# Vision d'un industriel

Gilles Plessis - ENERBAT

SIMUREX 2 - Avril 2012

This is an Open Access article distributed under the terms of the Creative Commons Attribution License 2.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
# Vision d'un industriel

Gilles Plessis - ENERBAT

SIMUREX 2 - Avril 2012

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# Outline

* EDF challenges and tools for building energy systems

* Modelica for EDF

* Library for buildings and systems models from EDF

* Use cases

* Multi-physic model and parametric analysis

* Stochastic modeling: building properties & occupancy

* MOR for LTI and LTV systems

* Grey box modeling

* Generating standalone executable

* Modeling the building stock by aggregation

* MOR for a building stock model – Test on 37 building in Nice

* Dynamic calibration

* Optimization by genetic algorithm

* Hardware in the loop
# EDF Challenges

## Conception & Prototyping

- New technologies (HP, PV, VIP...)

- New services (Energy management...)

## Sizing

- Tools for design office

- Building refurbishment operation

## Simulation

- Renewable energy potential

- LCA of building and systems

- Sustainable cities

## Diagnostic

## Optimization

- Control algorithm (load management)

- Smart grid optimization

WTC (Watt/Degre Celsius)
unite qui exprime les dépendances
thermiques ou portes de chaleur
ou travers d'une paroi

Touature 10% (24 W/C)

Ventilation 28% (88 W/C)

Murs et points thermiques* 25% (82 W/C)

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Fenditures et portes-fenêtres 21% (51 W/C)

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# Tools used and developed by EDF (2010)

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# Building energy simulation tools

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# Why Modelica ...?

equation
G*dT = Q_flow;
end ThermalConductor;

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## Language for modeling complex physical systems

- Non causal and equation based language

- Multi-physics modeling

- Standardized interactions between models

- Formal expression of the equations

- The model follows the topology of the physical system

- Easy to understand and improve

equation
Q_flow - G*dT = 0;
end ThermalConductor;

equation
Q_flow = G*dT;
end ThermalConductor;

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## Normalized language

- Object-oriented programming (inheritance, maintainability...)

- Non proprietary language

model ThermalConductor
 extends Interfaces.Element1D;
 parameter ThermalConductance G
 "Constant thermal conductance of
 material";
equation
 Q_flow = G*dT;
end ThermalConductor;

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Peter Fritzson, 2011. Introduction to Object-Oriented Modeling, Simulation and Control with Modelica. Tutorial for Modelica conference 2011
# Modelica and Dymola Short History

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## References

* Per Sahlin, 2000. The methods of 2020 for building envelope and HVAC systems simulation—will the present tools survive?

* Essam O Aasem, 1993. Practical simulation of buildings and air-conditioning systems in the transient domain. PhD Thesis

* ....
# Main advantages for EDF R&D

## Development

* Reduce the developing time and increase the code efficiency

* Broad spectrum of mathematical, physical and engineering fields (time scales...)

* Focus on developing accurate physical models and avoid coding problems

* Risk limitation when developing stand alone applications

* Robust solution (symbolic calculation)

## Use & Maintenance

* Increase the exchanges between the developers, practitioners and the external cooperation

* Increase the reliability of the corrective and preventive maintenance

* Boost the design of technologies using a multi-domains / multi-physics approach

* Better abstraction and increase reuse of models
# Building and Energy tools based on Modelica

## Building libraries

- Buildings & BCVTB

- ATPlus library

- HumanComfort Library

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TECHNISCHE UNIVERSITÄT
KAISERSLAUTERN

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Modelon

XRG
XRG Simulation GmbH

## Thermodynamic Libraries

- Modelon libraries (AirConditioning, Hydraulics)

- TILSuite and StateViewer

- ThermoSysPro

- ThermoBondLib

eDF

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Fraunhofer

MODELICA

## Fluid & thermal library from Modelica Standard Library

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# Model Library for buildings and systems from EDF

## Modèles de base

- Utilitaires (météo, matériaux, fluides, outils d'analyse, scénarios)

- Calculette solaire

- Thermique pure

- Thermo-hydro-aéraulique

- Thermodynamique

- Systèmes - Composants

NB: les modèles sont génériques

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## Assemblages dont études et bâtiments types

- Thermique Pure

- Thermo-hydro-aéraulique

- Systèmes

NB: les modèles sont pré-paramétrés par défaut mais restent modifiables
# Focus on thermal elementary components

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## Architecture des classes

* Elements (physique)

* Interfaces

* Conditions limites

* Modèles d'échanges

* Capacités thermiques

* Capteurs

* Composants (spécifique métier)

* Conduction homogène, 1 nœud 1D, 1 seul matériau

* paroiNCouchesHomogènes, m nœud 1 D n matériaux

* ParoiComplete ajout de convection

* ParoiRad ajout du rayonnement

* Vitrage générique et spécifique DVitrage

## ZonesThermiques

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# One-zone thermal model

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Modèle de base : monozone sur terre plein vitré

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# Building and system example

* Maison type

* Météo

* Systèmes

* Chauffage PAC R/0

* Réseau d'eau chaude

* Emetteurs

* Contrôle

* Scénarios

* Consigne

* Apports internes

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# Use case: Multi-physic model of a PV generator

## Fields

- Conception and prototyping

## Description

- Electrical part

- Two-diode model

- Thermal part

- Multi layer conduction

- Radiation to the surrounding

- Sun rays absorption

## Advantages

- Increase reuse of models

- Ease of modeling

- Reduce the development time

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# Use case: Complex multi-physics model and parametric analysis

## Fields

- Conception and prototyping

## Description

- Components

- Building model

- Heat pump & SHW

- Control and scenarios

- Parameter sensitivity

## Advantages

- Ease of modeling (same topology)

- Reduce the development time

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# Use case: Stochastic modeling

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## Fields

- Simulation, sizing and diagnostic

- Load management

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## Description

- Residential Low Energy Building

- 1/ Uncertainty estimation

- Building properties $\lambda \rightarrow \mathcal{N}(0.03, 0.005)$

- Air change rate $\rightarrow \mathcal{N}(0.5, 0.05)$

- 2/ Occupancy modeling

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±34% on the annual
heat demand

Space heating [Wh] over the year for a LEB

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Influences:
- occupancy
- availability
- Priorities:
  Logical rules

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Probabilistic
Schedule

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Daily power
load

Power load for
each equipment

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Washing machine load [W]

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500

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5500

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6500

Time [s] over a day
# Use case: Fast simulation using model order reduction (MOR)

## Fields

- Simulation, optimization...

## Description

- MOR of models for time consuming studies

## Advantages

- Preserve the dynamic behavior

- Reduce the simulation time (~30 to 100 times faster)

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# Use case: Model Order Reduction for linear time variant

## Fields

- Simulation, optimization...

## Description

- MOR of models for time consuming study

## Advantages

- Preserve the dynamic behavior

- Reduce the simulation time (~5 times faster)

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Linear Time Variant

$$ egin{cases} \dot{X} = A(t) \cdot X + B(t) \cdot U \\ Y = C(t) \cdot X + D(t) \cdot U \end{cases} $$

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# Use case: Grey box modeling

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## Fields

- Conception & prototyping, Sizing...

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Dymola
detailed model

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Linearization
and reduction

$$FT_j(s) = K \cdot \sum_{i=1}^{N_r} \frac{k_i}{(s - p_i)} \quad j \in \{1, \dots, N_{\text{sol}}\}$$

Exact 2nd order
model

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Parametric
regression

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## Description

- Parametric analysis from reduced $p_i, k_i, K_i \dots = f(\text{Building Age}, U_{\text{bat}} \dots)$

Parametric 2nd
order model

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## Advantages

- Ensuring accuracy

- Few parameters from typological studies or early design stages

Validation over a week for a Low Energy
Building

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# Use case: Generating standalone executable

## Fields

- Conception, sizing, simulation...

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## Description

Using studies and/or models to generate executables

## Advantages

- Reduce the development time

- Improved reactivity

- Diversity (1 model → x executables)
# Use case: Modeling the building stock by aggregation

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## Fields

- Simulation, optimization, conception (building stock and smart-grid)

<table><thead><tr><th>GV</th><th>SH</th><th>Inertie</th><th>%SV</th><th>abs fenetre</th><th>trans fenetre</th></tr></thead><tbody><tr><td>336</td><td>95</td><td>1</td><td>0.08</td><td>0.13</td><td>0.6</td></tr><tr><td>837.64</td><td>150</td><td>1</td><td>0.08</td><td>0.13</td><td>0.6</td></tr><tr><td>678.48</td><td>135</td><td>1</td><td>0.08</td><td>0.13</td><td>0.6</td></tr><tr><td>1757.34</td><td>135</td><td>1</td><td>0.08</td><td>0.13</td><td>0.6</td></tr><tr><td>2070.21</td><td>135</td><td>0</td><td>0.08</td><td>0.13</td><td>0.6</td></tr><tr><td>335.98</td><td>95</td><td>1</td><td>0.08</td><td>0.13</td><td>0.6</td></tr><tr><td>1757.34</td><td>135</td><td>1</td><td>0.08</td><td>0.13</td><td>0.6</td></tr></tbody></table>

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## Description

- Typology and greybox model

- Meteo file : Nice

- Electric heating + controller

- Stochastic behavior of the occupants

- Variable time step solver

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# Use case: MOR for a building stock model – Test on 37 buildings in Nice

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## Fields

- Simulation, optimization, conception
  (building stock and smart-grid)

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## Description

- Aggregation of greybox model

- Linearization

- MOR and parametric study

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## Linearization and MOR

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High order (74*74)

Energy
consumption
discrepancy 5...7%

Power load max
discrepancy 20%
# Use case: Dynamic calibration

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## Fields

- Diagnostic, fitting to experiments

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## Description

- Greybox model

- Sollicitations: heating load and weather data

- Experiment: 4 days and time step 60s

- Initialization GV=300W/°C & SH 225 m²

<table><thead><tr><td>GV</td><td>Error</td></tr></thead><tbody><tr><td>300</td><td>361218</td></tr><tr><td>217882</td><td>375.631</td></tr><tr><td>79.1635</td><td>5.6837</td></tr><tr><td>79.1828</td><td>5.6805</td></tr><tr><td>79.1828</td><td>5.6805</td></tr></tbody></table>

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# Use case: Optimization by genetic algorithm

## Fields

- Conception, prototyping, sizing and optimization...

## Description

- Sizing of windows to minimize the heat demand

## Advantages

- Increase reuse of models

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<table><thead><tr><th>Tuner parameters</th><th></th><th></th><th></th><th></th><th></th><th></th></tr><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td></td><td>zoneRDC.fenetreS.S</td><td>11.249266862170089</td><td>-3.3137829911211725e-5</td><td>0</td><td>12</td><td></td></tr><tr><td></td><td>zoneRDC.fenetreW.S</td><td>9.6422287390029329</td><td>8.0821087390029334</td><td>0</td><td>12</td><td></td></tr><tr><td></td><td>zoneRDC.fenetreN.S</td><td>1.759530791788563</td><td>-0.7741892082111439</td><td>0</td><td>12</td><td></td></tr><tr><td></td><td>zoneRDC.fenetreE.S</td><td>11.401759530791789</td><td>6.920819530791789</td><td>0</td><td>12</td><td></td></tr><tr><td></td><td>zoneET.fenetreS.S</td><td>6.3695014662756595</td><td>-4.1289985337243404</td><td>0</td><td>12</td><td></td></tr><tr><td></td><td>zoneET.fenetreW.S</td><td>8.7507331378299114</td><td>6.5689131378299113</td><td>0</td><td>12</td><td></td></tr><tr><td></td><td>zoneET.fenetreN.S</td><td>6.9325513196480939</td><td>-0.6099686803519058</td><td>0</td><td>12</td><td></td></tr><tr><td></td><td>zoneET.fenetreE.S</td><td>8.9032258064516121</td><td>5.0909058064516124</td><td>0</td><td>12</td><td></td></tr></tbody></table>

<table><thead><tr><th>Criteria</th><th></th><th></th><th></th><th></th><th></th><th></th></tr><tr><th></th><th></th><th></th><th>scaled criteria</th><th>diff. to start</th><th>unscaled criteria</th><th>usage</th><th>demand value</th></tr></thead><tbody><tr><td></td><td>BesoinRDC.lt</td><td></td><td>4.6256316065514839e+3</td><td>49.9%</td><td>4.6256316065514839e+3</td><td>minimize</td><td>1</td></tr><tr><td></td><td>BesoinET.lt</td><td></td><td>5.5683367745934329e+3</td><td>-59.8%</td><td>5.5683367745934329e+3</td><td>minimize</td><td>1</td></tr><tr><td></td><td>Sum of squared criteria</td><td></td><td>5.2402842194817662e+7</td><td>-73.9%</td><td></td><td></td><td></td></tr></tbody></table>
# Use case: Hardware in the loop

## Fields

- Conception, prototyping, optimization...

## Description

- Testing control devices for HVAC systems by emulation (real time and accelerated simulation)

## Advantages

- Reuse basic models

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# Questions ??

Team project

C. Muresan, A. Kaemmerlen, H. Bouia,

D. Covalet, M. Schumman, S. Filfli...

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