1. Results of Smart Charging Research
Nazir Refa

2. Why to target EVs as source of flexibility?

3. Why to target EVs as source of flexibility?

4. Approach: simulation of three scenarios;
Without increase of renewables
With increase of renewables (factor 4)
With V2G
Research question: What is the impact of EV energy demand on the Dutch energy system?

5. Impact of scaling number of EV’s

6. Impact of scaling number of EV’s
Results scenario 1:
Consumption = 23%
Peak load = 43%
Nr. BEVs
Total consumption
Peak load
[TWh]
[MW]
Reference scenario
87.89
16286
Scaled BEV demand
107.78
23253

7. Impact of scaling renewables
Installed capacity RES
Total curtailment
Average genereation cost
[MW]
[TWh]
[€/MWh]
Reference scenario
4786
.161
27.2
Scaled BEV + increased RES
19144
2.89
22.4
Results scenario 2:
Curtailment = +2,7 TWh
Generation cost = -5 €/MWh

8. Impact of renewables & storage
Installed capacity RES
Storage capacity
Total curtailment
Average
genereation cost
[MW]
[TWh]
[€/MWh]
Reference scenario
4786
.161
27.2
Scaled BEV + increased RES + storage
19144
6984
0.989
20.5
Results scenario 3:
Curtailment = +0.8 TWh
Generation cost = -6.7 €/MWh

9. Conclusions Increasing EV energy demand results in:
Higher variability in dispatch curve.
Higher electricity production from conventional sources (e.g. natural gas) and higher start-up frequency (could reduce liftetime of production plant).
Flexibility of charging and V2G are needed to ‘better’ match supply and demand -> optimal usage of renewables & lower generation costs.

10. Approach: simulation of two scenarios;
Research questions:
Do EV owners have specific habits to charge their cars?
Are the characteristics of the charging sessions sensitive to seasonal changes or weekdays?
How is flexibility (in terms of amount, time and duration of the shifted energy) exploited? Which aspect of flexibility (time and duration of availability or amount of deferrable energy) is more useful at various times of the day?
Approach: simulation of two scenarios;
Load flattening
Load balancing

11. Behavioural clusters of sessions
Charge near work:
9.3%
Park to charge:
62.9%
Charge near home:
27.8%

12. EV flexibility Energy:
Amount of deferrable energy (i.e., the amount of energy that can be delayed without jeopardizing customer convenience or quality of the task to be fulfilled).
Time:
Time of availability (i.e., the time at which a customer offers the flexibility for exploitation).
Availability:
Deadline/permissible duration to exploit the offered flexibility (i.e., the maximum allowable delay for the energy consumption).
tBAU is the time of the completion of charging in the BAU regime.

13. When, how much energy, and for how long do we need to shift?
Energy demand (kWh)

14. Average flexibility utilization per scenario and per cluster

15. Average flexibility potential per scenario and per cluster

16. Conclusions
There is room for flexibility per cluster, given the current charging behaviour of EVs.
Flexibility of ‘near work’ and ‘park to charge’ sessions can be utilized during day and sessions belonging to ‘charge near home’ cluster offer flexibility during night hours.
Further reading:

17. Questions?