OPAL-RT Smart transmission grid applications for real-time monitoring

“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

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ge (

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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

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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





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?

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Pazos de Borben

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11 12,13

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Enipower Ravenna

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13

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11

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22

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Interconnected network

Situation January 2011

Neurath

GKW Ost

FarbwerkeSud

YHAN

KW

Eyck

11

12

Oostzaan

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5

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Glorenza

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Cycofos

Julien

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16. Sagrera

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Visp1

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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

Lacu

G. lalomitei

Cernavoda

Smirdan

Braila

LB

Tepeören

Atisalani

Osmanca

Eregli

Cayirhan

Seyitömer

Seydisehir

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Aksa

Konya

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Hamitabat Alibeykoy

Izmir DGKC

Uzundere

Germencik

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Kemerkoy

Denizli

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Can

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Bursa Sanayi

Bursa

Tuncbitek

Adapazari

Adapazari 2DGKC

Erzin

Adana

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Kapasitör

Agacoren

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5

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Gaziantep 2

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^

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18

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Kaptan Celik

UnimarDavutpasa Ümraniye

Yolbulan

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

http://www.springer.com/energy/policy,+economics,+management+&+transport/book/978-1-4471-6280-3









http://www.barnesandnoble.com/w/renewable-energy-integration-lawrence-e-jones/1117605149?ean=9780124079106











http://www.springer.com/energy/systems,+storage+and+harvesting/book/978-1-4614-1802-3









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.

http://dx.doi.org/10.1109/TPWRS.2012.2227840




Thank you!

Questions?

http://www.vanfretti.com

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