Go to-U rewards to smooth out EV charging station demand | NTUA- EPU Team

Go to-U rewards to smooth out EV charging station demand | NTUA- EPU Team

Our challenge, your solutions
challenge
winner

Incentivizing drivers to charge at less busy times

We analyzed data from charging stations managed by Go-To U app and we identified demand patterns and peaks. To increase the occupancy of charging stations we need to optimize charging sessions so that they are evenly spread out across time intervals we developed an innovative reward system.

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Team: National Technical University of Athens. Decision Support Unit

Team members

Christos Stefanatos, Panagiotis Skaloumpakas

Members roles and background

Panagiotis Skaloumpakas (Data Analyst, Electrical Engineer Msc NTUA)

Christos Stefanatos ( Phd Candidate : Charging behavior of EV drivers , worked in Renewable Energy Investment industry, Mech Eng MSc NTUA  )

Both members of Decision Support Labaratoty of National Technical University of Athens

Contact details

c.stefanatos@acg.edu

Solution description

We analyzed data from charging stations managed by Go-To U app and we identified demand patterns and peaks. We identified that in public charging station demand was concetraded at specific time intervals during the day. As EV adoption grows this will lead to driver frustration as they might find that public charging stations are occupied when they arrive at the spot. To avoid such issues and assure that charging stations remain occupied across time intervals, we need to optimize charging sessions so that they are evenly spread out throughout the day.

To achieve this we incentivize drivers to charge during time intervals when charging stations have been observed to have lower occupancy. We have created a reward point system to be used by Go To-U app. We based our reward system on a python algorithm we developed during the hackathon that estimates occupancy of the charging station at intervals. User are rewarded with Go To-U points when they opt to charge during time intervals with lower occupancy. They can redeem those points to charge for free

Solution context

We analyzed data from charging stations managed by Go-To U app and we identified demand patterns and peaks. We identified that in public charging station demand was concetraded at specific time intervals during the day. As EV adoption grows this will lead to driver frustration as they might find that public charging stations are occupied when they arrive at the spot. Through analyzing the data via our python algorithm we estimated that 75% of energy consumption in public charging stations takes place in 6 hour interval ( 12:00- 18:00)

Solution target group

The algorithm and reward system can be adopted by multiple operators of charge point applications. The end user is the EV driver that can receive rewards when charging during off peak hours.

Solution impact

The end user ( driver ) is the main beneficiary earning rewards and decreasing charging cost when charging during off peak hours. Charging station owners also benefit as arrival of EVs is spread out throught the day , instead of peaking.

Solution tweet text

Charge smart , use green energy and help the grid with Go To-U rewards

Solution innovativeness

The end user ( driver ) is the main beneficiary earning rewards and decreasing charging cost when charging during off peak hours. Charging station owners also benefit as arrival of EVs is spread out throught the day , instead of peaking. Pytho predictive algorithm for station occupancy

Solution transferability

The algorithm and reward system can be adopted by multiple operators of charge point applications that connect to charging stations via OCPP.
If data from the grid is also provided the algorithm can be adapted to reward users that charge during time intervals with surplus production of green energy. 

Solution sustainability

The variable pricing system also helps the grid. Instead of having peak demand in charging station at a narrow time intervals , demand is spread out, reducing peaks in demand. Usually during peaks of demand fossil fuel production kicks in , resulting into higher CO2 emissions, If data from the grid is also provided the algorithm can be adapted to reward users that charge during time intervals with surplus production of green energy. Then it would reward users that charge at times intervals when there surplus production from Wind Farms or solar panel farms , reducing their charging cost while they help prevention of curtailment of Green Energy. 

Solution team work

Panagiotis Skaloumpakas (Data Analyst, Electrical Engineer Msc NTUA)

Christos Stefanatos ( Phd Candidate : Charging behavior of EV drivers , worked in Renewable Energy Investment industry, Mech Eng MSc NTUA  )

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DigiEduHack 2021 partners & supporters

DigiEduHack is an EIT initiative under the European Commission's Digital Education Action Plan, led by EIT Climate-KIC and coordinated by Aalto University. In 2021, the main stage event is hosted by the Slovenian Presidency of the Council of the European Union in cooperation with the International Research Center on Artificial Intelligence (IRCAI) under the auspices of UNESCO.

EIT Climate-Kic

Aalto University

European commission

Slovenian Ministry of Education, Science and Sport

International Research Center on Artificial Intelligence

EIT Community: Human Capital