IEEE Power & Energy Magazine - Grid Edge 2023 - 75

can be reduced at any given time, it
is crucial to understand how many
EVs are charging simultaneously
so that they can be disconnected.
Given that the EV models based
on the MEA data include the
start-charging time and duration
of each charging event, it is possible
to know when power (3.6 kW)
is withdrawn by each EV. Consequently,
these models can also be
used to determine the amount of
power (and corresponding number
of EVs) that could, in theory,
be disconnected.
The number of EVs simulta100
90
80
70
60
50
40
30
20
10
40%
60% 80% 100%
CIL
figure 14. The probability of CILs for control actions every minute.
neously charging will depend on
multiple factors, including the time of day (morning/afternoon/evening),
type of day (weekday or weekend), weather
conditions, and so on. For illustration purposes, Figure 15
presents-for weekdays only-the results of 1,000 EVavailability
curves resulting from a Monte Carlo analysis
using 1,000 EV profiles (each with the same probability)
and considers the MEA process in terms of the number of
EVs charging on the same day (Figure 7). This availability is
given for each minute and represents the percentage of EVs
that are being simultaneously charged.
As shown, this availability varies significantly throughout
the day, from negligible during early morning hours (roughly
1% at 5 a.m.) to significant at night (30% at 8:30 p.m.); on
average (as shown by the thick black line), 20% of the EVs
would be available during peak time, and approximately 3%
would be available early in the morning, resulting in a daily
average of 9.83%. In the United Kingdom, this value translated
to a population of 10 million EVs (expected by 2030)
that could result in an average availability of approximately
3.54 GW. This means that, in 12 years, the system operator
might be able to use these services when trying to maintain
balance in the electricity system. It can be concluded that the
deployment of these services can provide a significant range
of benefits to the electricity system, particularly at night
when the United Kingdom might need it the most.
The provision of grid services from EVs appears to be
a very attractive alternative for our electricity systems. To
truly assess this potential, it is critical to capture the interactions
among EV management schemes required to mitigate
problems at the distribution level (such as the one proposed
in the MEA project) and the availability of EVs to
provide the services. The availability shown in Figure 15
would significantly change if the EV management solution
deployed in the MEA project had been considered when
creating the models. We must quantify the cost-effectiveness
of these services, which are likely to require investments
in communications and infrastructure. Finally,
the effects on customer satisfaction (delays and battery
november/december 2018
degradation) must be measured against the potential benefits
that they may receive.
The widespread adoption of EVs and their evolving technologies
also allows for envisioning a future in which EVs
can feed electricity back into the grid, known as vehicleto-grid
(V2G) integration. V2G refers to the partial use of
the energy in the batteries while EVs are plugged into the
grid. V2G generation could also be used to help balance
demand and generation in the regional or national system
or even locally (at home or within microgrids), potentially
reducing costs and increasing reliability. From the perspective
of the provision of services, however, an estimation of
V2G availability is also necessary. Although the charging
patterns of these more advanced EVs will depend on many
other (future) factors, data similar to those used in the MEA
project will make it possible to estimate EV availability. In
particular, this quantification needs to capture the SOC of
EVs to determine the amount of energy that could, in theory,
be discharged from the EV while keeping the autonomy of
the vehicle acceptable for the user and reducing the degradation
of its battery.
35
30
25
20
15
10
5
12
(a.m.)
24 68 10 24 68 10
Time of Day
12
(p.m.)
12
(a.m.)
figure 15. The aggregated EV availability for the provision
of reserves.
ieee power & energy magazine
75
Probability (%)
Aggregated EV Availability (%)
0 (0%)
1 (1-25%)
2 (26-50%)
3 (51-75%)
4 (76-100%)
5 (101-125%)
6 (126-150%)
7 (151-175%)
8 (176-200%)
9 (>200%)
IEEE Power & Energy Magazine - Grid Edge 2023

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