The document discusses the modeling and simulation of smart homes using energy management systems (EMS) and demand-side management (DSM) techniques, highlighting their benefits such as reducing energy costs and CO2 emissions. It presents four case studies, including the e-mobilie project, which demonstrates real-world applications and integration of smart home technology. Additionally, the experiences and economic advantages of using SimulationX for evaluating smart home models are elaborated upon.

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Practical Experiences With Smart Homes Modelling and
Simulation
November 24-25, Dresden
November 24th
Wessam El-Baz, Christian Kandler, Patrick Wimmer, Mark Windeknecht, and Peter Tzscheutschler

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Agenda
• Smart Home Modelling
• Case Study #1: e-MOBILie Project
• Case Study #2: Micro-CHP in the Loop
• Case Study #3: SOFC Modelling and Simulation
• Outlook

3. 2
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Note:
This presentation was published together with a technical paper.
The full paper can be downloaded here.
Paper Abstract
Within the next years, more homes will be equipped with smart metering devices, intelligent devices and home
energy management systems (HEMS). The EMS are designed to adapt Demand Side Management (DSM) to
households. The goals behind the DSM can vary within the household. It can target shaving the load peaks,
minimize CO2 emissions, or minimize the overall energy bill via controlling the in-house energy supply resources
and intelligent consuming devices. Thus, the EMS represents the dominant ‘smart home’.
Through this contribution, different practices of smart home modeling will be presented in which SimulationX has
been integrated under different configurations, software and hardware integrations. The developed models
represent the state-of-the art of the current, upcoming and futuristic smart homes. The incentives behind
developing these models will be deliberated, along with the economic advantages in its applications within the
smart grid. Moreover, the experience behind using SimulationX for evaluating such models will be presented.

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Smart Home Models
P
t
€
model
Occupants Activity
Simulation
Irradiance
data
parametersprocessingoutput

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Agenda
• Smart Home Modelling
• Case Study #1: e-MOBILie Project
• Case Study #2: Micro-CHP in the Loop
• Case Study #3: SOFC Modelling and Simulation
• Outlook

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e-MOBILie
Project Background
Goals
• Development and implementation
of hierarchical and distributed
energy management systems
• evaluation of the environmental
benefits of a combination between
an electric vehicle and local energy
generation
Main focus:
• Implementation and operation of an
hardware-in-the-loop test bench for
evaluating the integrated energy
management concept (iEM)
• Demonstration of these concepts in
a real residential building and a
plus-energy parking garage

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Simulation model framework
e-MOBILie
MATLAB
Physical
Simulation
Building, electrical and
thermal components
SimulationX
[Modelica]
Optimization
Home Energy
Management System
GAMS
[CPLEX solver]
Rolling Horizon
© TUM IfE 69-056-L15

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Results
e-MOBILie
0%
5%
10%
15%
20%
25%
30%
DSM Devices Electric Vehicle Battery Storage Heatpump HEMS
AnnualCostsSavingsPotential[%]
Components
© TUM IfE 69-064-L16
Building: EnEV2012+
PV: 7 kWp
Battery Storage: 10 kWh
Driving Profile: Commuter
Electricity Tariff: variable

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Copyright © ESI Group, 2016. All rights reserved.
Agenda
• Smart Home Modelling
• Case Study #1: e-MOBILie Project
• Case Study #2: Micro-CHP in the Loop
• Case Study #3: SOFC Modelling and Simulation
• Outlook

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9
Test Bed
+ Accuracy
+ Micro CHP Dynamics
+ Operations Constrains
- Costs
- Time
- Lack of building dynamics

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Why HiL?
10
Test BedSimulations
• Building is Modelled
• Thermal Load Profile is generated
• Cooling circuit emulate thermal load via
heat exchanger
• CHP cover the generated load

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Why HiL?
11
Test BedHardware in the loop (HiL)
Test BedSimulations
Feedback Loop

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Whispergen Testbed Hydraulic Schematic
12
Source:J. Lipp, F. Sänger, Potential of power shifting using micro–CHP units and heat storages, Microgen 2013,
Naples, Italy, 2013

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SimulationX Model Layout
13

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Operation Strategy Overview
14

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Supply-Return Temperature Interaction
1
1
2 3
2
3

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Supply-Return Temperature Interaction
16
1 2 3

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Supply-Return Temperature Interaction
17
TRef= 48.5 °C
TAct= 43 °C
TReturn= 36 → 34 °C

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Copyright © ESI Group, 2016. All rights reserved.
Agenda
• Smart Home Modelling
• Case Study #1: e-MOBILie Project
• Case Study #2: Micro-CHP in the Loop
• Case Study #3: SOFC Modelling and Simulation
• Outlook

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SOFC micro CHP
GreenBuilding modell with self-written SOFC CHP typ

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SOFC micro CHP
Characteristics of SOFC
0,0 kW
0,2 kW
0,4 kW
0,6 kW
0,8 kW
1,0 kW
1,2 kW
1,4 kW
1,6 kW
15°C 20°C 25°C 30°C 35°C 40°C 45°C 50°C 55°C 60°C 65°C
Thermal Power Heat Efficiency
Heat power and efficiency depending of the return temperature [1]

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SOFC micro CHP
Heat output of the SOFC over one year (reference case)
0,0 kW
0,1 kW
0,2 kW
0,3 kW
0,4 kW
0,5 kW
0,6 kW
0,7 kW
0,8 kW
0,9 kW
0d
7d
14d
22d
29d
36d
43d
50d
57d
65d
72d
79d
86d
93d
100d
108d
115d
122d
129d
136d
143d
151d
158d
165d
172d
179d
186d
194d
201d
208d
215d
222d
229d
237d
244d
251d
258d
265d
272d
280d
287d
294d
301d
308d
315d
323d
330d
337d
344d
351d
358d
Heat Power Fuel Cell Reference Case

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SOFC micro CHP
Heat output of the SOFC over one year (35°C Case)
0,0 kW
0,1 kW
0,2 kW
0,3 kW
0,4 kW
0,5 kW
0,6 kW
0,7 kW
0,8 kW
0,9 kW
0d
7d
14d
22d
29d
36d
43d
50d
57d
65d
72d
79d
86d
93d
100d
108d
115d
122d
129d
136d
143d
151d
158d
165d
172d
179d
186d
194d
201d
208d
215d
222d
229d
237d
244d
251d
258d
265d
272d
280d
287d
294d
301d
308d
315d
323d
330d
337d
344d
351d
358d
Heat Power Fuel Cell Reference Case Heat Power Fuel Cell 45°C Case Heat Power Fuel Cell 35°C Case

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Smart Heat- Electricity Micro-Grid
Outlook
23
CHP CHP HP CHP HPHPElectricity grid
DH Return
DH Supply
Electricity Heat

25. 3
Copyright © ESI Group, 2017. All rights reserved.
www.esi-group.com
Download the Paper
This presentation was published together with a technical paper.
The full paper can be downloaded here.
Paper Abstract
Within the next years, more homes will be equipped with smart metering devices, intelligent devices and home
energy management systems (HEMS). The EMS are designed to adapt Demand Side Management (DSM) to
households. The goals behind the DSM can vary within the household. It can target shaving the load peaks,
minimize CO2 emissions, or minimize the overall energy bill via controlling the in-house energy supply resources
and intelligent consuming devices. Thus, the EMS represents the dominant ‘smart home’.
Through this contribution, different practices of smart home modeling will be presented in which SimulationX has
been integrated under different configurations, software and hardware integrations. The developed models
represent the state-of-the art of the current, upcoming and futuristic smart homes. The incentives behind
developing these models will be deliberated, along with the economic advantages in its applications within the
smart grid. Moreover, the experience behind using SimulationX for evaluating such models will be presented.

26. 24www.esi-group.com
Copyright © ESI Group, 2016. All rights reserved.
M.Sc.
Wessam
El-Baz
Lehrstuhl für Energiewirtschaft
und Anwendungstechnik
Technische Universität München
Fakultät für Elektrotechnik und
Informationstechnik
Arcisstraße 21
80333 München
Tel +49 89 289-28314
Fax +49 89 289-28313
wessam.elbaz@tum.de
Questions