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Speaker: Yee Wei Law Collaborators: Umith Dharmaratna, Jiong Jin, Slaven Marusic, Marimuthu Palaniswami 1 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 > 110kV AS 600382000 “Standard voltages” 66kV, 33kV < 33kV 3 For conductor ◦ Temperature RF temperature sensor Ice build-up For insulator, transmission line surge arrester ◦ Leakage current RF leakage current sensor 4 For transformers ◦ Detection of hydrogen in oil Metal insulated semiconducting (MIS) sensor for detecting hydrogen For on-load tap changers ◦ Detection of gas in oil (symptom of overheating) Internally mounted tap changer For bushings ◦ Leakage current 15 kV 69 kV 242 kV 5 MIS sensor Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009. 6 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 Example: Thermal model of overhead lines [Black ‘83]: 𝑑𝑇 𝑚𝑐𝑝 = 𝑄𝑔𝑒𝑛 + 𝑄𝑠𝑢𝑛 − 𝑄𝑟𝑎𝑑 − 𝑄𝑐𝑜𝑛𝑣 𝑑𝑡 𝑚: mass of the line 𝑐𝑝 : specific heat of the line 𝑇: temperature 𝑄𝑔𝑒𝑛 : Ohmic loses per unit length 𝑄𝑠𝑢𝑛 : solar heat input per unit length 𝑄𝑟𝑎𝑑 : radiated heat loss per unit length 𝑄𝑐𝑜𝑛𝑣 : convected heat loss per unit length 7 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 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 Ageing hardware + population growth = equipments at limits Market deregulation ◦ Advances in communications infrastructure Cost of outages in USA in 2002: $79B Climate change ◦ Government initiatives (USA, Europe, China, Japan, Australia..) ◦ Renewable energy and distributed generation ($652m fund) 10 Smart grid = envisioned next-gen power grid that is [DOE, USA]: Accommodating Intelligent (senses overload, rerouting) (renewable energy) Efficient (meets demand without more cost) Motivating Qualityfocused (minimal disturbances, interruptions) (demand response) “Green” (minimal environment impact) Resilient (to attacks, disasters) 11 Generation ◦ Distributed generation ◦ Microgrid Transmission ◦ Wide-area monitoring system Distribution ◦ Distribution automation Consumption ◦ Demand response 12 Control center Substation Distribution network Remotely and efficiently identify and resolve system problems Alleviates overload conditions, and enables computeroptimized load shifting Reconfigures the system after disturbances or interruptions Facilitates coordination with customer services such as timeof-use pricing, load management and DERs 13 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 Recloser Voltage regulator Switched capacitor bank 14 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 Urban area NAN Transmission grid Distribution grid Low voltage ((( HAN )) ) Collector ))) ((( )) ) City power plant ))) NAN BAN ))) Collector ((( )) Collector ((( )) Industrial customers ) IAN ) ))) Substations as gateways FAN Rural area ((( )) ) ((( )) ) Pole with wireless communication capability Distributed Energy Resources NAN = Neighborhood Area Network; FAN = Field Area Network HAN/BAN/IAN = Home/Building/Industry Area Network WAN standard is TCP/IP 16 SecureMesh 17 Jemena, United Energy, Citipower and Powercor Interoperability Capacity Latency Interference rejection CDMA2000 GE-MDS 900MHz Open standard Proprietary 76.8 kbps (80-ms frame) 153.6 kbps (40-ms frame) 307.2 kbps (20-ms frame) Hundreds of milliseconds 19.2 kbps (80 km) 115 kbps (48 km) 1 Mbps (32 km) DSSS, 2 GHz frequency band allows frequency band re-use Transmission Nation-wide service range coverage Configuration Point-to-multipoint SP AusNet and Energy Australia Silver Spring Networks Proprietary Wi-Fi/IEEE 802.11 100 kbps 54 Mbps (802.11a) 11 Mbps (802.11b) 54 Mbps (802.11g) 72 Mbps (802.11n) Open standard Tens of milliseconds Tens of Milliseconds milliseconds FHSS, 902-928 MHz FHSS, 902-928 802.11a: ODFM, 5 GHz MHz 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 80 km Unknown 802.11a: 120 m 802.11b/g: 140 m 802.11n: 250 m Point-to-point, Point-to-point Point-to-point, pointpoint-to-multipoint to-multipoint WiMAX/IEEE 802.16 Open standard 9 Mbps Milliseconds OFDM, 3.65-3.70 GHz 20 km Point-tomultipoint * Note: ZigBee is not in here 18 Year 2002 2004 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 2009 4G status not until 802.16m 19 Silver Spring Networks UtilityIQ: 20 Itron OpenWay: 21 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) Source: Lennvall et al. “A Comparison of WirelessHART and ZigBee for Industrial Applications,” IEEE WFCS 2008 22 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 DA makes dynamic reconfiguration possible Multi-objective optimization problem ◦ Objectives: minimize real losses, regulate voltage profile, loadbalancing ◦ Optimal topology: quadratic minimum spanning tree (q-MST) is NP-hard ◦ Bio-inspired heuristics, e.g. Artificial Immune System and Ant Colony Optimization 24 Grid Sensors Smart Grid Distribution Automation Wide-Area Monitoring System 25 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 Hence WAMS 26 PMU PMU ... PMU PMU WAN Layer 2: Data management PDC Application Data Buffer Real-Time Monitoring Real-Time Control Layer 1: Data acquisition Layer 3: Data services Real-Time Protection Layer 4: Applications 27 Synchronized phasor measurement units or synchrophasors for measuring voltage and current (phasor: 𝐴𝑒 𝑗𝜙 ) Typically 30 time-stamped samples per sec Invented by Phadke and Thorp of Virginia Tech in 1988 IEEE 1344 completed in 1995, replaced by C37.118 in 2005 For frequency, use Frequency Disturbance Recorder 28 Macrodyne’s model 1690 ABB’s RES521 MiCOM P847 29 Source: North American SynchroPhasor Initiative (NASPI) 30 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):980–996, 2005. • M. Larsson et al., “Predictive Frequency Stability Control based on Wide-area Phasor Measurements,” IEEE Power Engineering Soc. Summer Meeting, 2002. 31 𝑧1 𝑒1 ℎ1 (𝜃1 , … , 𝜃𝑛 , 𝑉1 , … , 𝑉𝑛 ) ⋮ System equation: ⋮ = + ⋮ 𝑧𝑚 𝑒𝑚 ℎ𝑚 (𝜃1 , … , 𝜃𝑛 , 𝑉1 , … , 𝑉𝑛 ) Measurements Errors Weighted least square ◦ 𝑥 𝑘+1 = 𝑥 𝑘 + 𝐻 𝑥 𝑘 𝑅−1 𝐻 𝑥 𝑘 Measurement Jacobian −1 𝐻 𝑥 𝑘 𝑅−1 [𝑧 − ℎ(𝑥 𝑘 )] PMU measurement s.d. 32 Observability: whether the system state can be uniquely estimated ◦ unobservable when 𝐻 𝑥 𝑘 𝑅−1 𝐻 𝑥 𝑘 cannot be inverted Critical measurement: absence of which destroys observability ◦ Residual sensitivity matrix 𝑆 = 𝐼 − 𝐻 𝐻𝑇 𝑅−1 𝐻 −1 𝐻𝑇 𝑅−1 ◦ If row 𝑖 and column 𝑖 are zeroes, then 𝑖th measurement is critical Redundant measurement: non-critical measurement 33 For an 𝑛-bus system, the PMU placement problem can be formulated as an integer programming problem: 𝑛 min 𝑐𝑖 𝑥𝑖 𝑖 s.t. 𝑓 𝑋 ≥ 𝟏, 𝑋 = 𝑥1 … 𝑥𝑛 𝑇 • 𝑐𝑖 is cost of installing a PMU at bus 𝑖 • 𝑥𝑖 = 1 if a PMU is installed at bus 𝑖 𝑓(𝑋) is a vector function, whose entries are non-zero if the corresponding bus voltage is solvable given the measurement – the problem becomes defining 𝑓 𝑋 Identify critical measurements; so that their removal doesn’t cause unobervability [Chen ‘05] Recent study [Emami ‘10]: ◦ To improve robustness against contingencies and failures ◦ To detect bad data among critical measurements 34 Classification Multiple Single #1 #2 #3 #4 #5 Bus Non-interacting e.g. #1 and #6 not correlated Interacting #6 Non-conforming Conforming e.g. #2 and #5 not correlated e.g. #2 and #5 correlated Linearized model: 𝑧 = 𝐻𝑥 + 𝑒 Common bad data detection mechanism 𝑧 − 𝐻 𝑥 > 𝜏 Q: Suppose true state is 𝑥, error in measurement is 𝛼, how much error in measurements will result in estimated state 𝑥 = 𝑥 + 𝑐? A: By def. 𝑧 − 𝐻𝑥 ≤ 𝜏, (𝑧 + 𝛼) − 𝐻𝑥 = 𝑧 − 𝐻𝑥 + (𝛼 − 𝐻𝑐) ≤ 𝜏, 𝛼 = 𝐻𝑐 maximizes probability that (𝑧 + 𝛼) − 𝐻 𝑥 ≤ 𝜏 Opportunity for attack 35 Attacker controls 𝑘 PMUs [Liu ‘09] Don’t care about 𝑐 𝑘 ≥ 𝑚 − 𝑛 + 1? yes 𝛼 always exists no 𝛼 exists depending on structure of 𝐻 Want specific 𝑐 Suppose, for example Unfixed Fixed 𝑐= , 𝛼 exists Unfixed Fixed depending on structure of 𝐻 Symbols: 𝑘 = number of hacked PMUs 𝑚 = number of measurements 𝑛 = number of system states 𝑐 = deviation from true states 𝛼 = induced measurement errors 36 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 Attack [Xie ‘10]: 1. In the day-ahead forward market, buy and sell virtual power at two different locations 𝑃1 and 𝑃2 2. Inject false data to manipulate the nodal price of the Ex Post market 3. In the Ex Post market, sell and buy virtual power at 𝑃1 and 𝑃2 respectively 4. Profit 37 Notable omission in this presentation: • Distributed generation, microgrid • Demand response 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 What’s next? 38 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. 16th ACM Computer and Communications Security, 2009. O. Kosut et al., “Limiting false data attacks on power system state estimation,” Proc. 44th Conf. Information Sciences and Systems, 2010. L. Xie et al., “False data injection attacks in electricity markets,” Proc. 1st 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 (corrosion, vandalism, animals) *TLSA=Transmission Line Surge Arrester RF temperature sensor RF leakage current sensor Ice build-up Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009. 40 Ref: EPRI, “Sensor Technologies for a Smart Transmission System,” white paper, Dec 2009. 41 1 if 𝑖 = 𝑗 𝑓 𝑥 = 𝐹 ∙ 𝐴 ∙ 𝑋, where 𝐴𝑖𝑗 = 1 if branch 𝑖𝑗 exists 0 otherwise Bus-to-bus connectivity matrix 1 1 𝐴= 0 0 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 1 1 0 0 1 bus bus 𝐹 is to make sure every pair of observable islands upon removal of each critical bus will have at least one PMU 1 0 𝐹= 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 bus Branch 1-2 Bus 2 island J. Chen et al. “Improved Bad Data Processing via Strategic Placement of PMUs,” IEEE Power Engineering Society General Meeting, 2005 42 Centralized scheduling Coordinated distributed scheduling Uncoordinated distributed scheduling schedule 43 Where the measurements are used: Real-time contingency analysis Real-time network analysis Study network analysis 44 Tropos GridCom: 45