Speaker: Yee Wei Law Collaborators: Umith Dharmaratna, Jiong Jin, Slaven Marusic, Marimuthu...
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- Slide 1
- Speaker: Yee Wei Law Collaborators: Umith Dharmaratna, Jiong
Jin, Slaven Marusic, Marimuthu Palaniswami 1
- Slide 2
- 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
- Slide 4
- For conductor Temperature For insulator, transmission line
surge arrester Leakage current 4 Ice build-up RF temperature sensor
RF leakage current sensor
- Slide 5
- 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)
- Slide 12
- Generation Distributed generation Microgrid Transmission
Wide-area monitoring system Distribution Distribution automation
Consumption Demand response 12
- Slide 13
- 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
- Slide 14
- 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
- Slide 15
- 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
- Slide 16
- 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
- Slide 19
- 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
- Slide 20
- 20 Silver Spring Networks UtilityIQ:
- Slide 21
- 21 Itron OpenWay:
- Slide 22
- 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
- Slide 23
- 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
- Slide 24
- 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
- Slide 25
- 25 Grid Sensors Smart Grid Distribution Automation Wide-Area
Monitoring System
- Slide 26
- 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
- Slide 27
- 27
- Slide 28
- 28 For frequency, use Frequency Disturbance Recorder
- Slide 29
- 29 ABBs RES521 Macrodynes model 1690 MiCOM P847
- Slide 30
- 30 Source: North American SynchroPhasor Initiative (NASPI)
- Slide 31
- 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.
- Slide 32
- 32 Measurements Errors Measurement Jacobian PMU measurement
s.d.
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- 33
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- 34
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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
- Slide 36
- 36 yesno
- Slide 37
- 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
- Slide 38
- 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
- Slide 39
- 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
- Slide 40
- 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
- Slide 41
- 41 Ref: EPRI, Sensor Technologies for a Smart Transmission
System, white paper, Dec 2009.
- Slide 42
- 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
- Slide 45
- 45 Tropos GridCom: