This package provides the tools to simulate new EV charging session,
on the basis of the Gaussian Mixture Models (GMM) of
different EV user profiles found and modeled with {evprof}.
The EV model obtained with evprof::get_ev_model
function is an object of class evmodel, which contains
multiple time-cycle models (e.g. Weekdays and Weekends). At the same
time, each time-cycle model consists in a combination of multiple EV
user profiles and each user profile consists in multiple GMM. For more
information about the EV models read this
article from website of evprof package.
Required data
The only function of evsim package necessary to
simulate new EV sessions is function simulate_sessions,
which returns a tibble of EV charging sessions. The required input
arguments to run this function are:
- The EV model (object of class
evmodel) - Number of daily sessions for each time-cycle model
- User profiles distribution (ratio of every user profile) and specific charging power (if required)
- Charging power distribution (ratio of every different charging rate)
- Dates to simulate
EV model
Package evsim provides an EV model of example, being the model created from California ACN data in the California article from evprof documentation. This model can be accessed loading the evsim package or with the following function:
ev_model <- evsim::california_ev_modelLet’s take a look to the information that the print
function shows for an evmodel object:
print(ev_model)## EV sessions model of class "evmodel", created on 2024-01-29
## Timezone of the model: America/Los_Angeles
## The Gaussian Mixture Models of EV user profiles are built in:
## - Connection Models: logarithmic scale
## - Energy Models: logarithmic scale
##
## Model composed by 2 time-cycles:
## 1. Workday:
## Months = 1-12, Week days = 1-5
## User profiles = Visit, Worktime
## 2. Weekend:
## Months = 1-12, Week days = 6-7
## User profiles = VisitWe have 2 different time-cycles in this case (Workdays and Weekends), and each one has its corresponding user profiles. At the same time, each user profile of each time-cycle is modeled by two different models:
- Connection Models: combination of multiple Gaussian distributions of two variables (Connection Start Time and Connection Duration)
- Energy Models: combination of multiple Gaussian distributions of one variable (Energy)
Moreover, each User profile has a corresponding ratio over the total number of daily sessions. In this case, for the Workday time-cycle the half of charging sessions are Vist users and the other half Worktime users, while for the Weekend time-cycle all sessions are Visit users.
workday_models <- ev_model$models$user_profiles[[1]]
workday_models## # A tibble: 2 × 4
## profile ratio connection_models energy_models
## <chr> <dbl> <list> <list>
## 1 Visit 0.460 <tibble [3 × 3]> <tibble [1 × 3]>
## 2 Worktime 0.540 <tibble [3 × 3]> <tibble [1 × 3]>
weekend_models <- ev_model$models$user_profiles[[2]]
weekend_models## # A tibble: 1 × 4
## profile ratio connection_models energy_models
## <chr> <int> <list> <list>
## 1 Visit 1 <tibble [2 × 3]> <tibble [1 × 3]>Number of sessions per day
To set the number of sessions per day to function
simulate_sessions() we need to pass a tibble with variables
time_cycle (names corresponding to
evmodel$models$time_cycle) and n_sessions
(number of daily sessions per day for each time-cycle model). For
example:
sessions_day <- tibble(
time_cycle = ev_model$models$time_cycle,
n_sessions = c(150, 50)
)
sessions_day## # A tibble: 2 × 2
## time_cycle n_sessions
## <chr> <dbl>
## 1 Workday 150
## 2 Weekend 50User profiles distribution
In the “EV model” section we have seen that inside the
ev_model object there is already a default distribution of
user profiles, each one with the corresponding ratio. These ratio values
were obtained from the clustering process done by evprof
package and represent the presence of every user profile over the total
number of sessions clustered.
However, we may want to simulate different scenarios or use cases
where these ratios differ from the current ones. For example, to
simulate the EV demand in an industrial area it could be interesting to
prioritize the presence of Worktime sessions over Visit sessions. This
can be done by setting a new user profiles’ distribution tibble, which
must contain the variables time_cycle, profile
and ratio.
The default values for these variables, included within the model,
can be obtained with the function
get_user_profiles_distribution. It is not mandatory that
all time-cycles and user profiles appear in the
user_profiles tibble. The rule is simple: only time cycles
and user profiles appearing in the user_profiles tibble
will be simulated.
Moreover, we can specify the charging power of a
concrete user profile. This is useful in some scenarios where there is a
type of vehicle (e.g. truck) that charges at different rate than the
rest of vehicles. This can be done with the column power in
the user_profiles tibble, setting the power values in kW.
All user profiles without a specific power value will be
simulated with a random charging power obtained according to the
charging power distribution (see next section).
For example, let’s say we want to simulate a Workday
distribution with 80% of sessions being Worktime and 20% of sessions
being Visit, and considering that all Worktime sessions charge with
three-phase AC vehicles with 16A connection (11 kW). Then our
user_profiles tibble should be the following one:
user_profiles <- tibble(
time_cycle = c('Workday', 'Workday', 'Weekend'),
profile = c('Visit', 'Worktime', 'Visit'),
ratio = c(0.2, 0.8, 1),
power = c(NA, 11, NA)
)
user_profiles## # A tibble: 3 × 4
## time_cycle profile ratio power
## <chr> <chr> <dbl> <dbl>
## 1 Workday Visit 0.2 NA
## 2 Workday Worktime 0.8 11
## 3 Weekend Visit 1 NACharging powers distribution
The charging power is not a variable modeled by Gaussian Mixture Models (GMM) like the connection variables or the energy. However, the energy GMM can be built by charging power separately (see User profiles modelling on evprof website), since in general the energy charged depends on the charging power (the higher the charging power the higher the energy demand).
The purpose of letting the charging power out of the GMM is that it
is a variable that depends on the EV model and the charging point, so it
may change between scenarios. For example, we may want to simulate a
scenario where all vehicles have a three-phase connection (11 kW)
instead of the current scenario with still a lot of vehicles charging
with a single phase (3.7 kW). Therefore, it was convenient to set an
option to simulate a specific distribution of charging powers for the
simulated EV fleet. This is done with a charging_powers
tibble with variables power and ratio. In the
public charging infrastructure the most common and standard power rates
are:
- 3.7 kW AC (single-phase 16A)
- 7.3 kW AC (two-phase 16A)
- 11 kW AC (three-phase 16A)
If the energy GMM were built according to the charging power, it is
recommended that the power values in the
charging_powers tibble match the original charging power
values. These can be found inside the models. For the example
ev_model at issue, we see that the Workday energy models of
the Visit user profile are not built by charging power
since the charging_rate column shows an
Unknown value:
workday_models$energy_models[[1]] # Visit profile energy models## # A tibble: 1 × 3
## charging_rate ratio energy_models
## <chr> <int> <list>
## 1 Unknown 1 <tibble [6 × 3]>In this case, the same energy GMM will be used for all
power values. However, if we had energy models for 3.7 kW
and 11 kW separately, and we want to simulate 22 kW sessions, the models
used for the 22 kW sessions will be the 11 kW models since they are the
best approximate solution.
To follow the example, we can set the following charging power distribution:
## # A tibble: 3 × 2
## power ratio
## <dbl> <dbl>
## 1 3.7 0.2
## 2 7.3 0.4
## 3 11 0.4This complements the variable power from the
user_profiles tibble (see previous section). This
charging_powers tibble will be used to assign a charging
power to all user profiles’ sessions without a power
specified in user_profiles.
Datetime values
The function simulate_sessions() automatically selects
the time-cycle that fits to a specific date. This is why we have to pass
a vector of the dates that we want to simulate. For example:
dates_sim <- seq.Date(from = as.Date('2019-09-10'), to = as.Date('2019-09-15'), by = '1 day')
dates_sim## [1] "2019-09-10" "2019-09-11" "2019-09-12" "2019-09-13" "2019-09-14"
## [6] "2019-09-15"Another datetime argument that the simulation function needs is the
time resolution of the datetime variables of the sessions (connection
start, connection end, charging end, …). For this example we will set a
time resolution of 15 minutes, so the argument will be
resolution = 15.
Simulation
We have now all required data and we are ready to simulate the new sessions:
sessions_estimated <- simulate_sessions(
ev_model,
sessions_day,
user_profiles,
charging_powers,
dates_sim,
resolution = 15
)
head(sessions_estimated)## # A tibble: 6 × 11
## Session Timecycle Profile ConnectionStartDateTime ConnectionEndDateTime
## <chr> <chr> <chr> <dttm> <dttm>
## 1 S1 Workday Visit 2019-09-10 05:00:00 2019-09-10 10:43:00
## 2 S2 Workday Visit 2019-09-10 05:15:00 2019-09-10 11:48:00
## 3 S3 Workday Worktime 2019-09-10 05:15:00 2019-09-10 14:35:00
## 4 S4 Workday Worktime 2019-09-10 05:15:00 2019-09-10 15:11:00
## 5 S5 Workday Worktime 2019-09-10 05:15:00 2019-09-10 15:28:00
## 6 S6 Workday Worktime 2019-09-10 05:15:00 2019-09-10 18:22:00
## # ℹ 6 more variables: ChargingStartDateTime <dttm>, ChargingEndDateTime <dttm>,
## # Power <dbl>, Energy <dbl>, ConnectionHours <dbl>, ChargingHours <dbl>We have obtained a total of 700 sessions for our 7 simulation dates
(i.e. 100 sessions/day), each session corresponding to a specific
charging profile. We can notice that the datetime variables have a
resolution of 15 minutes according to our configuration, and the energy
required corresponds to product of Power and
ChargingHours.
We can check that the number of sessions of each power rate corresponds to our configuration:
sessions_estimated %>%
group_by(Profile, Power) %>%
summarise(n = n()) %>%
mutate(pct = n/sum(n)*100)## # A tibble: 4 × 4
## # Groups: Profile [2]
## Profile Power n pct
## <chr> <dbl> <int> <dbl>
## 1 Visit 3.7 44 20
## 2 Visit 7.3 88 40
## 3 Visit 11 88 40
## 4 Worktime 11 480 100EV demand
Finally, we can calculate the estimated demand with function
get_demand(). This function accepts an optional argument,
dttm_seq, which will be the datetime sequence of the
time-series demand data frame returned by the function. We will set the
datetime sequence from our first to last date with a resolution
of 15 minutes and the timezone of the original model (using the
lubridate::with_tz() function):
dttm_seq <- seq.POSIXt(
from = as.POSIXct('2019-09-10'),
to = as.POSIXct('2019-09-16'),
by = '15 min'
) %>%
lubridate::with_tz(
ev_model$metadata$tzone
)
estimated_demand <- sessions_estimated %>%
get_demand(dttm_seq)We can also visualize the estimated demand in an HTML plot using the
function plot_ts: