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Smart Transmission Grids Applications for Real-Time and Predictive Monitoring and Control of Power Systems
Citation preview
“without deviations from the norm, progress is not possible” Frank Zappa
Smart Transmission Grids Applications for Real-Time and Predictive Monitoring and Control
of Power Systems
Prof.Dr.Ing. Luigi Vanfretti [email protected] - http://www.vanfretti.com
[email protected] Associate Professor, Docent
SmarTS Lab KTH
Stockholm, Sweden
[email protected] Special Advisor in Strategy and Public Affairs
Research and Development Division Statnett SF
Oslo, Norway
Invited Seminar – Department of Mechanical Engineering
Massachusetts Institute of Technology (MIT)
March 17, 2014 – Cambridge, MA, USA
Outline • SmarTS Lab Research Group, Projects and Funding, and
“The Lab”
• Challenges brought by renewable energy sources - Impact on system dynamics
• Large Scale Power System Operation - Technical Needs
- Vision for Smart Operation Monitoring and Control Systems
• Concepts - Power system dominant paths: concept and identification
- Power system dominant path feedback signal properties and time delay impacts
• Methods - Identification of dominant paths
• Tools - Real-time monitoring and control of oscillations
• Looking forward – challenges for R&D - Solutions needed for Smart Operation
- Interesting challenges
Research Group Members
• 19 persons: 2 staff, 3 post-docs, 9 phd students, ~< [1 - 5] MSc students at a time
• Research Group Leader: Prof. Luigi Vanfretti
• Lab coordinator: Viktor Appelgren
• Post-docs (3):
- F. Rafael Segundo-Sevilla,
- Hossein Hosshyar, Rujiroj Leelaruji
• PhD Students (9):
- Yuwa Choompoobutrgool, Vedran Peric,
- Wei Li, M. Shoaib Almas, Tetiana Bogodorova,
- Jan Lavenius, Farhan Mahmood,
- Maxime Baudette , Francisco Gomez
• Researchers (0):
- N/A
• MSc Students (4):
- Mengjia Zhang, Le Qi, Giusseppe Laera, M. Ahsan Adib Murad, Jahidul Islam Razan 4
International Projects and Funding
• The research group participates in several international projects funded by the EC within the FP7 program and Nordic projects funded by NER, such as:
• Funding Total of 36 Million SEK
• About 12% from National funding.
5
SmarTS Lab: A test-bench for developing and testing Smart Transmission Grid Technologies
Sync. Timing
Phasor Measurement Data
Controllable and Protective Device Measurement and Status Data
Data Alignmentand
Concentration
Comm
unica
tion N
etwork
sData Storage and Mining
Monitoring
Transmission System Control Center
Advanced Displays
Early Warning System
Decision Support Intelligent System
Automatic Control and Protective
Actions Actions
Smart Operation Decision/Control Support System
Smart-Automatic Control Actions
Smarter OperatorDecision Support
Smarter Operator Control Actions
Grid Optimization
• Basic actions for automatic power system management:
- Measure – Communicate – Analyze the System (System health and limit assessment) – Determine Optimizing Actions and Preventive/Corrective Actions – Communicate – Control and protect
• Smart Operation, Control and Protection solutions:
- The ultimate goal should be to attain a (nearly) automatic-feedback self-healing control system
- The functionalities of the Smart Operation Decision/Control Support System need to be developed and tested
- The harmonization and interaction of model-based and measurement-based tools needs to be developed
Physical Layer ICT Layer
Virtual Layer: Decision Support,
Control and Optimization
Functionalities for Smart Operation
I
O
SmarTS Lab: A test-bench for developing and testing Smart Transmission Grid Technologies
Sync. Timing
Phasor Measurement Data
Controllable and Protective Device Measurement and Status Data
Data Alignmentand
Concentration
Comm
unica
tion N
etwork
sData Storage and Mining
Monitoring
Transmission System Control Center
Advanced Displays
Early Warning System
Decision Support Intelligent System
Automatic Control and Protective
Actions Actions
Smart Operation Decision/Control Support System
Smart-Automatic Control Actions
Smarter OperatorDecision Support
Smarter Operator Control Actions
Grid Optimization
• Basic actions for automatic power system management:
- Measure – Communicate – Analyze the System (System health and limit assessment) – Determine Optimizing Actions and Preventive/Corrective Actions – Communicate – Control and protect
• Smart Operation, Control and Protection solutions:
- The ultimate goal should be to attain a (nearly) automatic-feedback self-healing control system
- The functionalities of the Smart Operation Decision/Control Support System need to be developed and tested
- The harmonization and interaction of model-based and measurement-based tools needs to be developed
Our Architecture
Our laboratory!
Design and implementation of time-synchronized measurements (i.e. synchrophasor applications)
- Concepts: inception and design
- Methods: implementation of algorithms for monitoring, control and protection with real-time execution constraints
- Tools: software architecture and prototype implementation
- Testing and Validation: of the implemented tools using off-line & real-time simulation, as well as laboratory experiments.
10 15 20 25
0.985
0.99
0.995
1
1.005
1.01
1.015
1.02
Volta
ge (
pu)
Time (sec)
SCADA: Old Technology, Slow
PMU: New Technology, FAST!
PMU vs SCADA Measurements
from Wind-Farm Interactions (oscillations)
Applications using
PMU Data can detect oscillations, while those based on SCADA can not.
SmarTS Lab Architecture
Smar
TS L
ab
Har
dw
are
Imp
lem
enta
tio
n
Challenges: Renewable energy sources bring more production capacity...
• But production from renewables is not enough to attain a sustainable energy system
• For a secure, efficient, and flexible use of renewables secure power transmission is necessary!
Bringing More Renewable Capacity in the Nordic Grid Will pose a challenges on how the grid is operated and controlled
11
Old pattern: winter/summer, wet/dry – mainly seasonal – operation based on
historical experience!
Bringing More Renewable Capacity in the Nordic Grid Will pose a challenges on how the grid is operated and controlled
• With changes in generation patterns – “system dynamics” will also change.
• This means faster power transfer interactions –which need to be monitored, and controlled for secure power transmission.
• Smart Grid solutions need to be developed to safely integrate renewable energy sources: - Real-time monitoring and predictive analytics
can provide real-time visibility and aid in assessing the health of the system due to fast dynamic phenomena – across traditional operational boundaries
- Real-time control can help handling operation as operation conditions become more stringent
12
Old pattern: winter/summer, wet/dry – mainly seasonal – operation based on
historical experience!
New pattern: price differences, much wind / less wind – daily, hourly, per
minute, per second changes!
Needs for Grid Operation Monitoring, Operation, Control, Optimization and Protection Solutions
Different types of challenges in Scandinavia: Picture of the Regional Control Center at Alta, Norway. Operators have the challenge of running a power system under severe weather conditions and guaranteeing supply to critical heavy industries
Needs for Grid Operation Monitoring, Operation, Control, Optimization and Protection Solutions
Different types of challenges in Scandinavia: Picture of the Regional Control Center at Alta, Norway. Operators have the challenge of running a power system under severe weather conditions and guaranteeing supply to critical heavy industries
Needs for Grid Operation Monitoring, Operation, Control, Optimization and Protection Solutions
Different types of challenges in Scandinavia: Picture of the Regional Control Center at Alta, Norway. Operators have the challenge of running a power system under severe weather conditions and guaranteeing supply to critical heavy industries
From the X-Ray to the MRI for the Power Grid Identifying and controlling renewable energy sources in the grid
• We need advances in monitoring and control technology, similarly of the transition from the X-Ray to the MRI:
Wind-farm induced oscillations at @ OG&E • Occurring during periods of high wind generation. • 5% fluctuation at a frequency of 13-15 Hz. • The oscillations were product of interactions between
controllers in two different wind farms. • Countermeasures:
o Switching to electrically isolate the wind farms. o Curtail the power output!
10 15 20 25
0.985
0.99
0.995
1
1.005
1.01
1.015
1.02
Voltage (
pu)
Time (sec)
SCADA: Old Technology,
Slow
PMU: New Technology,
FAST! Grid monitoring
technology of today
Smart Grid Monitoring Technology
of tomorrow!
From the X-Ray to the MRI for the Power Grid Identifying and controlling renewable energy sources in the grid
• We need advances in monitoring and control technology, similarly of the transition from the X-Ray to the MRI:
• Conventional Technology (SCADA) is not capable to cope with new challenges!: too slow, can never capture the phenomena.
• New software applications and power-electronics-based control technology can help to:
- Detect unwanted behaviour from renewable energy sources (sub-synchronous wind-farm oscillations)
Control unwanted/unexpected behaviour and optimize the performance of the grid.
• This in turn will allow to safely transport and utilize renewable energy sources!
Wind-farm induced oscillations at @ OG&E • Occurring during periods of high wind generation. • 5% fluctuation at a frequency of 13-15 Hz. • The oscillations were product of interactions between
controllers in two different wind farms. • Countermeasures:
o Switching to electrically isolate the wind farms. o Curtail the power output!
10 15 20 25
0.985
0.99
0.995
1
1.005
1.01
1.015
1.02
Voltage (
pu)
Time (sec)
SCADA: Old Technology,
Slow
PMU: New Technology,
FAST!
Large Scale Power System Operation
Technical Needs at Different Time Scales
• Smart Operation and operational planning solutions can help to satisfy these needs!
Severe Emergency Conditions requiring
Automatic and Self-Healing Control and Protective
Actions
Non-Severe Emergency Conditions requiring
Coordinated Operator Assited Control and
Optimized Re-Dispatch and Balancing
Steady State Operating Conditions requiring
Preventive Control and Power Flow Optimization
Miliseconds, Seconds Months, Year (1) Years (2-5)
Near Real-Time Operation
Market Forces
Uncertainties in variable production and demand; External Forces; Vulnerabilities (physical and cyber)
Short-Term Mid-Term Long Term
Years (5-15)
Real-Time Operation
Minutes, Hours, Days
Regulation, Legislation
Resource Adequacy and Grid Development Gaps
requiring Coordinated Grid Planning Optimization
Long-term goals for “green” resource adequacy
and grid development optimization requiring
Coordinated Grid Planing
Operations
Operational Planning Planning
Technical Needs
Large Scale Power System Operation
Technical Needs at Different Time Scales
• Smart Operation and operational planning solutions can help to satisfy these needs!
Severe Emergency Conditions requiring
Automatic and Self-Healing Control and Protective
Actions
Non-Severe Emergency Conditions requiring
Coordinated Operator Assited Control and
Optimized Re-Dispatch and Balancing
Steady State Operating Conditions requiring
Preventive Control and Power Flow Optimization
Miliseconds, Seconds Months, Year (1) Years (2-5)
Near Real-Time Operation
Market Forces
Uncertainties in variable production and demand; External Forces; Vulnerabilities (physical and cyber)
Short-Term Mid-Term Long Term
Years (5-15)
Real-Time Operation
Minutes, Hours, Days
Regulation, Legislation
Resource Adequacy and Grid Development Gaps
requiring Coordinated Grid Planning Optimization
Long-term goals for “green” resource adequacy
and grid development optimization requiring
Coordinated Grid Planing
Operations
Operational Planning Planning
Our focus!
Technical Needs
Smart Operation → Requires new functionalities That take advantage of the supporting infrastructure (ICT) to enhance power system operation. That is new monitoring, control, network optimization and market functions.
Transmission System Operator
Smart Operation
Smart Operational Planning
New Markets
Conventional Generation
Biomass
PV
Wind
Distributed Loads
Large Controllable Loads Domestic Loads
and PV Gen.
Storage Other Data
Sources (e.g. weahter)
Power Transmission Backbone
Comms Backbone
New functionalities enabled by the ICT
need to be designed to allow for Smart
Operation!
Smart Operation Advanced Monitoring, Control and Optimization Functionalities
Transmission System Operator
Smart Operation
Smart Operational Planning
New Markets
Power Transmission Backbone
Comms Backbone
Advanced ICT Solution as “backbone” to hold new functionalities.
Real-Time Meas. Data
Control Actions /
Activation Actions Enabling Optimization, Special Control and Protection Schemes
Enhanced Monitoring and
Situational Awareness
Tools
Real-Time and Predictive Decision
Support Tools
Grid Optimization
Tools
Stability Monitoring and
Control using Real-Time and
Predictive Tools
Automatic Control Loops Other
Concepts: Where do the main dynamics of large interconnected systems ‘flow’? Where can we observe them? How to identify critical links?
22
p. 09.02.10 /ngt
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Legend:
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220 kV circuit
Power substation
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Note: This is a representation of all 380 and 220 kV circuits
of the interconnected system except transmission lines
between power plants and substations
750 kV circuit
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Situation January 2011
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Continental Europe
Massstab Format
Zust.St.
Bl.-Nr. Anz.Bl.
Gezeichn.
Gepr.
Änderu
ngen= +
Geprüft
AutoCADENTSO-E750/380/220kV
/emm
/ngt
07.01.2011
27.09.1996
AM-AN-DA
1SPEZ
PA050024
Situation January 2011
Interconnected network
Continental Europer. 07.01.11 /ngt
Cyprus
IL
SY
JO
SA
IQ
IR
AM
KZ
AZ
EE
GE
RU
NO
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G. lalomitei
Cernavoda
Smirdan
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LB
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Dynamics in the Continental European System February 19th 2011, Frequencies
23
49.85
49.9
49.95
50
50.05
50.1
50.15
08:08:00 08:08:10 08:08:20 08:08:30 08:08:40 08:08:50 08:09:00 08:09:10 08:09:20 08:09:30 08:09:40 08:09:50 08:10:00
f [H
z]
20110219_0755-0825
Freq. Mettlen Freq. Brindisi Freq. Wien Freq. Kassoe
Courtesy of Dr. Walter Sattinger
Dynamics in the Continental European System February 19th 2011, Frequencies
24
49.85
49.9
49.95
50
50.05
50.1
50.15
08:08:00 08:08:10 08:08:20 08:08:30 08:08:40 08:08:50 08:09:00 08:09:10 08:09:20 08:09:30 08:09:40 08:09:50 08:10:00
f [H
z]
20110219_0755-0825
Freq. Mettlen Freq. Brindisi Freq. Wien Freq. Kassoe
Courtesy of Dr. Walter Sattinger
0
200
400
600
800
1000
1200
08:08:00 08:08:10 08:08:20 08:08:30 08:08:40 08:08:50 08:09:00 08:09:10 08:09:20 08:09:30 08:09:40 08:09:50 08:10:00
P [
MW
]
20110219_0755-0825
Sils -Soazza Robbia-Gorlago Robbia-San Fiorano Mettlen-Lavorgo
Dominant Inter-Area Oscillation Paths
Interaction Paths
The group of transmission lines, buses, and controllers which the generators in a system use for exchanging energy during swings.
[J. Hauer]
25
Definition
The passageway containing the highest content of the inter-area oscillations
Dominant Inter-Area Oscillation Paths can be characterized by using Network Modeshape
26
Dominant Inter-Area Oscillation Paths
Observability of System Dynamics Projecting the eigenvector to measurement output variables
27
Linearizing around an equilibrium point
Right Eigenvector/ Modeshape
Network sensitivities
Network Modeshape
Network sensitivities
Modeshape
Illustration: System with 1 Dominant Path
Important Features
The largest SV or the smallest S element(s) indicates the center of the path.
The difference between S elements of two edges of the path is largest among any other pair within the same path.
SV elements of the edges are the smallest or one of the smallest within the path
29
1 234 5
1G
2G
30
Feedback Properties and Time Delay Impacts
1 234 5
1G
2G
Time delay ≈ phase lag
Inter-area frequency
Δθ45
31
Feedback Properties and Time Delay Impacts
1 234 5
1G
2G
Time delay ≈ phase lag
Inter-area frequency
PSS
Δθ45
Dominant Path Signals: Delay Margin
32
1 234 5
1G
2G
Voltage Angle Difference |θij|
PSS
Delay Margin (DM) is defined as the smallest time (for Td > 0) required to destabilize the closed-loop system.
0
200
400
600
800
1000
1200
θ14 θ43 θ45 θ42
Delay Margin (ms)
494 ms
- Delay margin = upper bound for the design of WAPOD
- Although it’s attractive to use the signals with the largest observability, we found that these signals result in a smaller delay margin.
Methods: Identification of Dominant Paths
• Current magnitude modeshapes indicate how much of the contents of the inter-area modes are distributed among the transfer corridors.
• Thus, for particular inter-area oscillations, corridors having the highest content are the paths where the inter-area modes travel to the most.
• We propose an ambient-data based algorithm:
Pre-processing PMU
measurements
Filter the meas. so that only inter-area
modal content is preserved.
Compute PSD
Select appropriate window
Sort the contents of current meas.
in descending order
Dominant Paths
Verify the characteristics of the dominant paths
Study System: KTH-Nordic32
• Nordic grid ‘conceptual’ model
• 20 generators 52 buses
• Small-signal stability analysis using eigenanalysis
• Eigenanalysis: 2 poorly damped inter-area oscillations:
• 0.5 Hz (Mode 1) and
• 0.73 Hz (Mode 2)
Algorithm: Ambient Data
Pre-processing PMU measurements
Compute PSD Sort in descending
order Dominant Paths
35
36
0.50-Hz Mode 0.73-Hz Mode
October 22,
2012
Dominant Paths
Known System (ABCD)
Transient measurements
Ambient measurements
Model-based
Algorithm
Transient-based
Algorithm
Ambient-based
Algorithm
Dominant Paths (for individual inter-area oscillation)
PMU placement
Controller placement F
eedba
ck input si
gnals
Wide-Area Damping Control
Overview of the Algorithms
Discussion
Algorithms Comparison
Synchrophasor measurements should be used as the primary source of information while model-based algorithm can be used for planning studies since it is difficult in practice to know the exact model of the system.
0.73-Hz Mode 0.50-Hz Mode
Tools: Need for real-time monitoring of system dynamics. (Data from the 1996 WECC Break-up)
0.27 Hz 7% Damping (Ambient)
0.264 Hz 3.46% Damping (Transient)
0.252 Hz 1.2% Damping (Ambient)
350 400 450 500 550 600 650 700 750 800
1100
1200
1300
1400
Time (sec.)
Po
wer
Tra
nsf
er (
MW
)
Alarm
System Unsable
15:42:03 – K-A Line Trip
15:47:36 – R-L Line Trip
McNary Generation Trip
15:48:51 Out-of-step Separation
• Infrastructure (external to the software development kit): - PMU: phasor measurement unit.
- PDC: phasor data concentrator. A software running in a dedicated server .
- Communication Network: composed by routers/switches, fiber optic links (or other medium)
• Software Development Kit (SDK): a set of different computer software that allows a user to develop other derived software applications.
Computer/Server
Statnett’s Synchrophasor Software Development Kit (SDK)
Real-Time Data
Mediator (RTDM) “DLL”
LabView
PMU Recorder Light
(PRL)
PMU 1
PMU 2
PMU n
PDC Communication Network
Infrastructure
Data in IEEE C37.118 Protocol
Tools: Synchrophasor Software Development Kit
SDK is composed by two main parts:
• Data Collector - DLL reads data from PDC/PMU filling the
Live Buffer
- Access Buffer keeps the data
• Data Extractor - Queue Handler enables reading the Live
buffer
- Buffer Handler enables reading the Access Buffer
- Selector is a user interface for signal selection
PDC
DLL
Live Buffer
Access Buffer
Remote
Buffer Handler
Selector
C37.118
Data
Data Collector
Data extractor (LabView PMU control)
· Time step· Voltage Phasor· Current Phasor· Frequency
PRL Library
Queue Handler
Software architecture
Program interface
SDK Software Architecture and Implementation
SDK Monitoring and Options
PRL Main GUI Connection Settings Buffers and Queue
Bad Data Advanced
Ambient Data-Based Mode Estimation – Principle
• Objective: To estimate modes frequency and damping ratio
• Assumptions: - System is excited only by small load changes
- Load changes are modeled as white noise (no forced oscillations)
- Behavior of the system is modeled by a linear model
Power System H(f)
Load Changes Measurements
43
Mathematical Model Yule-Walker’s Method
44
• Model of the measured stochastic process
• Yule-Walker Equations (assuming that e(k) is white noise process)
0
1
( )( ) ( ) ( )
( )1
qi
iip
i
ii
b zB z
y k e t e kA z
a z1 0
( ) ( ) ( )p q
i ii i
y k a y k i be k i
1( ) ( 1) ( 1)
( 1) ( ) ( )p
r q r q p a r q
r q p r q a r q p
1
0
1( ) ( ) ( )
N n
k
r n y k n y kN
Implementation of the Real-Time Mode Estimation Application
•State machine architecture
•Program interface structure
• Interface example – Time domain signal
• Interface example – Options
Data Acquisition
Initialization
Read real-time
data
Read
simulated data
ARMA model
coefficientsWait Reports
Preprocessing
Signal
generator
PDC/SDK
Experimental Testing
•Synthetic measurement data - KTH Nordic 32 Test system
- Software (only) simulation
- Hardware in the loop experiment using real time simulator (KTH Nordic 32 Test system implemented in the simulator)
- Frequency signal obtained using National Instruments cRIO PMU
•Real field measurement data - Real-time measurements from the actual
Nordic Grid
Results - Synthetic Data
Real Mode
Hardware in the loop
Software simulation
Mean {f} [Hz]
0.4987 0.5073 0.4985
Mean {} [%]
3.5223 4.0910 3.6905
Var {f} - 1.1037e-04 7.5797e-05
Var {} - 1.9088 1.7184
Stochastic properties of the mode estimator
• Spectrogram and spectrum
• Complex plane of the system
• Damping ratio evolution
• The 0.5 Hz mode is analyzed
• Statistical properties are analyzed by 120 independent estimations
Results - Real-time measurements from the Nordic Grid
• The critical mode is at 0.39 Hz with damping ratio 9 % (in average)
• Other modes are observable at 0.2 Hz, 1 Hz and 1.4 Hz
• 0.5 Hz mode sporadically appears as a poorly damped mode
• Development of a phasor-based damping control
• Algorithm for excecution on FPGA
• Implementation and performance assessment of Power Oscillation Damping Controller deployed in NI-cRIO
• Major Issues encountered: Signal-to-Noise Ratio of Analog Output Modules of Opal-RT, SNR of Analog output modules of NI-cRIO
OPAL-RT
2Real-Time Digital simulation is converted to Analog / Digital Signals through I/O s
1RealTime simulations are accessed from the console generated by OPAL-RT Lab software
Ethernet Switch
SEL-421 (PMU)
SEL-5073 (PDC)
Concenterates all the PMU streams, time-alligns them and creates a single PDC stream
BabelFish
Unwraps the PDC stream and provides Raw Data in Labview to develop visualization and control applications based on PMU measurements
TCP
UDP
Shared Variables
Analog Outputs of the RTS are wired to the CT/VT inputs of the PMUs
3
4The PMU streams are fed to the Network switch and these streams are made accessible to the Phasor Data Concentrator
56
7 NI-cRIO executes control algorithms based on PMU measurements and sends the control actions to the RTS
SEL-487E (PMU)
Generator
1000 MVA13.8 kV
Step Up Transformer13.8:500 kV1000 MVA
Transmission Line 350 km
Transmission Line 350 km
Load 500kV
5000 MW
Bus 1 Bus 2 Bus 3
Generator
Step Up Transformer13.8:500 kV5000 MVA
5000 MVA
13.8 kV
SVC
Bus 2_SVC
SVC Controller for Power Oscillation Damping
SEL-421 (PMU)Phasor POD Algorithm
deployed in NI-cRIO
Tools: Real-Time Control of Oscillations
Result from Simulink
software model
Result from RT-HIL with NI-cRIO
Power oscillation damping (Active Power Transfer From Area 1 to
Area 2
Software Model vs. Hardware Implementation
Time-delay impact
Methods and SW for balancing, re-distpach
and power flow control considering variable
sources
Methods, SW and HW to Improve Stability,
Security and Control
Power System with Hybrid AC/DC Transmission System
and variable generation sources and flexible loads
Miliseconds, Seconds, Minutes
Minutes, Hours, Days Days, Months, Years
Time Scales
Methods and SW Tools considering variable sources
and transmissionresource adequacy, power transfer optimization, and
optimized grid development
ICT System for Messaging, Data Management, etc.
Communication and Information Technologies
Electricity MarketLegislation & Regulation
Looking forward – Smart Operation Solutions Needed!
Smart Operation Solutions
Operation, control, and protection actions
Coordinated control execution cycles
DataFlow
Market effects transmitted through ICT network
Market events transmitted in physical process dynamics
Methods and SW for balancing, re-distpach
and power flow control considering variable
sources
Methods, SW and HW to Improve Stability,
Security and Control
Power System with Hybrid AC/DC Transmission System
and variable generation sources and flexible loads
Miliseconds, Seconds, Minutes
Minutes, Hours, Days Days, Months, Years
Time Scales
Methods and SW Tools considering variable sources
and transmissionresource adequacy, power transfer optimization, and
optimized grid development
ICT System for Messaging, Data Management, etc.
Communication and Information Technologies
Electricity MarketLegislation & Regulation
Looking forward – Smart Operation Solutions Needed!
Operation, control, and protection actions
Coordinated control execution cycles
DataFlow
Market effects transmitted through ICT network
Market events transmitted in physical process dynamics
A future vision for a Smart Operation Control System Blending Measurement/Data-Based and Model-Based Analysis Methods and Tools
Model-Based Dynamic Analysis and Control Tools
Measurement-Based DynamicAnalysis and Control Tools
Conventional Static Analysis ToolsSCADA/EMS
Preventive Protection and Control Actions
Emergency Protection and Control Actions
PMU & IED Data
SCADA/EMS,PMU & IED Data
Real-Time Protection and Control
Dynamic Models
Op
timizatio
n A
nalysis To
ols
Other Data
OtherData
Challenges - Scientific work to Support the Smart Operation Vision Hybrid Measurement-and-Model-Based Tools for Smart Operation
• There is a need to coherently combine real-time operation and control tools for: - Traditional static analysis tools and SCADA/EMS functions: • Provide steady state limits and control
– Efficient computation techniques are needed for equilibrium and boundary calculations (near-real-time).
- Dynamic analysis and control tools based on models • Provide corrective and preventive actions based on results predicted from models
– Uncertainty needs to be modeled and computation approaches considering uncertainty need to be developed. – Simulation and control design methods & tools, considering also ICT, need to be developed. – Techniques for model identification, reduction and validation are needed.
- Dynamic analysis and control tools based on measurements • Identify in real-time problems in the system, provide immediate relief for real-time protection and
feeback control – Signal processing methods coping with real-time constraints. – Algorithms for feature extraction and pattern recognition (“Data Analytics”) – Near-real-time methods for model identification and stability computations (“Data Analytics”) – Partly-distributed / partly-centralized control systems and laws need to be
- Optimization methods and tools that can exploit both models and measurements • Determine preventive and corrective actions to provide higher exploitation and stability of the grid
– Solution of NP hard problems involving large interconnected systems with continuous and discrete variables. – Computational techniques exploiting HPC and cloud.
References – Smart Operation, Concepts, and Lab
• About Smart Operation state of the art, challenges and vision:
- L. Vanfretti, D. Van Hertem and J. O. Gjerde, “Smart Transmission Grids vision for Europe”, Book Chapter, in Smart Grids and Sustainable Energy Transformation, Editors: P. Hills, Daphne Mah, Victor O.K. Li, Richard Balme, Springer, 2014. http://www.springer.com/energy/policy,+economics,+management+%26+transport/book/978-1-4471-6280-3
- L. Vanfretti, M. Baudette, and A. White, “Monitoring and control of renewable energy sources using synchronized phasor measurements,” Book Chapter, in Renewable Energy Integration: Practical Management of Variability, Uncertainty and Flexibility in Power Grids, Editor: Lawrence E. Jones, Elsevier, 2014. http://www.barnesandnoble.com/w/renewable-energy-integration-lawrence-e-jones/1117605149?ean=9780124079106
• About some of the concepts presented:
- L. Vanfretti, Y. Chompoobutrgool, and J.H. Chow, “Chapter 10: Inter-Area Mode Analysis for Large Power Systems using Synchrophasor Data”, Book Chapter, in Coherency and Model Reduction of Large Power Systems, Joe H. Chow (Ed.), Springer, 2013. http://www.springer.com/energy/systems%2C+storage+and+harvesting/book/978-1-4614-1802-3
- Y. Chompoobutrgool and L. Vanfretti, “Analysis of Time Delay Effects for Wide-Area Damping Control Design using Dominant Path Signals,” IEEE PES General Meeting, 2014, National Harbor, MD (Washington, DC Metro Area).
• About our “SmarTS Lab”, Testing and Validation Methods:
- L. Vanfretti, et al, “SmarTS Lab: A Laboratory for Developing Applications for WAMPAC Systems,” IEEE PES General Meeting 2012, San Diego, CA, USA.
- L. Vanfretti, M. Baudette, I. Al-Khatib, M. S. Almas, and J. O. Gjerde, “Testing and Validation of a Fast Real-Time Oscillation Detection PMU-Based Application for Wind-Farm Monitoring,” Invited Paper, Technical Session, Track 5: Communication and Control in Smart Grids, in Proceedings of the First International Black Sea Conference on Communications and Networking 2013 (BlackSeaCom 2013), Batumi, Georgia.
- M. Baudette, L. Vanfretti, G. Del-Rosario, A. Ruiz-Alvarez, J.L. Dominguez-Garcia, I. Al-Khatib, M. Shoaib Almas, I. Cairo, and J.O. Gjerde, “Validating a Real-Time PMU-Based Application for Monitoring Sub-Synchronous Wind Farm Oscillations,” IEEE ISGT 2014, Washington, DC, Feb. 19-22, 2014
References – Methods and Tools
• About some of the methods developed:
- V. Peric and L. Vanfretti, “Power System Ambient Mode Estimation Considering Spectral Load Properties,” IEEE Transactions on Power Systems, in press. Accepted November 2013. Available for early access: DOI: 10.1109/TPWRS.2013.2292331
- Yuwa Chompoobutrgool and L. Vanfretti, “Identification of Power System Dominant Inter-Area Oscillation Paths,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 2798 – 2807, Aug. 2013. DOI: http://dx.doi.org/10.1109/TPWRS.2012.2227840
- C. Sturk, L. Vanfretti, Y. Chompoobutrgool, and H. Sandberg, “Non-coherency based structure model reduction of power systems,” IEEE Transactions on Power Systems. August 2013. Revision, Dec. 2013. Accepted, January 2014. Availabe for early acces: http://dx.doi.org/10.1109/TPWRS.2014.2302871
- N.T. Anh, L. Vanfretti, D. Van Hertem, and J. Driesen, “A Quantitative Method to Determine ICT Delay Requirements for Wide-Area Power System Damping Controllers,” submitted, IEEE Transactions on Power Systems, March 2014.
• About some of the tools implemented:
- L. Vanfretti, M. Baudette, J.L. Dominguez, A. White, I. Al-Khatib, M.S. Almas, and J.O. Gjerde, “A PMU-Based Fast Real-Time Oscillation Detection Application for Monitoring of Wind Farm Dynamics,” submitted, Electric Power System Research (Elsevier), February 2014.
- M.S. Almas, M. Baudette, L. Vanfretti, S. Løvlund and J.O. Gjerde, “Synchrophasor Network, Laboratory and Software Applications developed in the STRONg2rid project”, IEEE PES General Meeting, 2014.
- L. Vanfretti, T. Bogodorova, and M. Baudette, “A Modelica Power System Component Library for Model Validation and Parameter Identification,” 10th International Modelica Conference 2014, Lund, Sweden, Mar. 10 – 12, 2014.
- V.S. Peric, L. Vanfretti, M. Baudette, J.O. Gjerde and S. Lovlund, “Implementation of a Real-Time Mode Estimation Algorithm using Ambient Synchrophasor Data,” Power Systems Conference, Clemson University, Clemson SC, 2014.