Functions simulate_sessions(), get_demand()
or get_occupancy() requires a parameter called
resolution, which defines the minutes
between a time-slot and the following one.
For example, if resolution = 15, the charging sessions
simulated during 4 PM of a specific day will start at
16:00, 16:15, 16:30 or
16:45, but not during any time between these time-slots.
However, the connection duration of every session can have any value, so
the session can finish at 16:22 for instance. The same
concept is applied to the charging times.
Therefore, the function get_demand gives the
average charging power during a certain time-slot. If
we use a resolution = 15, a demand of 55kW for time-slot
16:30 means that between 16:30 and
16:45 it has been consumed the energy corresponding to an
average power of 55 kW (so 55·15/60=13.75 kWh). This
can modify the power profile of sessions that stop charging at a time
between time-slots. For example, considering a
resolution = 15 and a session charging at 10kW from
16:30 to 16:55, the demand of time-slot
16:30 will be 10kW but the demand of time-slot
16:45 will decrease to 6.67 kW (charging only 3/4 parts of
the time-slot). The “real” power profile of the session is a constant
power step of 10kW, but the demand profile obtained with a
resolution = 15 does not. To obtain a more accurate power
profile of the session, the resolution parameter should be
set to higher resolutions (until a maximum of 1 minute).
Finally, note that the resolution parameter of
time-series functions (i.e. get_demand() and
get_n_connections()) does not have to correspond
necessarily to the resolution of simulated sessions, even
though it would not have much sense to obtain the time-series demand in
a lower resolution (longer time intervals) than sessions since we loose
accuracy on the power profile.
Let’s see and example simulating 10 charging sessions with a
resolution of 30 minutes:
| ConnectionStartDateTime | ConnectionEndDateTime | ChargingStartDateTime | ChargingEndDateTime |
|---|---|---|---|
| 31/01/2023 06:00 | 31/01/2023 15:01 | 31/01/2023 06:00 | 31/01/2023 08:22 |
| 31/01/2023 06:00 | 31/01/2023 15:39 | 31/01/2023 06:00 | 31/01/2023 09:08 |
| 31/01/2023 06:00 | 31/01/2023 15:39 | 31/01/2023 06:00 | 31/01/2023 09:42 |
| 31/01/2023 06:30 | 31/01/2023 16:58 | 31/01/2023 06:30 | 31/01/2023 08:37 |
| 31/01/2023 06:30 | 31/01/2023 16:58 | 31/01/2023 06:30 | 31/01/2023 07:12 |
| 31/01/2023 06:30 | 31/01/2023 16:58 | 31/01/2023 06:30 | 31/01/2023 06:46 |
| 31/01/2023 07:30 | 31/01/2023 17:07 | 31/01/2023 07:30 | 31/01/2023 08:15 |
| 31/01/2023 07:30 | 31/01/2023 17:07 | 31/01/2023 07:30 | 31/01/2023 08:46 |
| 31/01/2023 09:00 | 31/01/2023 16:43 | 31/01/2023 09:00 | 31/01/2023 11:15 |
| 31/01/2023 11:00 | 31/01/2023 14:58 | 31/01/2023 11:00 | 31/01/2023 12:07 |
If we calculate the aggregated demand of the above simulated sessions
with a resolution of 5, 15 and
30, the resulting power profile loses accuracy when we
decrease the time resolution (so higher time intervals):
demand_5 <- sessions %>%
get_demand(resolution = 5)
demand_15 <- sessions %>%
get_demand(resolution = 15)
demand_30 <- sessions %>%
get_demand(resolution = 30)Then we can create a common object to compare the three vectors of EV
demand, making use of dplyr::left_join to join by
datetime and tidyr::fill to fill the gaps with
the previous exisiting power:
demand_comparison <- demand_5 %>%
rename(`5-minute resolution` = Users) %>%
left_join(
rename(demand_15, `15-minute resolution` = Users)
) %>%
left_join(
rename(demand_30, `30-minute resolution` = Users)
) %>%
tidyr::fill(-datetime, .direction = 'down')
demand_comparison %>%
plot_ts(stepPlot = T, strokeWidth = 2, ylab = "EV demand (kW)")The power profile with a resolution of 5 minutes represents in a higher accuracy when sessions start and finish. If we zoom-in, we can see clearly that the average power profile really depends on the time resolution:
demand_comparison %>%
filter(
datetime >= dmy_h("31/01/2023 16", tz = ev_model$metadata$tzone),
datetime < dmy_h("31/01/2023 19", tz = ev_model$metadata$tzone)
) %>%
plot_ts(stepPlot = T, strokeWidth = 2, ylab = "EV demand (kW)")However, the total area of the three lines (energy consumed) corresponds to the same value since the power values are the average power of every time-slot whatever the resolution is:
sum(demand_5$Users*5/60) # in kWh## [1] 114.79
sum(demand_15$Users*15/60) # in kWh## [1] 114.79
sum(demand_30$Users*30/60) # in kWh## [1] 114.79