domotics (home automation and energy conservation)

TU-Eindhoven 6-12-2004

Domotics (Home Automation and Energy conservation)

• René Kamphuis

• ECN-DEGO; Energie onderzoek Centrum Nederland

• Duurzame Energie in de Gebouwde Omgeving

• Integrale Concepten/IT & Energie

• kamphuis@ecn.nl

TU-Eindhoven 6-12-2004

Contents

• Developments/drivers• Domotics and energy management• Automation of energy-functions• Potential• Conclusions/benchmark figures

TU-Eindhoven 6-12-2004

Development: Embedding of renewables

TU-Eindhoven 6-12-2004

• Step 1: Internet plus World Wide Web

• Universal base infrastructure• Step 2: Pervasive/ubiquitous computing

• Every appliance has built-in “invisible” computing power• Step 3: Ambient intelligence

• Local intelligence plus global communication• Semantic Web: next generation web

– Data exchange-> Behavior based on Web-semantics

• Intelligent Agents: computers and other appliances “talk”, negotiate, take decisions and cooperate with each other and people

• Step 4: Information systems => Information “eco”Systems

• Clusters of cooperating people and systems• Systems are not aware of user context

Development: WWW

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Development: Price volatility liberalised energy markets

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Price dynamics: Enlarged

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Price dynamics: Clustered

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Price dynamics: residential customer view

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What does the user want: Top-7 of energy related services

1. Energy at the lowest possible cost

……

2. (Automated) Energy saving advice

3. Control of power usage to cheaper periods

4. Low cost Internet using power line carrier data-communication

5. Green energy

…..

6. Electronic warning/burglary alarm

7. Real-time energy Usage survey

….

8….14 Different control functions at a distance

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• Common in utility building sector- Energie management projects typically yield a 10-20 % saving

• Better embedding of small scale renewable generation and storage

• Cost efficient price-/contractcontrol- Costreduction for customers

- Risico management energycompany- Energy cost are becoming more important; variation in usage/load factor- Feedback about usage leads to (manifest) reduction

• Role of IT- Automated saving- Follow user preferences intelligently- Connects to external information

Centralised energy management

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What can conservation rate can be reached ??

E-Box

Internet

Homenetwork

Energy-meter

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E-Box energy control module simulations; http://www.ecn.nl/library/reports/2003/c03017.html

• Input:- Pricepaths (t)

- Required demand(t) and --demand articulation(t)--

• Cleaning and drying

• Cooling

• Home heating and air condition

• Embedding of local generators (PV, micro-CHP)

• Output- Setpoints appliances

- Switching moments

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Validation: Electrically produced hot tapwater (€ct/100 liter)

Prijspath Minimal f Average f Maximal f Sigma

APX Januari 16.01 0.43 37.90 1.01 84.97 2.27 16.81 APX0901 19.35 0.52 54.74 1.46 186.12 4.96 35.66 APX2108 11.32 0.30 196.99 5.25 589.41 15.72 218.92 APX Augustus 11.55 0.31 38.23 1.02 90.39 2.41 26.77 Double Meter 18.95 0.51 38.31 1.02 46.78 1.25 12.59 Single Meter 37.50 1.00 37.50 1.00 37.50 1.00 0.00

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E-Box Heating + powerdelivery/consumption (€ct/dag)

Appliances Pricepath Min. f Average f Max f Sigma

CV-gas Gas 190.73 1.00 190.73 1.00 190.73 1.00 0.00 E-heatpump Double meter 207.65 1.09 207.65 1.09 207.65 1.09 0.00 E-heatpump Single meter 188.17 0.99 188.17 0.99 188.17 0.99 0.00 E-heatpump APX-Januari 210.49 1.10 231.52 1.21 252.67 1.32 11.15 E-heatpump APX-0901 290.39 1.52 373.05 1.96 461.20 2.42 54.97μ-WKK Double meter 158.10 0.83 158.10 0.83 158.10 0.83 0.00μ-WKK Single meter 160.94 0.84 160.94 0.84 160.94 0.84 0.00μ-WKK APX_Januari 150.73 0.79 154.08 0.81 157.41 0.83 1.76μ-WKK APX-0901 119.35 0.63 133.30 0.70 146.39 0.77 8.70E-Spaceheating Double meter 519.13 2.72 519.13 2.72 519.13 2.72 0.00E-Spaceheating Single meter 470.43 2.47 470.43 2.47 470.43 2.47 0.00E-Spaceheating APX-Januari 526.24 2.76 581.16 3.05 638.25 3.35 32.62E-Spaceheating APX-0901 725.98 3.81 938.95 4.92 1,173.46 6.15 166.58

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E-Box Concerted consumption and generation

Apparaten Prijspad Min. f Gemiddeld f Max f Sigma

Cooling foodstuffs/PV Dubbele Meter -14.44 1.29 -13.78 1.23 -13.41 1.20 0.30Cooling foodstuffs/PV Een Meter -11.36 1.01 -11.20 1.00 -11.03 0.98 0.12Cooling foodstuffs/PV APX-Augustus -19.70 1.76 -19.01 1.70 -18.15 1.62 0.31Cooling foodstuffs/PV APX-2108 -125.45 11.20 -117.98 10.53 -110.75 9.89 3.46μ-CHP + Laundry Dubbele Meter 172.82 0.88 195.74 1.00 206.75 1.06 8.55μ-CHP + Laundry Een Meter 184.44 0.94 195.79 1.00 199.95 1.02 2.79μ-CHP + Laundry APX-Januari 164.64 0.84 191.32 0.98 219.40 1.12 10.90μ-CHP + Laundry APX-0901 135.31 0.69 187.89 0.96 265.75 1.36 23.39

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E-Box Buffering (€ct/dag)

Appliances Prijspath Min f Gem. f Max f Sigma

E-HP + Heat-buffer Dubbele Meter 154.19 0.85 201.22 1.11 246.47 1.37 26.96 E-HP + Heat-buffer Een Meter 145.31 0.80 180.52 1.00 204.11 1.13 21.49 E-HP + Heat-buffer APX-Januari 129.75 0.72 223.68 1.24 322.06 1.78 30.90 PV+SpaceClng.+El-b.Dubbele Meter 4.54 0.11 47.87 1.19 92.14 2.29 22.98 PV+SpaceClng.+El-b.Een Meter 2.39 0.06 40.16 1.00 72.96 1.82 23.99 PV+SpaceClng.+El-b.APX-augustus 39.77 0.99 63.56 1.58 114.09 2.84 17.66

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E-Box energy control module simulations

• Input:- Pricepaths (t)

- Required demand(t) and demand articulation(t)

• Cleaning and drying

• Cooling

• Building heating and air condition

• Embedding of local generators (PV, micro-CHP)

• Output- Setpoints appliances

- Switching moments

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Energy management potential

- Dependent upon appliances (ten’s of Euro’s/jaar)

• Frequency• Controllability• Embedding in life and usage style• Connection with lighting control

- Even in double-tariff strategy considerable contribution:

• Potential increases with– more tariffdifferentiation – Usage of buffers

• Possibilities voor 1-1 concerted clusters- Direct feedback to users

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C-Box: Thermal innerclimate module http://www.ecn.nl/library/reports/2003/c03036.html

- Predictive strategy determination for control of thermal comfort within an enhanced context

• heating/cooling• ventilation• automated solar entrance

- Integrated in user life style- Baseerd upon a similar concept for utility buildings (SMART-

project)- User put in role of judge of inner climate; not as a system

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C-Box: Thermal innerclimate module

• Comfort model scope:- Physical building model (pre-emptive; predictive)

• Walls • Air transport• Usage pattern of home

- User model (individually, comfort acc. to Fanger)• lifestyle• activity pattern

- External data• Expected temperature, cloud coverage (external DG-RES)• Real-time energy prices

• Optimalisation upon utility function:- Thermal inner comfort and conservation target

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C-Box: Thermal innerclimate module; variants

1:

2:

3:

C-BoxThermo-

statInstallation

Thermo-stat Installation

Thermo-stat

C-Box Installation

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C-Box: Thermal innerclimate module; conclusions

• Variant 2:- Automatically gaining of energy reduction target (w.r.t. graaddagen)- Winter:

• 5 % savings due to more elaborated control using PMV instead of T

- Early-/afterseason:• 9 % savings by better control using knowledge about heating

characteristics and utilizing solar radiation

• Variant 3:- Summer:

• Cooling of building mass by enhanced nightventilation• Avoidance of overheating in the afternoon

- Demand ventilation- More exact, momentaneous comfort control- In case of ideal interaction with users and more actuators: an extra

5-15 procent savings

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Home automation for energy saving will not be invested in, if not combined with other applications !!

• Mobile care applications

• Tele-xxxx gateways

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NIDO-project: http://www.ineigenomgevingoudworden.nl/upload/attachment

s/1083669207.pdf• C-domotics Conventional

- Lock, Alarming, home automation living functions

• E-domotics Conventional and emphasis on E-saving- Same as C-, but now interfaced to energy consuming

appliances and using in a smart way to conserve energy

• AE-domotics Advanced E-domotics- Added intelligenence geared at energy conservation

(expected outside temperatuure/ solar incidence)

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Automation of energy functions; Where is there a potential saving??

• Heating -> better control to desired comfort-profile over 24 hours

• Demand ventilation on the basis of measured air quality

• Lighting• Sunblinds

- Maintain thermal comfort by avoiding overheating- Preheating; winter/spring/autumn- Not into account: Avoided cooling in summer

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Simulation met TRNSYS;Obtain reliable estimates

• NOVEM referentie galerijwoning- Consumption data - Physical characteristics (walls, glazings, doors)- Environment (whole reference year)

• Lifestyle inhabitants (SCP-figures time spending)- Economic- Average- Spillin

• BEK- and BAK-consumption figures; electricity an natural gas

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Potential.Cost and environmental key figures

Type Gas (m3/jr.) kWh/jr Kg CO2/jr. Euro/jr.

ZE 33 (3,6 %) 20 (0.8 %) 68 (2.4 %) 19 (2.2 %)

ZAE 35 (3.8 %) -16 (-0.6 %) 54 (1.9 %) 14 (1.6 %)

GE 44 (4,0 %) 63 (2.5 %) 109 (3.4 %) 31 (3.3 %)

GAE 67 (6,1 %) 28 (1.1 %) 132 (4.1 %) 36 (3.9 %)

VE 105 (7.0 %) 107 (4.1 %) 240 (6.1 %) 68 (5.9 %)

VAE 166 (11.0 %) 72 (2.7 %) 329 (8.3 %) 91 (7.9 %)

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Summary NIDO (percentual savings)

-2

0

2

4

6

8

10

12

ZE ZAEGE GAE

VE VAE

Pro

cen

t

Euro

CO2

Electra

Gas

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Conclusions

• No silver bullet• Has to be integrated with other in home

applications to be viable• Need for a PC-like control gateway computer• Savings closely related to life style• Potential from 0 up to 10-15 percent in the Dutch

sitation