opal-rt rt14 conference: sesa laboratory

The 7th International Conferenceon Real-Time Simulation Technologies

Montreal | 9-12 June, 2014

Dr. Sebastian Rohjans

OFFIS – Institute for InformationTechnology

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

THE SMART ENERGYSIMULATION ANDAUTOMATION LAB (SESA)


Future Energy Systems

Integration of high numbers of active components into control systems Supply-dependent and uncertainty about forecasts

Automated operational optimization required

LV LV

MV


Managing Complexity

Smart Grids are complex systems

Field Testing:

Expensive

Inflexible

Dangerous

Simulation


Simulation of Smart Energy Systems

Suitable consideration of all facets is essential!

Formal analysis vs. simulation

Integration of heterogeneous models and simulators

1° at 50 Hz1 cycle1 second1 minute1 hour1 day

Partial Differential Equations (PDE)

Ordinary Differential Equations (ODE)

Algebraic Equations/Phasors (AE)

Discrete Automata (DA)

Time Series (TS)

Probability Density Functions (PDF)

Static Rules/Descriptions (SR)

V

Hz

computational expensebased on the

model representation

„trusted“

„untrusted“

Model Representations


Simulation of Smart Energy Systems (cont`d)

Use case specific „functional“ combination Simplified discrete models

High-precision (dynamic) models

Automated composition and orchestration of heterogeneous models in dependence of Availability of models

Availability of time

Availability of budget

Inspected matter

Assessing the quality of conducted experiments Systematic „errors“

Statistical significance

A recurring process!

MS

MS

MS

NS

NS

NS

NS

NS

NS

NS

NS

NS


SESA-LabHard- and Software Integration Platform

Real-Time Automation Lab(University of Oldenburg)

Co-Simulation Platform(OFFIS – Institute for Information Technology)


mosaikmosaik – Modular Smart Grid Co-Simulation

Automated composition and orchestration of heterogeneous energy system models

Flexible mosaik-API for simulators, control strategies (MAS), etc.

Powerful description language for scenarios (rule-based instantiation and linking of models)

Smart Grid reference model


mosaikExample Scenario

# Starting Simulatorspypower = start('PyPower')hhsim = start('HouseholdSim')pvsim = start('CSV')

# Instantiating modelsgrid = pypower.Grid(gridfile=GRID_FILE)houses = hhsim.ResidentialLoads(

profile_file=PROFILE_FILE)pvs = pvsim.PV.create(20)

# Connecting Entitiesconnect_by_node_id(houses, grid)connect_randomly(pvs, grid.filter_name('node_*'))


mosaikmosaik – Modular Smart Grid Co-Simulation

Automated composition and orchestration of heterogeneous energy system models

Flexible mosaik-API for simulators, control strategies (MAS), etc.

Powerful description language for scenarios (rule-based instantiation and linking of models)

Smart Grid reference model

Event-based simulation (coordinated execution and data exchange)

After a testing phase with international partners it is now available as Open Source Software (http://mosaik.offis.de)

http://mosaik.offis.de



Real-Time Automation Lab(University of Oldenburg)

Co-Simulation Platform(OFFIS – Institute for Information Technology)


SESA-LabTopology Independent Linking and Allocation of I/O (analog and digital)

...VPN Gateway Router/Firewall

Internet

HERO Cluster (High-End Computing Resource Oldenburg)

25 (max. 100) Sessions @ 1Gbit/s

...

...

6x BECKHOFF CX2020RT-Device-TargetsComponent Models

AgentsController

18x

Communication Simulation

D/A

EtherCAT

Ethernet

96x Ports

48x Ports

I/O-Switch

Station DisplaysSwitching StateUtilizationLabViewConsole

2x6x CPUs @ 2,6 GHz

Virtualization Servermosaik-SimulationDevelopment EnvironmentLicense Server

Control StationSCADA

IEC 60870

IEC

618

50

U

ML

CIM

(619

70/6

196

8)

OP

C U

A (

62

541)

Standard-compliant Information- and

Process Chain

Protocol Switch 18x

6x Cores (~140 Nodes)

...

Station ComputerControl SystemProtection SystemComponent ModelsAgentsController

3x BECKHOFF C6920

D/A

D/A

Projected Extensions(2014-2016)

RT-Grid-TargetPower Grid Models

Component Models

3x Cores (~70 Nodes)

OPAL-RT eMEGAsim



Highly dynamic

Steady State

Opal-RT eMEGAsimReal-time simulator

Models created with Matlab/Simulink


First Tests eMEGAsimSimPowerSystems

mm


First Tests eMEGAsimSimPowerSystems

m

Ueff

Ieff

PLoad

PGeneration

Wait forreal time


Coupling Requirements

Real-time

Continuous

μs resolution Transients

Time domain Harmonics

Simulation time

Discrete event

sec resolution

Frequency domain

Tool A (eMEGAsim) Tool B (mosaik)

Latency handling

A B

Real-time!Information exchange!

fft,

wavelets,

ML?

Loss handling

Accuracy!

A B

Uncertaintyquantification

Effectivity!

A ! B

Scheduling


Future Developments

SimPowerSystems instead of Simscape

Interfacing between SPS and Simulink

General approach for RT scenario component

Event identification for dynamic simulation

Dynamic real-time scheduling

Framework for global uncertainty quantification


Summary

Transition towards Smart Grids implies significant changes in the overall energy system’s architecture and infrastructure

Continuously increasing level of the system’s complexity

For managing, analyzing, and understanding this novel Smart Grid system, simulation approaches have to be adopted

Although different approach already exist, most of them are still developed for a certain purpose and do not follow a modular, composable and integrative approach

However, as Smart Grid simulation is a highly interdisciplinary field of application, simulation suites (tools) should fill this gap

opal-rt rt14 conference: sesa laboratory

Technology

international conference

smart energy simulation

instantiating models

international partners

automation lab sesa

sesalab hard

smart grids

x cores