Practical Experiences with Smart-Homes Modeling and Simulation

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Copyright © ESI Group, 2016. All rights reserved.Copyright © ESI Group, 2016. All rights reserved.

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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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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

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.

https://www.simulationx.de/kundencenter.html

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Smart Home Models

P

t

€

model

Occupants Activity

SimulationIrradiance

data

par

amet

ers

pro

cess

ing

ou

tpu

t

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Agenda





• Outlook

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e-MOBILie

Project Background

Goals• Development and implementation

of hierarchical and distributedenergy management systems

• evaluation of the environmental benefits of a combination betweenan electric vehicle and local energygeneration

Main focus:• Implementation and operation of an

hardware-in-the-loop test bench forevaluating the integrated energymanagement 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

An

nu

al

Co

sts

Savin

gs P

ote

nti

al

[%]

Components

© TUM IfE 69-064-L16

Building: EnEV2012+PV: 7 kWpBattery Storage: 10 kWhDriving Profile: CommuterElectricity Tariff: variable

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Agenda





• 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

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Supply-Return Temperature Interaction

1

1

2 3

2

3

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16

1 2 3

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17

TRef= 48.5 °C

TAct= 43 °C

TReturn= 36 → 34 °C

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Agenda





• 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

0 d

7 d

14

d2

2 d

29

d3

6 d

43

d5

0 d

57

d6

5 d

72

d7

9 d

86

d9

3 d

10

0 d

10

8 d

11

5 d

12

2 d

12

9 d

13

6 d

14

3 d

15

1 d

15

8 d

16

5 d

17

2 d

17

9 d

18

6 d

19

4 d

20

1 d

20

8 d

21

5 d

22

2 d

22

9 d

23

7 d

24

4 d

25

1 d

25

8 d

26

5 d

27

2 d

28

0 d

28

7 d

29

4 d

30

1 d

30

8 d

31

5 d

32

3 d

33

0 d

33

7 d

34

4 d

35

1 d

35

8 d

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

0 d

7 d

14

d2

2 d

29

d3

6 d

43

d5

0 d

57

d6

5 d

72

d7

9 d

86

d9

3 d

10

0 d

10

8 d

11

5 d

12

2 d

12

9 d

13

6 d

14

3 d

15

1 d

15

8 d

16

5 d

17

2 d

17

9 d

18

6 d

19

4 d

20

1 d

20

8 d

21

5 d

22

2 d

22

9 d

23

7 d

24

4 d

25

1 d

25

8 d

26

5 d

27

2 d

28

0 d

28

7 d

29

4 d

30

1 d

30

8 d

31

5 d

32

3 d

33

0 d

33

7 d

34

4 d

35

1 d

35

8 d

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

3


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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.

https://www.simulationx.de/kundencenter.html

24www.esi-group.com


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

[email protected]

Questions

Software

Practical Experiences with Smart-Homes Modeling and Simulation