Smart buildings lecture for Aalto Pro

Smart Buildings: Älykkäät rakennukset tulossa (Smart buildings are coming)

Aalto Pro 11.6.2012

Smart Grid: For Energy Generators

Electricity Generation

Traditional Electricity Grid

Customers consume electricity and electricity companies generate electricity to match the demand

© Fortum


Smart Grid

Electricity companies shall try to influence WHEN and HOW MUCH consumers use electricity

© Fortum


3 Important Elements

Electricity generators will operate more efficiently

Energy consumers will have a new method of pricing

Private energy generators can sell back to the grid more easily

© Fortum

Definitions

Smart grid:

• Energy supply network that shall charge consumers at a variable energy price per hour

• Prices shall be varied with demand

• First project in Finland January 2013 (Fortum)

• Kalasatamankeskus first smart grid neighbourhood (Helsingin Energia)

Smart meters:

• Electronic energy meters that record detailed customer data

• the amount of energy consumed and when this energy is consumed

• Information can be viewed in real-time

Electricity Generation: Inefficient Load

American example of electricty generation for 1 day

© Data from NIST

0

0,2

0,4

0,6

0,8

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1,2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

No

rm

alized

ele

ctr

ic s

yste

m l

oad

Hour of day

Electricity Generation: Inefficient Load

Finnish example of electricty generation for 1 year (2011)

©Energiateollisuus

Finland: Peak electricity demand in winter


Finnish peak day energy generation: February 2011

0

5

10

15

20

25

30

35

40

Nuclear Power Hydro Power Wind CHP Condensing Nett imports

% o

f p

eak l

oad

Energy generation method

18%


Cost of generation (approximate costs, operation only)

System €/ MWh

Wind 5

Nuclear Power 15

Hydro Power 20

Condensing Coal 25

CHP 38

Nett imports 49

Conventional gas turbine 125

Energy Generation

Prices: Electricity is important

1 The Finnish electricity price for 2020 has assumed to be equal to the German electricity price for 2011. The German 2011 price has been taken from a Eurostat report that showed German energy prices for mid-size industrial companies (500–2000 MWh)

Electricity District Heating

2011 84,4 €/MWh 2011 63,9 €/MWh

2020 110,7 €/MWh1 2020 65,8 €/MWh

Change 31,2 % Change

2.9%

Smart Grid: For Energy Consumers

Energy Energy Energy

Time Time Time

Energy Reduction Load shifting Peak Shaving

• Renewable energy

• Energy reduction measures

• Smart appliances

• Task scheduling

• React to energy prices by

turning systems on or off

• Reduce internal conditions

• Advanced presence

detection

Options: Options: Options:

Smart Grid: How to reduce consumption

Smart Grid

Load shifting

Electric car charging at night

Smart appliances: dishwashing machine, clothes washing machine

Peak shaving

Winter peak: turn off night-time lighting or to dim advertisement lighting when prices are particularly high

Summer peak: less cooling, target temperature rises from 21oC to 24oC

High supply

Plenty of wind, take advantage of low energy prices:

industrial processes that require large amounts of electricity may be automatically performed

cheaper to use expensive home systems such as sauna

©Zumawire

http://static.iltalehti.fi/autot/sahkoauto_2101JID_au.jpg

“the wind is blowing in Denmark so maybe we will have a sauna”

Smart Buildings: Case Studies

Smart Buildings

VTT test apartment in Oulu

Opened 2012

Electric car

Electricity storage

5.5 kW wind power plant

20 m2 of solar cells generating 4 kW

Graphical displays to monitor the electricity consumption

© VTT

© VTT © VTT

Smart Buildings

Airut, Jätkäsaari, Helsinki

To open 2015

Solar power, geothermal heating

Dashboard:

smart appliances

showing energy consumption

comparing energy consumption with the building average

booking system for shared cars

booking system for community sauna

public transport timetables

© Sitra

© Sitra

© Sitra

Airut, Jätkäsaari

Airut, Jätkäsaari

Locking system: - Turns energy systems off - Heating reduced - Non-essential circuit off - Lighting off - Kitchen stove off - Sauna off - Ventilation off

Heating system: - Radiators with remote controlers - Pay the heating you use not per m2

© Sitra

Low2No Smart Systems Selections

Showers: - Water meter per apartment - Pay the heating you use not per m2

Information and control dashboard - Laptop / ipad / phone - Link to smart software - Feedback from meters - Control heating and ventilation - Link to community information

Additional metering: - Electrical (per circuit) - Heating (space / water)

Smart Buildings

San Francisco Public Utility Commission (SFPUC)

Opened 2011

26 000 m2

450 dashboards providing all building users with:

• energy consumption

• water consumption

• carbon footprint

© SFPUC

©Smart Buildings, LLC

Smart Buildings

NASA Ames Research Center

Opened 2012

4 750 m2

5 000 wireless sensors:

temperature

carbon dioxide levels

natural lighting

air flow

Construction costs were only 6% more than a traditional building

also includes solar panels and geothermal cooling

© io9.com

Smart Buildings

Bridesburg Metalworks, Pennsylvania

• Operate 0700 – 1500

• US electricity peak is in summer

• The are paid to turn off metal melting machines

• Melting employees move to the packaging department

• They are earning an extra $25 000 per year

© opower

Smart Buildings New Ways of Working

Commercial Buildings:

Peak shaving

• Can we find items to turn off in the middle of the day

• Office staff on holidays, sick, external meetings, sales team

• UK study shows offices on average 45% occupied 2

2: Regus, “Measuring the benefits of agility at work”, May 2011

• Turn off (where people are missing)

• Lighting

• Ventilation

• Computers

Energy

Monitoring occupancy

Access control system - measures when people are in the building

Employees log in/out of the building via:

• Electronic time clock

• Smart phone

• Personal computer

• Real time location tags

Location is defined as a set of routines:

• Routine 1: out of the building

• Routine 2: in the building

Building

IN OUT

Advanced presence detection

Use presence knowledge to control energy consuming systems

Define location as a set of routines:

• Routine 1: out of the building

• Routine 2: in the building

• Subroutine A: at workspace

• Subroutine B: in a meeting

• Subroutine C: at lunch

IN OUT

IN

Building

IN OUT OUT

Use presence to control

Use presence knowledge to control:

• Shut down an individual’s workspace if they leave the building

• Set to standby an individual’s workspace if they are in a meeting / at lunch

• Shut down a lighting / ventilation zone if all of the occupants are out of the office

Example zone control modes:

System Type

Presence Detected

Desk Lighting ON

Common Lighting ON

Equipment ON

Ventilation 100 %

Heating 21oC

Cooling 25oC







System Type

Presence Detected

No presence 15 mins

Desk Lighting ON OFF

Common Lighting ON ON

Equipment ON STAND BY

Ventilation 100 % 100 %

Heating 21oC 21oC

Cooling 25oC 25oC







System Type

Presence Detected

No presence 15 mins

No presence 1 hour

Desk Lighting ON OFF OFF

Common Lighting ON ON OFF

Equipment ON STAND BY STAND BY

Ventilation 100 % 100 % 50 %

Heating 21oC 21oC 20oC

Cooling 25oC 25oC 27oC

System Type

Presence Detected

No presence 15 mins

No presence 1 hour

No presence 2 hours

Desk Lighting ON OFF OFF OFF

Common Lighting ON ON OFF OFF

Equipment ON STAND BY STAND BY OFF

Ventilation 100 % 100 % 50 % Night time mode

Heating 21oC 21oC 20oC Night time mode

Cooling 25oC 25oC 27oC Night time mode







Concept development

Technology

Smart Grid

Advanced Controls

Advanced Presence Detection

Virtual Energy Prices

Incentive schemes

Residential building example:

• A block of similar 2 bedroom apartments

• Incentive scheme to reduce energy

• Reward given to the lowest energy consumption

Occupation density

Energy directly related to people is not considered by area metrics

Average occupation density in UK offices is 11.8m2 per workspace3

• 77% of workspaces between 8m2 & 13m2 per workspace

• Using kWh/m2, 13m2 per workspace will seem more energy efficient than 8m2 per workspace

3: Occupier Density Study Summary Report, British Council for Offices, June 2009

Source: Fooducate.com

Case study: Occupation density

Case A B C

Population density (m2/person) 8 10 12

Number of occupants 500 400 332

Energy consumption (kWh/m2) 102 99 98

Energy consumption (kWh/person) 951 1150 1368

Energy consumption (Wh/m2h) 0.087 0.105 0.126

Simulated case study: office building in Helsinki

• Area: 4650m2

• Hours of occupancy 08:00 – 17:00 (9 hours)

Similar day lengths, different occupation densities

Results

• kWh/m2: case C consumes the least

• kWh/person or Wh/m2h: case A consumes the least

(C consumes 44% more than A)

Hours of occupation

Not considered by area metrics

• Comparison of two similar healthcare buildings

• Hospital ”A” open 24 hrs / Hospital ”B” open 12 hrs

• kWh/m2 does not provide an allowance for the longer day of ”A”

• Thus ”A” has a higher energy consumption per m2 and seems less energy efficient

Case study: Hours of occupation

Case D E F

Working hours per day (h) 12 9 6

Hours of occupancy 08 - 20 08 - 17 09 - 15

Energy consumption (kWh/m2) 115 99 84

Energy consumption (kWh/person) 1330 1150 981

Energy consumption (Wh/m2h) 0.092 0.105 0.134

• Simulated case study: office building in Helsinki

• Area: 4650m2

• Population density 10m2 / person

Similar occupation densities, different day lengths

Results

• kWh/m2: case F consumes the least

• kWh/person or Wh/m2h: case D consumes the least

(F consumes 45% more than D)

Concept development

Behaviour Change

Motivation / Incentives

Measure per Person

Technology

Smart Grid

Advanced Controls

Measure Wh/m2h

Advanced Presence Detection

Virtual Energy Prices

Summary

• Smart grid will bring more efficiency in energy generation

• Cheaper prices on average, but a different way of charging

• People who prepare for smart grid will save money – people who dont prepare will pay more

• Can we reduce our peak load AND measure energy efficiency more accurately?

Ken Dooley

Sustainability Group Manager

Energy and Environment

[email protected]

Smart buildings lecture for Aalto Pro

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