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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
Electricity Generation
Smart Grid
Electricity companies shall try to influence WHEN and HOW MUCH consumers use electricity
© Fortum
Electricity Generation
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
1
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
Electricity Generation
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%
Electricity Generation
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
“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
© Sitra
Airut, Jätkäsaari
© Sitra
Airut, Jätkäsaari
© 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
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:
Use presence to control
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
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:
Use presence to control
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
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:
Use presence to control
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?