Energy Efficiency & User BehaviourIEA DSM Task 24
“Energy Efficiency and Behavioral Change”
Energy Behaviour and Smart Meter
Simon MoserEnergy Institute at the Johannes Kepler University of Linz
Graz/Austria, 2014-10-13
Content
• The “smart“ meter
• Microeconomic theory
• Providing the smart meter’s information
• Field test results & biases
• Persistence of savings
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The „smart“ meter
• The “smart“ meter
• Meter readings: frequently or near real time
• Able to communicate
• Advantages when installed:
• uncomplicated provision of data
• automated use of and functions
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Microeconomic theory
• Asymmetric information � inefficient quantities
• Bill: Price and quantity (kWh) per year
• Price for the quantity (kWh)
• Quantity (kWh) for the sum of actions
• � Missing information: price for one action
• Consumption information based on frequent meter readings:
• Interval (daily / hourly) or real time consumption = hint for kWh/action
• No evidence on actual consumption
• No price connected (fix price components remain)
• Feedback theoretically leads to more efficient consumption• Adoptions in behaviour of energy/electricity consumption• Adoptions in appliance stock (which in turn is consumption behaviour) 4
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Providing the smart meter’s information
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Medium xy xzSeriousness of
informationReal Time
InformationPersistency of
impact
Postal Mail
Website
App (Tablet)
App (Smartphone)
SMS
Callcenter
In-House-Display
Meter‘s display
Ambient Equipment
Field test results• International results
• meta study from Sarah Darby (ECI Oxford)
• 2,5 % savings due to IHD in UK households (real life)
• Rather incomparable to other countries (Consumption volumes, appliances, …)
• Region Germany – Austria – Switzerland (number of studies reviewed = 10)
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Medium < 2 % 2 – 5 % > 5 %
Monthly Mail 1 2 1*
Website 1 4 1*
In-House-Display 1 4 1*
Ambient Equipment 1 - -
• Many problems with biases like selection of participants, number of participants, methodsof calculation
• * 11% by combining website, app and mail. However: no provision of n and CI
Kollmann, Moser, de Bruyn, Schwarz, Fehringer (2013): Smart Metering in the Contextof Smart Grids. Final project report, in German.
Biases of field test results
• Participants = persons interested
• When feedback is provided, they are more engaged than average people
• Thus the observed impact is higher than what the impact would be in the whole/real population
• Participants = persons interested
• As they are interested, participants had known about loads, appliance consumption etc. and had already adopted their consumption patterns and appliances even before the feedback was provided
• When feedback is provided, they are also more engaged than average people but find less opportunities.
• Thus the observed impact is lower than what the impact would be in the whole/real population
• Same starting point, different results � only little empirical evidence on biases 7
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Persistency of savings
• „People look at the In-House-Display for 3 months and then they are not interested any more“.
• Results on persistency in load shift experiments are ambiguous
• CONSTANT
• Savings due to Critical Peak Pricing: 12% in year 1, 13% in year 2 (Faruqui and Sergici, 2010)
• “Constant savings in test period” (eTelligence, Agsten et al., 2012)
• “Interest remained in year 2” (Smart-A, Kollmann et al., 2014)
• Manual reactions in response to real-time pricing remained for a long period” (Hillemacher et al., 2013)
• “Constant participation” (Karg et al., 2013)
• REDUCED
• Participation decreases to 33% after 3 months (Frey, 2013)
• Many expert interviews mention decreasing interest
• Is any more interest necessary after behaviour and appliances are adopted?8
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Thank you for your attention !
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Simon MoserEnergy Institute at the Johannes Kepler
University of Linz
[email protected]: +43 732 2468 5658
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