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(1)

OPTI-Sim: Co-simulation based virtualization of large scale DHC-networks

Wolfgang Birk 1 ,

Yvonne Ritter 2 , Nicklas Linder 2 , Ulrich Odefey 2 , Peter Lingman 3 , Vikas Chandan 4

1 Luleå University of Technology, 2 TWT GmbH Science & Innovation,

3 Optimation AB, 4 IBM India

(2)

Outline

• Motivation and approach

• Example for a thermal grid

• Challenge

• State of the art

• Automatic model generation

• Co-simulation

• Model integration methods

• Use cases

• Conclusions & Outlook

(3)

Motivation and approach

• Model-based development enables more efficient and accurate engineering solutions.

• Dynamic modeling and simulation can generate new insight.

• The OPTi project addresses the optimization of thermal grids.

Tools and methods for

• design of DHC systems

• operation of DHC systems

• increased energy efficiency

”Virtual Twin”

(4)

An example for a thermal grid

(5)

What is the challenge?

Modeling challenges:

• large and complex networks

• various models/granularity

• system dynamics

• simulation performance

• validation Approach:

→automatic model generation and simplification

→co-simulation of complete DHC networks A glimpse on the complexity:

• approx. 23000 double pipes

• more than 400km total pipe length

• more than 9000 buildings

(6)

State of the art

Scientific state of the art:

• Simplified dynamic models for DHC network simulations.

• Usually, the control system is not represented or extremely simplified

State of the art in industry:

• commercial tools based on static models: Termis , TRNSYS and Netsim not suitable to investigate short-term fluctuations in the network

• Open source simulation tool Dhemos, also uses static models

• APROS (from VTT Finland, originally used for nuclear power plants) unclear how control systems can be represented and not modular

• Dedicated and specialized simulators are available at different utilities

(7)

Automatic model generation and simplification (1/3)

The raw data

• Utilities maintain databases on all components.

• GIS data describes the network topology and components

Node

• ID

• X, Y ,Z coordinates

Consumer

• ID

• Node

• Nominal power and flow

• Measured yearly energy and water volume

Pipe

• ID

• Nodes

• Pipe type:

FSPP2X0040/0180

• Inner diameter

• Outer diameter

• Pipe type

• Insulation type

• Length

(8)

Automatic model generation and simplification (2/3)

Remodelling the raw data

• Automatic processing of GIS data ensures up-to-date model

Approach complies with goals of European roadmap for industrial process automation (www.processIT.eu)

Economic modelling?

“Estimate the amount of energy to be reduced”

Data management

More simplified

DHC network data House data

Data management

All details

Data management

Simplified

Transformation

Algorithm

Algorithm

Algorithm

(WP4) Simulation model (FMU)

Control algorithm Algorithm

(WP5) e.g. Pipe:

Diameter, Length

e.g. Pipe:

Volume

Data management

Even more simplified Data management

Most simplified

(9)

Returning to the example

• Luleå grid: > 10,000 consumers, > 45,000 pipes, 4 production units, sensors, pumps, valves

• Need for network reduction, simplification and automatic generation of dynamic models

Automatic model generation and simplification (3/3)

Reduction algoritms

Automatic model generation

FMU

GIS data

(10)

Co-simulation of complete DHC networks

(11)

Co-Simulation Framework

TWT CoSimLab manages signal exchange between

• multiple simulations, running in

• different tools, possibly located on

• multiple hosts.

Features:

• implemented in Java

• control and monitoring GUI

• connectors for several simulation tools

• FMI compliant

TWT CoSimLab

CoSim Router

Sim A

CON

Sim C

Master

CON

Sim B

CON

(12)

FMI: Functional Mock-up Interface

• Open interface standard for model exchange and tool coupling

FMI: .xml description of interface

FMU: .xml + model implementation (source or binary)

• Widely adopted (> 30 tools) in various disciplines

OPTi-Sim:

• FMI compliant co-simulation

• Secures flexibility and reusability

Tool

FMU

model

solver

FMI

(13)

Model integration methods

Method 3: Bridged connection to a Functional Mock-up Unit (shared library).

Requires: Simulation tool supporting FMU export.

Method 2: Bridged connection with inter process communication (i.e.

sockets).

Requires: Simulation tool with API in a

programming language supporting

sockets.

Method 1: Direct connection with Java interface.

Requires:

Simulation tool with Java API.

Master

Configure and monitor Co-simulation

Matlab / Simulink

CON

CoSim Router

Modelica

CON

TCP/UDP

StarCCM+

OpenFoam

CON

CON

FMU Connector

FMU

Method 4: Bridged connection with file communication.

Requires: Simulation tool with API supporting

file handling.

Simulation tool

CON

File

Method 5: Functional Mock-up Unit running inside of the Functional Mock-up Trust Center.

FMTC

FMU

CON

FMI

FMI

FMI FMI

FMI

(14)

Pilot use cases for OPTi-Sim

Four pilot use cases are performed by Luleå Energi AB

• 31,000 households

• base heat production: 185 MW

• peak production units: 350 MW

Use cases:

1) Peak load reduction

2) Lowered DH supply temperature 3) Limitations in the DH grid

4) Valve optimisation

(15)

Conclusion

Summary: OPTi-Sim

• facilitates virtual representation of the real DHC network

• features automatic model generation and simplification

• integrates different models using FMI compliant co-simulation

Future challenges

• validation of models and simulations, optimisation and control

• sensitivity analysis

integration of sensor data from real-life network: “tracking simulation”

on-line simulation functionality

(16)

Acknowledgements

Contacting us: www.opti2020.eu, contact@opti2020.eu, or on LinkedIn

References

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