.2 kbps (20-ms frame) 19.2 kbps (80 km) 115 kbps (48 km) 1 Mbps (32 km) 100 kbps54 Mbps (802.11a) 11 Mbps (802.11b) 54 Mbps (802.11g) 72 Mbps (802.11n) 9 Mbps LatencyHundreds of millisecondsTens of milliseconds Milliseconds Interference rejection DSSS, 2 GHz frequency band allows frequency band re-use FHSS, 902-928 MHz 802.11a: ODFM, 5 GHz 802.11b: DSSS, 2.4 GHz 802.11g: OFDM/DSSS, 2.4 GHz 802.11n: OFDM, 2.4/5 GHz *2.4 GHz band is crowded; 5 GHz less so OFDM, 3.65-3.70 GHz Transmission range Nation-wide service coverage 80 kmUnknown802.11a: 120 m 802.11b/g: 140 m 802.11n: 250 m 20 km ConfigurationPoint-to-multipointPoint-to-point, point-to-multipoint Point-to-pointPoint-to-point, point- to-multipoint Point-to- multipoint Jemena, United Energy, Citipower and PowercorSP AusNet and Energy Australia * Note: ZigBee is not in here

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19 2002 2004 2009 First published Beyer et al. Tutorial: 802.16 MAC Layer Mesh Extensions Overview: Centralized scheduling Coordinated distributed scheduling Uncoordinated distributed scheduling 802.16.2-2004 describes recommended practice for coexistence of point-to-multipoint and mesh systems 802.16j-2009 adds relay (tree) support Year 4G status not until 802.16m

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20 Silver Spring Networks UtilityIQ:

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21 Itron OpenWay:

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Standard by HART foundation Physical layer: IEEE 802.15.4 (since version 7); DSSS+FHSS Data link layer: TDMA Network layer: Graph routing or source routing Notable player: Dust Networks (founded by the Smart Dust people) 22 Source: Lennvall et al. A Comparison of WirelessHART and ZigBee for Industrial Applications, IEEE WFCS 2008

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IPv6 for low-power wireless personal area networks Motivation: interoperability with existing IP-based devices Standardized by IETF in RFC4919, RFC4944 etc. Physical and data link layer: IEEE 802.15.4 Network layer: still being standardized by the ROLL working group (Routing Over Low power and Lossy networks) Notable player: Sensinode 23

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DA makes dynamic reconfiguration possible Multi-objective optimization problem Objectives: minimize real losses, regulate voltage profile, load- balancing Optimal topology: quadratic minimum spanning tree (q-MST) is NP-hard Bio-inspired heuristics, e.g. Artificial Immune System and Ant Colony Optimization 24

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25 Grid Sensors Smart Grid Distribution Automation Wide-Area Monitoring System

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8-10% energy lost in transmission and distribution networks Energy Management System (EMS): control generation, aggregation, power dispatch EMS performs optimal power flow However, SCADA-based EMS gives incomplete view of system steady state 26 Hence WAMS

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27

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28 For frequency, use Frequency Disturbance Recorder

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29 ABBs RES521 Macrodynes model 1690 MiCOM P847

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30 Source: North American SynchroPhasor Initiative (NASPI)

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31 Oscillation control Voltage control The goal is to calculate maximum loadability using optimal power flow Frequency control The goal is to select which loads to shed, to minimize overvoltages or steady-state angle differences References: M. Zima et al., Design aspects for wide-area monitoring and control Systems, Proc. IEEE, 93(5):980996, 2005. M. Larsson et al., Predictive Frequency Stability Control based on Wide-area Phasor Measurements, IEEE Power Engineering Soc. Summer Meeting, 2002.

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32 Measurements Errors Measurement Jacobian PMU measurement s.d.

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35 #1 #3 #2 #4 #6 #5 Classification Single Multiple Non-interacting Interacting e.g. #1 and #6 not correlated e.g. #2 and #5 not correlated Non-conformingConforming e.g. #2 and #5 correlated Opportunity for attack Bus

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36 yesno

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Privatization of electricity market recent (80s) Locational marginal pricing (LMP) aka nodal pricing Case no constraint on Tx line: uniform market clearing price is the highest marginal generator cost Case congestion on Tx line: price varies with location 37

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Grid modernization stimulates multi-disciplinary research National priority vs. business priority In progress: $100m Smart Grid, Smart City demo project in Newscastle Intelligent Grid: CSIRO and five universities Whats next? 38 Notable omission in this presentation: Distributed generation, microgrid Demand response

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B.K. Panigrahi et al., Computational Intelligence in Power Engineering, Springer-Verlag Berlin Heidelberg, 2010. A. Monticelli and F.F. Wu, Network Observability: Theory, IEEE Trans. Power Apparatus and Systems, PAS-104(5):1042-1048, 1985. A. Monticelli, Electric Power System State Estimation, Proc. IEEE, pp. 262-282, 2000. A. Abur and A.G. Exposito, Power System State Estimation: Theory and Implementation, Marcel Dekker Inc., 2004. J. Chen and A. Abur, Improved Bad Data Processing via Strategic Placement of PMUs, IEEE Power Engineering Society General Meeting, 2005. R. Emami and A. Abur, Robust Measurement Design by Placing Synchronized Phasor Measurements on Network Branches, IEEE Trans. Power Systems, 25(1):38-43, 2010. Y. Liu et al., False data injection attacks against state estimation in electric power grids, Proc. 16 th ACM Computer and Communications Security, 2009. O. Kosut et al., Limiting false data attacks on power system state estimation, Proc. 44 th Conf. Information Sciences and Systems, 2010. L. Xie et al., False data injection attacks in electricity markets, Proc. 1 st International Conference on Smart Grid Communications, 2010. J. Momoh and L. Mili, Economic Market Design and Planning for Electric Power Systems, IEEE-Wiley Press, 2010. 39

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40 Ref: EPRI, Sensor Technologies for a Smart Transmission System, white paper, Dec 2009. RF leakage current sensor *TLSA=Transmission Line Surge Arrester (corrosion, vandalism, animals) RF temperature sensor Ice build-up

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41 Ref: EPRI, Sensor Technologies for a Smart Transmission System, white paper, Dec 2009.

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42 J. Chen et al. Improved Bad Data Processing via Strategic Placement of PMUs, IEEE Power Engineering Society General Meeting, 2005 bus Bus-to-bus connectivity matrix bus island Branch 1-2 Bus 2

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43 Centralized schedulingCoordinated distributed scheduling Uncoordinated distributed scheduling schedule

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44 Where the measurements are used: Study network analysis Real-time network analysis Real-time contingency analysis

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45 Tropos GridCom: