dynamic power system mirror for application in energy ... · dynamic power system mirror for...

Dynamic Power System Mirror for Application in Energy Management SystemsFrom the dynamic control centre towards the next generation of power system control technology

Christoph Brosinsky

Power Systems Group

Technische Universität Ilmenau, Germany

[email protected]

| Dynamic Power System Mirror for Application in Energy Management Systems | © Christoph Brosinsky, TU Ilmenau 2019

Technische Universität Ilmenau – IEES

Institute of Electric Power and Control Technologies (IEES)

Full Professorships Dept. of Power Systems (Dirk Westermann) Dept. of Switching Devices and Switchgears Dept. of Power Electronics Dept. of Electrical Machines Dept. of Electrical Process Engineering

Foundation Chairs Dept. of Energy Utilization Optimization Dept. of Industrial Electronics Dept. of Lightning and Overvoltage Protection

Research Groups High Voltage Technologies (Dr. Leu) Power Systems 2050 (Dr. Schlegel)

Senior Scientists / Postdocs / Research Associates 1 extraordinary Professor 1 honory Professor 3 private Lecturers 14 Postdocs ca. 80 Scientists and PhD Students


Ilmenau located in the centre of Europe

radius 400 km

Ilmenau

radius 1750 km


Latest Research Projects

HVDC P2P, FACTS, PST, etc.

HVDC-Grid, Overlay(Operation of new grid structures)

Next Generation Control Center,Digital Transformation and Automation

REGEES

OVANET 1.0

GleichMorgen

DynaGridControlCenter (DGCC)

VEREDELE (FACDS)

TSO

DSO

HyLite (Q4.18 – Q4.2021)

OVANET 2.0 (Q4.18 – Q4.21)

VEREDELE 2.0

VNB-DC (Q1.19 – Q2.21)

InnoSys 2030 (Q4.18 – Q4.21)

TSO

/ D

SO

planning phase

completed

approval stage

currently running

16:49 | | Dynamic Power System Mirror for Application in Energy Management Systems | © Christoph Brosinsky, TU Ilmenau 2019

Motivation

Faster and larger changes in operation, rising variability and uncertainty, new sensor data New flexibility options (e.g. VSC-HVDC, dynamic line rating (DLR), demand side management (DSM) Less time to take decisions New operator assistant systems needed Accurate dynamic models required to address arising challenges in close to real time (C2RT) operation

Possible new control center EMS applications: Continuous analysis and anomaly detection Prediction of future operational states Optimization of unit controls to address

dynamic system stability Reduction of unplanned outages trough

continuous online monitoring and optimal maintenance schedule

Continuous operator training on the job

Dig

ital T

win

-ce

ntric

arc

hite

ctur

eG

rid-

dyna

mic

s

Dynamization Degree

Future

3rd Generation:„Dynamic“ Control Room Applications

4th Generation:„Dynamic Digital Mirror“

Now

16:49 | | Dynamic Power System Mirror for Application in Energy Management Systems | © Christoph Brosinsky, TU Ilmenau 2019

Towards automation of power system operation

Electric power system in the process of a digital transformation Increased need for operative interventions in C2RT operation in

the future Repetitive work processes will be automatically evaluated and

executed in the future Shift in the areas of responsibility of the control center

personnel to be expected Future support of C2RT operations management through highly

automated assistance systems

GUI

Confirm setpoint adaption ?

NoYes

[1] A. M. Prostejovsky, C. Brosinsky, K. Heussen, D. Westermann, J. Kreusel and M. Marinelli, “The future role of human operators in highly automated electric power systems,” Electric Power Systems Research, vol. 175, p. 105883, 2019.

16:49 | Page 8 | Dynamic Power System Mirror for Application in Energy Management Systems | © Christoph Brosinsky, TU Ilmenau 2019

State of the Art Control Center Technologies – „Siemens SIGUARD® Family“

Dynamic EMS Tools can cover power system dynamic phenomena Time synchronized, high resolving Measurements enable new methodologies for system analysis New Data sources lead to new possible strategies for system operation

Dynamic Security Assessment (DSA)

OptimizedOperation

SIGUARD® DSADynamic Security Assessment

Selective Protection settings

Adaptive Configurationof Protection Devices

SIGUARD® PSAProtection Security Assessment

Event Classification

SIGUARD® DC

Pattern recognition

Disturbance ClassificationSIGUARD® PDP

Wide Area Monitoring System

SyncrophasorProcessing

Phasor Data Processing

Enhanced“Situation Awareness”

State Estimator Results

Protection DeviceSettings

SyncrophasorAnalytics


Seconds / MinutesMilliseconds (RMS)

InterconnectedPower System

RTU

„Classic steady-stateEMS“ Functions

„DynaGridCenter“EMS Functions

Phasordata ProcessingDynamic Security

Assessment IEC 61850 compatible

DatabaseDynamic Phenomena

New operator support Functions

Wide Area Protection / Control Adaptive Protection SettingsDynamic Security Assessment Preventive / curative measures Event Classification Parameteradaption

(Controllers) IEC 61850 compliant HVDC-

Substation controller link

Applications Transmission System Operation

SCADA Interface and Database State EstimationQuasi-stationary phenomena

PMU

C37

.118

IEC1

04

Functionalities of the „Dynamic Control Centre“


Concept of corrective measures in hybrid AC-HVDC Grids

Remedial Action Schemes (RAS) based on thetechnological characteristcs of VSC-HVDC converters in parallel operation to the HVAC-system NERC: Remedial Action Scheme (RAS) ENTSO-E: Special Protection Scheme Corrective measures are applied

(automatically) after a specific event in thepower system (e.g. n-1 security violation)

System security is maintainded due to a change of the VSC-HVDC operational setpoint

Activation is decentrally and event based Setpoint and activation criteria calculation in

advance is part of the (new) system operationschedule (e.g., in 15 min intervals)

pVSC,2

emergencynormal

pVSC,1

50%

100%

t

loading

1 1

50%

100%

t

loading

pVSC,2

pVSC,1

emergencynormal

2


The HVDC-RAS as possible new EMS Application

[2] F. Sass, T. Sennewald and D. Westermann, "Automated Corrective Actions by VSC-HVDC-systems: A novel Remedial Action Scheme," in IEEE Transactions on Power Systems, 2019.

training of classifier

time-domain simulation

database

dynamic model

feature extraction feature extraction

online measurements (PMU)historic PMU data

event detection

physical system

classification & identificationt0 + (300-500 ms)t0 + (50-150 ms)

Protection system (circuit breakers)

Local identification Activation of corrective actions

t0 + (500 ms)

Determination and execution of HVDC-based Remedial Action Schemes

Automatic activation of remedial actions based on characteristic fingerprint of failures

Feature extraction (determination of fault patterns) highly depend on correct dynamic model of the power system

0 1 2 3 40

20

40

60

80

100

120

ther

mal

line

load

ing

/ %

5 t/s

AC branch: 24-49AC branch: 25-43AC branch: 47-59

w. HVDC-RASwo. HVDC-RAS

0 1 2 3 4 5 t/s

1500

1000

500

0

-500

-1000

P VSC

/ M

W

VSC1VSC2VSC3VSC5VSC6VSC7


The „Digital Twin“ an Emerging Technology

Digital Twin: The Digital Twin: a widely used marketing term for a system, which can reflect the physical conditions

of a system, process or object Digital representation of a physical system connected to the real system via sensor data streams Provides advanced functions for data analysis

Dynamic Digital Mirror: A dynamic system model which "mirrors" the system state Objective: to provide a valid dynamic model instance that enables other functions to perform power

system analyses Continuous model parameter tuningTarget function:

Minimization of model errors Increase model accuracy


Towards new instances in the control room EMS

SCADA

State Estimation

TelecontrolIEC 60870-5-104

IEC 61850

Phasor DataConcentrator

EMS Applications

IEE

E C

37.1

18

Steady State Assessment

PMU PMU

Graphical User Interface (GUI)

Control Room

RTU IED

OperatorTeam

Dynamic Assessment

IEE

E C

37.1

18SCADA

State Estimation

TelecontrolIEC 60870-5-104

IEC 61850

Phasor DataConcentrator

EMS Applications

IEE

E C

37.1

18

Steady State Assessment

PMU PMU

Graphical User Interface (GUI)

Control Room

RTU IED

Operator Team

Dynamic Assessment

HIL Device

Virtual Sensor

Virtual Actuator

IEE

E C

37.1

18

Dynamic Digital Mirror

„Digital Twin“ Applications

[3] C. Brosinsky, D. Westermann and R. Krebs, "Recent and prospective developments in power system control centers: Adapting the digital twin technology for application in power system control centers," IEEE International Energy Conference (ENERGYCON), Limassol, 2018, pp. 1-6.

[4] C. Brosinsky, T. Sennewald, R. Krebs, and D. Westermann, “Applicational Concept for a Dynamic Power System Mirror in the Control Room,” in Proc. Cigré Symposium Aalborg, 2019.[5] C. Brosinsky, X. Song, and D. Westermann, “Digital Twin – Concept of a Continuously Adaptive Power System Mirror,” in Proc. Internationaler ETG-Kongress, Esslingen am Neckar, 2019.


Concept of the Dynamic Power System Mirror

System Data Aquisition

Data Validation & Improvement

System Modeling

System Analysis Tools

Operational Planning &

Control

Parameter Tuning

Assistance Systems

DynamicDigital Mirror

Asset Data

Process Data

Data Storage

Data “Conditioning“

Model Builder(time-domain)

1 2 3 4

1 2

[ ' ']

[ ][ ] [ ]

Tq d

T

T Tm fd

x e e

x x x xu T E u u

δ ω= ∆

=

= =

Real & Virtual Data Streaming

Data Integration

RTUPMU


Towards Implementation and Testsetup

Load step in original system (Load 2: P -40 %, Q: -50%)

i = 0

i > imax

i = i +1

Get initial state variablesand system conditions

from DT database (t = t0)

Reduce Δt

EndSingularity

likely

Δt < Δtmin

Event occured?

ComputeΔx and Δy

Numerical integration

routine

Process Data Interface

|Δx(i)|, |Δy(i)| <ε

Update x and y Output

Export CriterionUpdate Δt

Export state to DT Database

no

yes

no yes

yes

yes

no


New „Digital Twin centric“ Control Center Architecture

Seconds / MinutesMilliseconds (RMS)

Physical GridSensor

“State-of-the-Art“Dynamic Control Center

“Augmented“ Dynamic Control Center

Data Analytics Data Science Dynamic Modelling Virtual Sensor Data

“Digital Twin“ centric EMS utilizing “Dynamic Digital Mirror“

“Quasi“-stationaryphenomena

Phasor data processing Dynamic phenomena

Sensor

„Digital Twin“ Database Dynamic System Model Model Parameter Tuning Real-time Visualisation Disturbance Analysis and Classification Enhanced Observability Rising Situation Awareness Adaptive System Protection Data Integrity and Reliability

Dynamic Digital Mirror (DDM)Real Time Simulation Environment Virtual SensorVirtual Sensor

PMU


Possible new EMS Applications

[4] C. Brosinsky, T. Sennewald, R. Krebs, and D. Westermann, “Applicational Concept for a Dynamic Power System Mirror in the Control Room,” in Proc. Cigré Symposium Aalborg, 2019.

Unification of Control Room EMS Application Interfaces• Single operational interface for a clear understanding and a unified work flow• Potential to interconnect stationary and the dynamic monitoring and control applications

KPI Development and Evaluation of Operational Performance• Tracking of power system reliability and operational performance through KPIs, e.g.: • Frequency and voltage level tracking, recovery speed and stability assessment• Availability of reserves (e.g. demand response, dynamic line rating)• Efficiency of preventive and curative actions including automatic RAS• Response time to certain event types

Basis for new Operator Assistance Systems• Quick decision support in similar future situations• Evaluation of decision processes in a dynamic simulation environment• Enhancement of operator knowledge about dynamic phenomena during system operation• Real-time trending of system stability indicators• Look ahead projection for development of mitigation strategies

Control and Optimisation - Considering Transient Stability• Dynamic system model offers new information for optimization constraints e.g. for Transient Stability Constrained Optimal

Power Flow (TSCOPF) • Optimisation of unit controls to address dynamic system stability


Conclusion

Conclusion All necessities for a DT-centric control center are already

existing Physics-based models integrating data-streams can be core

application of EMS in control centers Analytics and data science will be an integrated part of future

power system operation

Obstacles for deployment: High computational demand for real time implementation and

processing of large amounts of real-time data Reliable long term stable numerical integration routines are

necessary A trustworthy confidence level of the mirrored system state has

to be defined Tradeoff between excessive simplified models (does not unveil the value the approach

promises) and a very accurate approach (might lead to unbearable complexity)

Real-Time Monitoring

Real-Time Applications

Control Center PDC & PDP

PDC Guide IEEE C37.244PDC Standard IEEE PC37.247

Data Storage

PMU

PMU

PMU

Substation PDC

Test Guide for Installation & Calibration, IEEE C37.242

TimingStandardsIEEE 1588IEEE C37.238IEC 61850-9-3

Measurement StandardsIEEE C37.118-1IEC 60255-118-1

Data Storage Standard IEEE C37.111

Communication StandardsIEEE C37.118-2IEC 61850-90-5

PMU

Offline Data Analysis

State Estimation

Digital Twinning

GPS

[3] C. Brosinsky, D. Westermann and R. Krebs, "Recent and prospectivedevelopments in power system control centers: Adapting the digital twintechnology for application in power system control centers," IEEE International Energy Conference (ENERGYCON), Limassol, 2018, pp. 1-6.

Thank you very much for your attention!

Christoph Brosinsky [email protected] Systems GroupTechnische Universität Ilmenau, Germany


Model Tuning Approaches

MeasurementSCADA/ PMU

MeasurementSCADA/ PMU

Model Component 1

IdentificationFunction

ComparatorΣ(xmeas-xsim)p

ΔParameter i

xmeas

xmeas

xsim

xsimModel

Component n

-250

-200

-150

-100

-50

0

50

100

150

200

250

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5

Pmeas Pinit Pest

Qmeas Qinit Qest

Pin

MW

/ Q in

MV

Ar

t in s

max

0

2, ,

1| ( ( ) ( )) |

t n

meas i sim i iit

e x t x t w dt=

= −∑∫xmeas,i is the measured response of device i,Xsim,i is the simulated response of device i,w is the weighting factor.

Parameter Estimation


Case Study HVDC-RAS

[6] F. Sass, T. Sennewald, A.-K. Marten, and D. Westermann, “Mixed AC high-voltage direct current benchmark test system for security constrained optimal power flow calculation,” IET Generation, Transmission & Distribution, vol. 11, no. 2, pp. 447–455, 2017.

Case study performed on test system presented in [6] Outage of a highly loaded AC interconnector

→ Overloading of parallel interconnector (107 %) Identification of contingency 400 ms after CB trip Ramp up of converter set-points in 410 ms Steady state line loading roughly 2 s after

contingency event

0 1 2 3 40

20

40

60

80

100

120

ther

mal

line

load

ing

/ %

0 1 2 3 4 5 t/s

1500

1000

500

0

-500

-1000

P VSC

/ M

W

VSC1VSC2VSC3VSC5VSC6VSC7

5 t/s

AC branch: 24-49AC branch: 25-43AC branch: 47-59

w. HVDC-RASwo. HVDC-RAS


Scenario – State of the Art (no HVDC-RAS available)

Observed network is not n-1-safe at an operating point Failure of a 400 kV line Rated current of parallel line exceeded Failure is reported to the operator in the control room in the

alarm list Curtailment of power plants and renewable energy feeders

causes redispatch costsAlarm informs control room

operator

Limit violation of AC-line

current


Scenario – New Dynamic Control Centre Feature: HVDC-RAS

Failure of a 400 kV line HVDC operating point is adapted to the HVDC station by

decentralised intelligence after fault pattern recognition Line failure does not lead to congestion (anymore)

HVDC setpoint change AC-Line

currents

Dynamic frequency response

Dynamic voltage phasors

dynamic power system mirror for application in energy ... · dynamic power system mirror for...

Documents