1. PERSONAL ENERGY ADMINISTRATION KIOSK APPLICATION
Mobilizing residential electricity consumer demand response using an ICT-ecosystem with dynamic electricity price discounts
Valeriya Azarova
Jed J. Cohen
Andrea Kollmann
Johannes Reichl
1 June 2019

2. FACTS 10 PARTNERS 7 COUNTRIES 3 years DURATION 1,9 million € BUDGET
smart metering
Energy efficiency
7 COUNTRIES
ICT
3 years DURATION
Behavioral change
250
500
1500
~2500 USERS
social networks
1,9 million € BUDGET
Serious gaming

3. MOTIVATION
80% of EU consumers shall be equipped with smart metering systems by 2020
An easy-to-use mobile application to manage electricity demand without intensive investments in hardware
About 50% of the 230 mil. dwellings in the EU are apartments, with no
property rights to install PV on the roof
possibility to benefit from low-priced spot market prices

4. OBJECTIVES RESEARCH BEHAVIOURAL CHANGE
EMPOWER THE END-USER
CREATE COMPETITIVE ADVANTAGE FOR THE PROVIDER
Gain insights on engagement, serious gaming, load shifts and sensitivity to prices of users in an energy efficiency context
REMOVE BARRIERS FOR MARKET UPTAKE
First market solution for forwarding the benefits of clean & cheaper renewable energy to consumers
Develop individualized savings opportunities and enable energy consumption control
without additional hardware
Realizing prompt roll-out, ready-to-sign contracts, cost-benefit analyses, full data protection

5. POTENTIAL EFFECT
Greater awareness of energy topics, prices, and own-consumption
Changes in time of electricity use
Switching to a ‘green’ power plan
Household improvements in behavioral energy efficiency
Reducing overall energy demand (2-7% reductions possible1)
Decreasing system peak-power – this decreases network costs!
Provides flexibility on the grid
Increase demand at times of high RES production (demand dispatch) – allows for greater integration of renewable sources
1Delmas, M. A., M. Fischlein, and O. I. Asensio (2013). Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to Energy Policy 61,

6. AUSTRIAN FIELDTEST
1.590 recruited users
1.060 with access to PEAKapp

7. FIRST INSIGHTS

8. Some statistics about PEAKapp users and their households*:
Age:
20% of users are 35 to 45 years old
53% of PEAKapp users are from 45 to 65 years old
17% of users are older than 65 years
Gender:
83% male
17% female
Education:
56% professional training or Higher Technical school
18% University degree
Income:
39% from 1400€ t0 2600€
26% from 2600€ to 3400€
27% more than 3400€
*based on results of the after survey completed by 250 PEAKapp users

9. EXPERIMENTAL DESIGN
App users in the “Discount” group were sent messages throughout the field test period offering them a discount on all electricity consumption during specific periods.
Price discounts at varying levels from 10% - to 50%
Economic (European market price situation) or environmental (high share of production of renewable energy from wind or sun) reason
On average two times per week
From June 2017 to October 2018
Duration 1 to 3 hours
Push messages sent day ahead or one hour ahead

10. Randomized control trial setting
DATA
Randomized control trial setting
Panel data
From June 2017 to October 2018
1589 households
Smart meter data for all the three groups in 15 minutes intervals
Socio-demographic data for all the three groups (including energy relevant appliances)
Google Analytics data on application usage from the two treatment groups

11. ESTIMATION STRATEGY „Fixed effects panel data regression”
ln(priceit) gives the cost in Euros per kWh of electricity for each 15 minute interval
λit daily and hourly fixed effects
µit household-hour fixed effects
Errors are clustered at the household level

12. REPEATED DISCOUNTS RESULTS
Discounts sent over the app increase consumption!
Most effective discounts are those that happen at 14:00 or 19:00 and give a renewable energy reason for the discount
Variable
Estimate
Std. Dev.
electricity price (EUR/kWh)
0.161*
(0.0915)
0.122
(0.0971)
discount indicator **
( )
0.0115**
( )
treatment effect
0.966%
1.157%
discount (%)
***
( )
***
( )
HH by hour FE
yes
Day of sample FE no
Hour of sample FE N
adj. R-sq
0.533
0.536
The “intention to treat effect” of the average discount is % increase in consumption

13. After PEAKapp: CHANGE
Confirmed possibility to increase the load during the times of higher renewable production or cheaper supply costs through discount messages
On average the electricity consumption increased by 1.15%
Environmental reasons for discounts showed higher increase in electricity consumption than economic
Proven potential to mobilize demand response during times of high renewable energy production, and thereby decrease the storage and transportation requirements on the grid
Improve ability of the electricity grid to integrate renewable sources of electricity, and decrease GHG emissions from the electricity sector by shifting consumption to times when more renewable electricity

14. CONTACT: peakapp@energieinstitut-linz.at WEBSITE: Peakapp.eu
Thank you
CONTACT: WEBSITE: Peakapp.eu