Towards CIM Compliant Model-Based Cyber-Physical Power System Design and Simulation using Modelica

The 7th International Conferenceon Real-Time Simulation Technologies

Montreal | 9-12 June, 2014

Luigi Vanfretti1,2, Francisco Gómez1, and Svein H. Olsen2

[email protected] , [email protected] Power Systems Dept.

KTHStockholm, Sweden

[email protected] [email protected]

Research and Development Division Statnett SF

Oslo, Norway

Towards CIM-Compliant Model-Based Cyber-Physical Power System Design and Simulation using Modelica

The 7th International Conferenceon Real-Time Simulation TechnologiesMontreal | 9-12 June, 2014

Outline

• Cyber-Physical Systems• Key Concepts from Hybrid Systems• Key Characteristics of Hybrid Systems• Consequences of increased ICT• PMU-Based Wide Area Control Systems• Damping Controller WACS

• Cyber-Physical Systems Modeling• Systems Data Modeling• Model Development• CIM for model simulation• Modelica Simulation Engine


Acknowledgments

• This work has been funded in part by the EU funded FP7 iTesla project: http://www.itesla-project.eu/ and Statnett SF, the Norwegian power system operator.

• Work related to the iTesla Modelica power systems library presented here is a result of the collaboration between RTE (France), AIA (Spain) and KTH (Sweden) within the EU funded FP7 iTesla project: http://www.itesla-project.eu/

• Special thanks for ‘special training’ and support from• Prof. Fritzson and his team at Linköping University• Prof. Berhard Bachmann and Lennart Ochel, FH Bielefeld

http://www.itesla-project.eu/

http://www.itesla-project.eu/


Key Concepts from Hybrid Systems(a.k.a. cyber-physical systems)

• Physical systems evolve continuously over time, but other systems evolve by discrete changes.

• These discrete changes, or events, occur at specific points in time.

• Discrete event dynamic systems, as opposed to continuous dynamic systems, may not be directly based on physical laws derived from first principles.

• Hybrid systems contain both discrete parts as well as continuous parts.

• E.g.: consider a digital computer system interacting with the physical world.

• This is a simplified view of the world as discrete parts are usually distributed together with physical components.

Physical System

Computer System

Actuators Sensors

CommsComms

010101010101

An example is a power plant where local digital controllers are integrated as part of the generators…

http://blog.xogeny.com/blog/sensor-artifact/


Key Characteristics of Hybrid Systems(a.k.a. cyber-physical systems)

• A reactive system is a subclass of a real-time computer system with discrete behavior such as the system explained above

• These systems continuously react to their environment at a speed determined by that environment (sharp time constraints)

• An interactive system continuously reacts to users and to the environment, subject to a mix of user and computer system constraints (milder time constraints)

• We have a combination of the cases above, and we need to address each of these characteristics with the right architecture.

• Reactive systems have the following features:• Concurrency• Time Constraints• Determinism• Reliability• Hardware/Software

Physical System

Computer System

Actuators Sensors

CommsComms

010101010101

Environment

Output

Reac

tive

Syst

em

Input

Environment

Output

Inte

racti

veSy

stem

InputInput

Output

User


Consequences of increased ICT in Power System Operation

• Many processes in the power system are continuous, with local control & protection systems

• Increased ICT will drive an evolution to understand power system operation as a “hybrid system”:

• The virtual layer residing in a computer system(s) will enable new functionalities.

• The functionalities will derive more events or discrete changes (actions) for:

• Control, protection and optimization of the whole grid.• Will make particular loops as reactive or interactive loops.

• Sampling and reconstruction will be needed at different sample rates, and different granularity.

• Communication networks will transport measurements and different types of control actions in digital telecommunication networks (these may not follow physical laws derived from 1st principles)

• A Smart Operation Control System is the heart into the evolution of a high-level “hybrid system” from the operations perspective.

• One example of this transformation are synchrophasor-based control systems that utilize remote input signals that require the use of time-synchronized measurements, which are transferred via communication links to a wide-area control system (WACS)

Physical System

Computer System

Actuators Sensors

CommsComms

010101010101

Power System

Smart OperationControl System

(for different tasks / services)

Controllable Devices Sensors

(eg. PMUs)

CommsComms


PMU-Based Wide Area Control Systems(challenges for design, implementation and operation)

• WACS includes an ICT platform that merges the input data and transforms it to a useful input signal for active devices.

• WACS consists of

• (i) a number of synchronized phasor measurements units (PMUs – a sort of GPS time-syncronized distributed sensor) from geographically spread locations,

• (ii) a computer system termed phasor data concentrator (PDC),

• (iii) a real-time computer system where control functions are implemented,

• (iv) a communication network, and

• (v) the power system dynamic process under control (i.e. wide-area oscillations).

• WACS represent a true cyber-physical system that requires:• Tools for design,

• Tools for simulation and

• Tools for hardware firmware deployment

for which technology today is not available in the power systems domain.


Damping Controller WACS (implemented at SmarTS-Lab)


Outline

• Cyber-Physical Systems• Key Concepts from Hybrid Systems• Key Characteristics of Hybrid Systems• Consequences of increased ICT• PMU-Based Wide Area Control Systems• Damping Controller WACS

• Cyber-Physical Systems Modeling• Systems Data Modeling• Model Development• CIM for model simulation• Modelica Simulation Engine



• Structural diagrams represent the static behaviour of the system

• Object, attributes, operations and relationships

• Class diagrams,• Component diagrams,

• Unified Modeling Language (UML) is a general purpose standard modeling language

• Information model representing real world objects

• Structured description of the semantics of a set of information

• Information stored in XML file format

• UML is compatible with Object Oriented software development methods

• Behavioral diagrams represents the dynamics of the system, collaboration among objects

• Sequence diagrams,• State diagrams,• Activity diagrams

Systems Data Modeling(uml)


• Common Information Model (CIM) is conceived for information exchange: power systems topology, equipment, measurements

• Standardization of the model diagrams for cyber-physical components,• IEC61970 provides standard data model for

power systems components

• Components involved in the generation layer: generators, turbine governors, etc

• Components involved in transmission and distribution layer: capacitors, protections, etc.

• Using UML representation to design a structured data model: Semantic transformation from real world to a model

Systems Data Modeling(CIM power systems)


• Modeling language based on equations, allow specification of mathematical models

• Multi-Domain modeling

• Visual Acausal Hierarchical Component Modeling

• Physical structure• No data flow direction

• Typed Declarative Equation-based Textual Language

• Decopling of models from solvers

model DCMotorModelica.Electrical.Analog.Basic.Resistor r1(R = 10);Modelica.Electrical.Analog.Basic.Inductor i1;Modelica.Electrical.Analog.Basic.EMF emf1;Modelica.Mechanics.Rotational.Inertia load;Modelica.Electrical.Analog.Basic.Ground g;Modelica.Electrical.Analog.Sources.ConstantVoltage v;

equationconnect(DC.p,R.n);connect(R.p,L.n);connect(L.p,EM.n);connect(EM.p,DC.n);connect(DC.n,G.p);connect(EM.flange_b,load.flange_a);

end DCMotor;

load

EM

DC

G

R L

Electrical

Mechanics

model GENROUparameter Complex It=conj(S/VT) “Some comments here“;

parameter Complex Is = It + VT/Zs; parameter Complex fpp = Zs*Is; parameter Real ang_P=arg(fpp); parameter Real ang_I=arg(It); parameter Real ang_PI=ang_P-ang_I; parameter Real psi = 'abs'(fpp);equation

der(Epq) = (1/Tpd0)*(Efd0 -XadIfd); der(Epd) = (1/Tpq0)*(-1)*(XaqIlq);

…anglev =atan2(p.vi, p.vr);Vt = sqrt(p.vr^2 + p.vi^2);

anglei =atan2(p.ii, p.ir); I = sqrt(p.ii^2 + p.ir^2);

…end GENROU;

Variable declaration

DAE and ODE Equations

Model Development(modelica language)


• The FP7 iTESLA project develops a high level library for modeling power grid components

• Generators,• Governors,• Controls,• Branches,• Loads,• Buses,• Events

• The library makes available standardized power systems models usually available in power system tools only accessible through proprietary (and expensive) licenses

• E.g.: Model in modelica validated against a PSSE model

Model Development(power systems library)


• Modelica provides data definition and compilers for equation based modeling

• ModelicaML is a tool to create UML definition for Modelica models

• Design of classes, components and models using a data model representation:

• Definition of start values for components and definition of mathematical equations

• Code generation creates classes and models with relation between classes

• CIM and Modelica follow the concept of Object Oriented Programming,

• We use the CIM/UML definition for generating Modelica Code

CIM for Model Simulation(uml for modelica)


• Automatic generation of Modelica code from CIM/UML definition.

• Process Data flow

• Manual design of CIM/UML definition and Mapping

• Loading CIM/XML and Mapping

• Semantic transformation into Modelica code:

• Set initial values from power flow

• Set connection between classes

• Simulation with Modelica tools

• Mapping

• Relation between CIM classes and Power system library classes

• CIM Attributes and values -> Modelica Variables and starting values

• CIM relations between classes -> Modelica connection between components

or

• CIM relations between classes -> Use of Modelica classes inside main class: class instances

CIM for Model Simulation (cim 2 modelica)


Modelica Simulation Engine (simulation engine)

Prop

ertie

s

Resu

ltsHDF5

JMOMC

PYTHON

JAVA

Dymola

• Open-source software for cyber-physical system simulation

• Plug-in different compilers and solvers

The 7th International Conferenceon Real-Time Simulation Technologies

Montreal | 9-12 June, 2014

Thank you! Questions?

[email protected] , [email protected] Power Systems Dept.

KTHStockholm, Sweden

[email protected] [email protected]

Research and Development Division Statnett SF

Oslo, Norway

Towards CIM Compliant Model-Based Cyber-Physical Power System Design and Simulation using Modelica

Engineering

Transcript of Towards CIM Compliant Model-Based Cyber-Physical Power System Design and Simulation using Modelica