Grid Data Architecture Design and Implementation in theFP7 iTesla Project

Innovative Tools for Electrical System Security within Large Areas

Presenting on behalf of the FP7 iTesla Project

Prof.Dr.-Ing. Luigi VanfrettiE-mail: luigiv@kth.se, luigi.vanfretti@statnett.no

Web: http://www.vanfretti.com

Workshop: Next Generation Grid Data ArchitectureMarch 13, 2014 – Knoxville, TN, USA

luigiv@kth.se Associate Professor, Docent

Electric Power Systems Dept.KTH

Stockholm, Sweden

Luigi.Vanfretti@statnett.noSpecial Advisor in Strategy and Public Affairs

Research and Development Division Statnett SF

Oslo, Norway

Acknowledgment

• The work presented here is a result of the collaboration between the iTesla project project partners: http://www.itesla-project.eu/

• The following people have contributed to this presentation:

– RTE: Jean-Baptiste Hyberger, Geoffroy Jamgotchian, Christian Lemaitre

– KTH: Luigi Vanfretti

– Statnett: Luigi Vanfretti, Svein Harald Olssen

Outline

• Introduction to iTesla

– iTesla Project Parners

– iTesla Main Objectives

– iTesla Main Challenges

– iTesla vs Existing Tools

• iTesla Grid Data Architecture for Security Assessment

– State of the art in security assessment architectures

– iTesla architecture for security assessment under uncertainties

– General architecture

– Computation manager

– Main flows

– Workflow manager

• Importance of Standarized Model Exchange– Importance

– State of the Art

– Challenges

• Model Validation within the iTesla Architecture

• Conclusions

3

INTRODUCTION

iTesla Project

4

iTesla Partners

5

iTesla Main Objectives

To operate large power networks, planners and operators need to analyze variety of operating conditions – both off-line and in near real-time (power system security assessment).Different SW systems have been designed for this purpose.

But, the dimension and complexity of the problems are increasing due to growth in electricity demand, lack of investments in transmission, and penetration of intermittent resources.

Today TSOs must operate the system with reduced margins. Current contingency analyses are no longer suitable to address phenomena such as high penetration of intermittent energy sources, new power electronic devices, larger power transfer over long distances.

This toolbox will provide operators with tools to assess the security of power system situations from 2 days ahead to real time. The outputs will be relevant preventive or curative actions when needed

iTesla Main Challenges

7

To model the increasingamount of uncertaintiesin the decision process

To model preventive and

corrective actions and take them into

account in the decision process

To take intoaccount system dynamics in the

securityassessment

iTesla vs Existing Tools

Most of today’s tools

Static SA

without

uncertainties

Static SA

with uncertainties

Dyn SA

without

uncertainties

Dyn SA

with

uncertainties

Coreso

A few tools

iTesla

8

FOR SECURITY ASSESSMENT

iTesla Data Architecture

9

Common Architecture of « most » Available Power System Security Assessment Tools

Online

Data acquisition and storage

Merging module

Contingency screening

(static power flow)

Synthesis of recommendationsfor the operator

External data (forecasts and

snapshots)

“Static power flow model”

That means no (dynamic) time-domain simulation is performed, and uncertainty is not considered.

The idea is to predict the future behavior under a given ‘contingency’ or set of contingencies.

BUT, the model has no dynamics – only nonlinear algebraic equations.AND the model doesn’t consider uncertainty!

Computations made on the power system model are based on a “power flow” formulation without accounting for stochastic changes in operation conditions.

Result : difficult to predict the impact of a contingency without considering system dynamics and uncertainty!

iTesla Toolbox Architecturefor Security Assessment under Uncertainty

Online Offline

Sampling of stochastic variables

Elaboration of starting network

states

Impact Analysis(time domainsimulations)

Data mining on the results of

simulation

Data acquisition and storage

Merging module

Contingency screening

(several stages)

Time domain simulations

Computation of security rules

Synthesis of recommendations for the operator

External data (forecasts and

snapshots)

Improvements of defence and

restoration plans

Offline validation of dynamic models

iTesla Toolbox ArchitectureServices Required

Sampling of stochastic variables

Elaboration of starting network

states

Impact Analysis(time domainsimulations)

Data mining on the results of

simulation

Data acquisition and storage

Merging module

Contingency screening

(several stages)

Time domainsimulations

Computation of security rules

Synthesis of recommendationsfor the operator

External data (forecasts and

snapshots)

Improvements of defence and

restoration plans

Offline validation of dynamic models

Data management

Data mining services

Dynamic simulation

OptimizersGraphical interfaces

Modelica use planned for time-domain simulation:

Need for automatic Modelica model builder/translator using the iTesla

Internal Data Model (IIDM)

General Architecture

Data managerComputation

manager

Workflow manager

Data Manager

NoSQL DB (MongoDB)Relational DB

Data mining (Pepite + R)

Storage layer

Data

Management

layer Dynamic data manager

Contingencies & action data

File system

CIM V1 converter

Network model

Converters (Eurostag, modelica)

Historical network

data

simulation data

Admin UI

Computation Manager

HPC/Cloud

Slurm

Infrastructure

Common API

Condor other

iTesla computation API

Job scheduler

(load balancing)

Dynamicsimulator (Eurostag)

Optimizer (based on AMPL/Knit

ro)

Load flow (Hades)

SamplerComputation

modules

Computation Manager Interface

16

IIDM

Main Flows

Network data model

Complementary database

CIM filesENTSOE-

V1/2

Eurostagdynamic

data

CIM importer

Data mining database

Data mining

functions

Data mining

platform (Pépito, R,

etc)

EurostagDD

importer

Contingencies and actions

databaseEurostag data converter

Modelicadynamic

data

Eurostag to Modelicaconverter

Dynamic database

UI

Data mining database

feeder

Full Modelicanetwork

converter

Dynamic database

Optimizers data converter

RTE load flow data converter

AIA Agora load flow data converter

WP4/5

WP4/5

WP 3

WP4/5

WP4/5

WP5

WP5

Offline Workflow Manager

Load network from CIM file

Expand network with step-up transformers

Stochastic variable sampling

Loop on sample

Starting point init optimizer Load flow

Impact analysis = dynamic simulation +

security index computation for each

of the contingencyHistorical DB

Dynamic DB

State variable + security index

storage

Simulation DB

Security rules computation

Contingencies DB

done

ongoing

Offline Workflow Interface

19

Stochastic Sampling ProcessTracking Interface

20

Security Index ComputationTracking Interface

21

IMPORTANCE OF STANDARIZEDMODEL EXCHANGE

iTesla Project

22

Importance of Standarized Model Exchange

• IEC Common Information Model (CIM) is such a standard for addressing exchange of data between systems.

• Next Generation Grid Data Architecture should utilize this.

• This does not mean that the data model inside the Next Generation Grid Data Architecture should reflect CIM, but it should be possible to map between them.

• CIM covers the support of both Node-breaker and bus-branch model exchange:

– Equipment Model

– Steady-State Hypothesis (input to the Power flow calculation ++)

– Steady-State Solution

– Schematic layout (one-line diagrams)

– Geographical position

– Dynamic parameters for Transient Study

– (HV/MV) Direct Current

– Short-Circuit parameters23

State of the Art of Standarized Model Exchange

• The current CIM standard helps to address– Flow based market calculation (Operation Security verses Social Welfare will become more and more

important)

– Time domain Study (primarily on hourly resolution for matching the financial market)

– Capacity Calculation taking the market into account and including System Integrity Protection Scheme (SIPS) and Outage plans

– Handling of change – Power System Project (PSP)

• There will be some requirements for exchange that Next Generation Grid Data Architecture will have that currently are not solved in CIM.

• However, it is important that those issues are raised to the standardization organization together with relevant use-cases.

• CIM has a methodology that support extending the standard for organization / tools needs. – CIM does not have the goal to describe all relevant Canonical Data Model (CDM), but rather

harmonize with existing Canonical Data Models e.g. 61850 for Substation, Wind farms, Environment etc.

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Challenges forStandarized Model Exchange

• Mathematical description of the dynamic models. – The parameters are defined, but the model are defined as diagrams (same as in IEEE).

• Efficient format for storing Measurements. – CIM support/encourage the use of OPC / OPC UA.

– Link to PMU bases standard and maybe the use of HDF5

• There is no need for spending time coming up with new exchange format for those item that is covered in CIM. – Using a property format like PSS/E is not an option.

• Handling of changes and name/ID problem are not trivial for data management– Grid Data Architecture should not spend time on this, but relay on standard.

• Supporting CIM does not prevent the solution from supporting other more "native" exchange format.

• Financial:– It is important that the Grid Data Architecture can support contribution from Utility, R&D project by

industry and/or academic, Vendors and Academic.

– Statnett SF has an institutional mandate to support and implement standarization for data exchange.

– EU SmartGrid Mandate requires the use of CIM. 25

WITHIN THE ITESLA ARCHITECTURE

iTesla Model Validation

26

Power System Phasor-Time Domain Modeling and Simulation Status Quo

10-7 10-6 10-5 10-4 10-3 10-2 10-1 1 10 102 103 104

Lightning

Line switching

SubSynchronous Resonances, transformer energizations…

Transient stability

Long term dynamics

Daily load following

seconds

Phasor Time-Domain Simulation

PSS/E

Status Quo:Multiple simulation tools, with their own interpretation of different model features and data “format”.Implications of the Status Quo:- Dynamic models can rarely be shared in a

straightforward manner without loss of information on power system dynamics.

- Simulations are inconsistent without drastic and specialized human intervention.

Beyond general descriptions and parameter values, a common and unified modeling language would require a formal mathematical description of the models – but this is not the practice to date.

These are key drawbacks of today’s tools for tackling pan-European problems.

Standarized Model Exchange using CIM and Modelica

Sampling of stochastic variables

Elaboration of starting network

states

Impact Analysis(time domainsimulations)

Data mining on the results of

simulation

Data acquisition and storage

Merging module

Contingency screening

(several stages)

Time domainsimulations

Computation of security rules

Synthesis of recommendationsfor the operator

External data (forecasts and

snapshots)

Improvements of defence and

restoration plans

Offline validation of dynamic models

Data management

Data mining services

Dynamic simulation

OptimizersGraphical interfaces

Modelica use planned for time-domain simulation:

Need for automatic Modelica model builder/translator using the iTesla

Internal Data Model (IIDM)

Requirements of a SW architecture for model validation and calibration

Models

Static Model

Standard Models

Custom Models

Manufacturer Models

System Level

Model Validation

Measurements

Static

Measurements

Dynamic

Measurements

PMU Measurements

DFR Measurements

Other

Measurement,

Model and Scenario

Harmonization

Dynamic Model

SCADA Measurements

Other EMS Measurements

Static Values:

- Time Stamp

- Average Measurement Values of P, Q and V

- Sampled every 5-10 sec

Time Series:

- GPS Time Stamped Measurements

- Time-stamped voltage and current phasor meas.

Time Series with single time stamp:

- Time-stamp in the initial sample, use of sampling frequency to

determine the time-stamp of other points

- Three phase (ABC), voltage and current measurements

- Other measurements available: frequency, harmonics, THD, etc.

Time Series from other devices (FNET FDRs or

Similar):

- GPS Time Stamped Measurements

- Single phase voltage phasor measurement, frequency, etc.

Scenario

Initialization

State Estimator

Snap-shop

Dynamic

Simulation

Limited visibility of custom or manufacturer

models will by itself put a limitation on the

methodologies used for model validation

• Support “harmonized” dynamic models

• Process measurements using different DSP techniques

• Perform simulation of the model

• Provide optimization facilities for estimating and calibrating model parameters

• Provide user interaction

User Target(server/pc)

Model Validation Software

iTesla WP2 Inputs to WP3: Measurements & Models

(RaPId) Rapid Parameter Identification ToolboxSoftware Architecture using Modelica and FMI Technologies

EMTP-RV and/or other HB model simulation traces and simulation configuration

PMU and other available HB measurements

SCADA/EMS Snapshots + Operator Actions

MA

TLA

B

MATLAB/Simulink (used for simulation of the Modelica Modelin FMU format)

FMI Toolbox for MATLAB(with Modelica model)

Model Validation Tasks:

Parameter tuning, model optimization, etc.

User Interaction

.mat and

.xml files

HARMONIZED MODELICA MODEL:Modelica Dynamic Model Definition for Phasor Time Domain Simulation

Data Conditioning

iTesla Cloud or Local Toolbox

Installation

Internet or LAN

.mo files

.mat and

.xml files

FMU compiled by another tool

FMU

RaPId Interface

Options and

Settings

Algorithm Choice

Results and Plots

Simulink Containerl

Output measurement data

Input measurement data

• RAPID has been developed in MATLAB, where the MATLAB code acts as wrapper to provide interaction with several other programs.

• Advanced users can simply use MATLAB scripts instead of the interface.

• Plug-in Architecture:– Completely extensible and open

architecture allows advanced users to add:

• Identification methods

• Optimization methods

• Specific objective functions

• Solvers (integration routines)

Conclusions

• The iTesla toolbox has been designed to allow flexibility of integration of different modules and to preserve scalability.

– Optimization tools can be interfaced.

– Simulation tools can be replaced.

• This flexibility is due to a fairly general workflow architecture and the use of a well defined set of data flows and standarized model exchange.

– A best effort is being made to use standardized CIM models, and to attempt to extend it using mathematical-based model definitions (Modelica)

– Still we need to support legacy and making room for new modeling philosophies within the project

• Open Source Software: many pieces of the iTesla Platform will be released and managed in an open source software community.

• Future synchrophasor applications should be able to exploit and merge the philosophy of model based prediction with real-time measurement-based assessment:

– The iTesla platform would be a good starting point to look at these synergies between the model world and measurements world 32