Title: Electric Vehicle Charging Sessions Simulation
Version: 1.6.1
Maintainer: Marc Cañigueral <marccanyigueral@gmail.com>
Description: Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Imports: MASS, dplyr, lubridate, purrr, rlang, tidyr, jsonlite, dygraphs, ggplot2
Suggests: spelling, testthat (≥ 3.0.0)
Depends: R (≥ 3.5)
URL: https://github.com/resourcefully-dev/evsim/, https://resourcefully-dev.github.io/evsim/
BugReports: https://github.com/resourcefully-dev/evsim/issues
Language: en-US
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-05-06 12:20:11 UTC; marc
Author: Marc Cañigueral ORCID iD [aut, cre, cph]
Repository: CRAN
Date/Publication: 2025-05-07 12:30:12 UTC

Adapt charging features

Description

Calculate connection and charging times according to energy, power and time resolution

Usage

adapt_charging_features(
  sessions,
  time_resolution = 15,
  power_resolution = 0.01
)

Arguments

sessions

tibble, sessions data set in standard format marked by {evprof} package

time_resolution

integer, time resolution (in minutes) of the sessions' datetime variables

power_resolution

numeric, power resolution (in kW) of the sessions' power

Details

All sessions' Power must be higher than 0, to avoid NaN values from dividing by zero. The ConnectionStartDateTime is first aligned to the desired time resolution, and the ConnectionEndDateTime is calculated according to the ConnectionHours. The ChargingHours is recalculated with the values of Energy and Power, limited by ConnectionHours. Finally, the charging times are also calculated.

Value

tibble

Examples

suppressMessages(library(dplyr))

sessions <- head(evsim::california_ev_sessions, 10)

sessions %>%
  select(ConnectionStartDateTime, ConnectionEndDateTime, Power)

adapt_charging_features(
  sessions,
  time_resolution = 60,
  power_resolution = 0.01
) %>%
  select(ConnectionStartDateTime, ConnectionEndDateTime, Power)

adapt_charging_features(
  sessions,
  time_resolution = 15,
  power_resolution = 1
) %>%
  select(ConnectionStartDateTime, ConnectionEndDateTime, Power)

Assign a charging station to EV charging sessions

Description

Variable ChargingStation and Socketwill be assigned to the sessions tibble with a name pattern being: names_prefix + "CHS" + number

Usage

add_charging_infrastructure(
  sessions,
  resolution = 15,
  min_stations = 0,
  n_sockets = 2,
  names_prefix = NULL,
  duration_th = 0
)

Arguments

sessions

tibble, sessions data set in standard format marked by {evprof} package

resolution

integer, time resolution in minutes

min_stations

integer, minimum number of charging stations to consider

n_sockets

integer, number of sockets per charging station

names_prefix

character, prefix of the charging station names (optional)

duration_th

integer between 0 and 100, minimum share of time (in percentage) of the "occupancy duration curve" (see function plot_occupancy_duration_curve). This is used to avoid sizing a charging infrastructure to host for example 100 vehicles when only 5% of time there are more than 80 vehicles connected. Then, setting duration_th = 5 will ensure that we don't over-size the charging infrastructure for the 100 vehicles. It is recommended to find this value through multiple iterations.

Value

tibble

Examples

# Assign a `ChargingStation` to every session according to the occupancy
sessions_infrastructure <- add_charging_infrastructure(
  sessions = head(evsim::california_ev_sessions, 50),
  resolution = 60
)
print(unique(sessions_infrastructure$ChargingStation))

# Now without considering the occupancy values that only represent
# a 10% of the time
sessions_infrastructure <- add_charging_infrastructure(
  sessions = head(evsim::california_ev_sessions, 50),
  resolution = 60, duration_th = 10
)
print(unique(sessions_infrastructure$ChargingStation))

EV model example

Description

Example of an evmodel object created with evprof for testing purposes. It has been created using an Open source data set of EV charging sessions provided by ACN.

Usage

california_ev_model

Format

california_ev_model

An evmodel object.

metadata

Information about the characteristics of the model

model

Gaussian Mixture Models for connection times and energy

EV charging sessions example

Description

Example of an charging sessions data set ready to use by evprof functions. It is the open source data set downloaded from the ACN-Data website, transformed according to the standard names defined by evprof

Usage

california_ev_sessions

Format

california_ev_sessions

A tibble object with standard variable names defined by evprof

Source

https://ev.caltech.edu/dataset

Clustered EV charging sessions example

Description

Example of a charging sessions data set that has been clustered by evprof functions

Usage

california_ev_sessions_profiles

Format

california_ev_sessions_profiles

A tibble object with standard variable names defined by evprof

Source

https://ev.caltech.edu/dataset

Convert numeric time value to a datetime period (hour-based)

Description

Convert numeric time value to a datetime period (hour-based)

Usage

convert_time_num_to_period(time_num)

Arguments

time_num

Numeric time value (hour-based)

Estimate sessions connection values

Description

Estimate sessions connection values following a Multi-variate Guassian distribution. The minimum considered value for duration is 30 minutes.

Usage

estimate_connection(n, mu, sigma, log)

Arguments

n

integer, number of sessions

mu

numeric vector, means of bivariate GMM

sigma

numeric matrix, covariance matrix of bivariate GMM

log

logical, true if models have logarithmic transformation

Value

vector of numeric values

Estimate sessions energy values following a Gaussian distribution. The minimum considered value is 1kWh based on real data analysis.

Description

Estimate sessions energy values following a Gaussian distribution. The minimum considered value is 1kWh based on real data analysis.

Usage

estimate_energy(n, mu, sigma, log)

Arguments

n

integer, number of sessions

mu

numeric, mean of Gaussian distribution

sigma

numeric, standard deviation of Gaussian distribution. If unknown, a recommended value is sd = mu/3.

log

logical, true if models have logarithmic transformation

Value

numeric vector

Estimate sessions parameters of a specific profile

Description

Estimate sessions parameters of a specific profile

Usage

estimate_sessions(
  profile_name,
  n_sessions,
  power,
  connection_models,
  energy_models,
  connection_log,
  energy_log,
  charging_powers
)

Arguments

profile_name

character, profile name

n_sessions

integer, total number of sessions per day

power

numeric, charging power of the session

connection_models

tibble, bivariate GMM of the profile

energy_models

tibble, univariate GMM of the profile

connection_log

logical, true if connection models have logarithmic transformations

energy_log

logical, true if energy models have logarithmic transformations

charging_powers

tibble with variables power and ratio The powers must be in kW and the ratios between 0 and 1.

Value

tibble

Expand a session along time slots within its connection time

Description

The session is divided in multiple time slots with the corresponding Power consumption, among other variables.

Usage

expand_session(session, resolution)

Arguments

session

tibble, sessions data set in standard format marked by evprof package

resolution

integer, time resolution (in minutes) of the time slots

Value

tibble

Expand sessions along time slots

Description

Every session in sessions is divided in multiple time slots with the corresponding Power consumption, among other variables.

Usage

expand_sessions(sessions, resolution)

Arguments

sessions

tibble, sessions data set in standard format marked by evprof package

resolution

integer, time resolution (in minutes) of the time slots

Details

The Power value is calculated for every time slot according to the original required energy. The columns PowerNominal, EnergyRequired and FlexibilityHours correspond to the values of the original session, and not to the expanded session in every time slot. The column ID shows the number of the time slot corresponding to the original session.

Value

tibble

Examples

library(dplyr)

sessions <- head(evsim::california_ev_sessions, 10)
expand_sessions(
  sessions,
  resolution = 60
)

Charging rates distribution

Description

Get charging rates distribution in percentages from a charging sessions data set

Usage

get_charging_rates_distribution(sessions, unit = "year", power_interval = NULL)

Arguments

sessions

tibble, sessions data set in standard format marked by {evprof} packaget

unit

character. Valid base units are second, minute, hour, day, week, month, bimonth, quarter, season, halfyear and year. It corresponds to unit parameter in lubridate::floor_date function.

power_interval

numeric, interval of kW between power rates. It is used to round the Power values into this interval resolution. It can also be NULL to use all the original Power values.

Value

tibble

Examples

get_charging_rates_distribution(evsim::california_ev_sessions, unit = "year")

Connection GMM

Description

Get connection Gaussian Mixture Models from parameters

Usage

get_connection_models_from_parameters(
  time_cycle_parameters,
  connection_log = FALSE
)

Arguments

time_cycle_parameters

tibble with Gaussian Mixture Models parameters. This tibble must have the following columns: profile, ratio (in %), start_mean (in hours), start_sd (in hours), duration_mean (in hours), duration_sd (in hours), energy_mean (in kWh), energy_sd (in kWh).

connection_log

logical, true if connection models have logarithmic transformations

Value

connection GMM tibble

Create the custom EV model

Description

Get the EV model object of class evmodel

Usage

get_custom_ev_model(
  names,
  months_lst = list(1:12, 1:12),
  wdays_lst = list(1:5, 6:7),
  parameters_lst,
  connection_log,
  energy_log,
  data_tz
)

Arguments

names

character vector with the given names of each time-cycle model

months_lst

list of integer vectors with the corresponding months of the year for each time-cycle model

wdays_lst

list of integer vectors with the corresponding days of the week for each time-cycle model (week start = 1)

parameters_lst

list of tibbles corresponding to the GMM parameters of every time-cycle model

connection_log

logical, true if connection models have logarithmic transformations

energy_log

logical, true if energy models have logarithmic transformations

data_tz

character, time zone of the original data (necessary to properly simulate new sessions)

Value

object of class evmodel

Examples


# For workdays time cycle
workdays_parameters <- dplyr::tibble(
  profile = c("Worktime", "Visit"),
  ratio = c(80, 20),
  start_mean = c(9, 11),
  start_sd = c(1, 4),
  duration_mean = c(8, 4),
  duration_sd = c(0.5, 2),
  energy_mean = c(15, 6),
  energy_sd = c(4, 3)
)

# For weekends time cycle
weekends_parameters <- dplyr::tibble(
  profile = "Visit",
  ratio = 100,
  start_mean = 12,
  start_sd = 4,
  duration_mean = 3,
  duration_sd = 2,
  energy_mean = 4,
  energy_sd = 4
)

parameters_lst <- list(workdays_parameters, weekends_parameters)

# Get the whole model
ev_model <- get_custom_ev_model(
  names = c("Workdays", "Weekends"),
  months_lst = list(1:12, 1:12),
  wdays_lst = list(1:5, 6:7),
  parameters_lst = parameters_lst,
  connection_log = FALSE,
  energy_log = FALSE,
  data_tz = "Europe/Amsterdam"
)

Get day sessions

Description

Get day sessions

Usage

get_day_sessions(day, ev_models, connection_log, energy_log, charging_powers)

Arguments

day

Date to simulate

ev_models

profiles models

connection_log

Logical, true if connection models have logarithmic transformations

energy_log

Logical, true if energy models have logarithmic transformations

charging_powers

tibble with variables power and ratio The powers must be in kW and the ratios between 0 and 1.

Value

tibble

Time-series EV demand

Description

Obtain time-series of EV demand from sessions data set

Usage

get_demand(
  sessions,
  dttm_seq = NULL,
  by = "Profile",
  resolution = 15,
  mc.cores = 1
)

Arguments

sessions

tibble, sessions data set in standard format marked by {evprof} package

dttm_seq

sequence of datetime values that will be the datetime variable of the returned time-series data frame.

by

character, being 'Profile' or 'Session'. When by='Profile' each column corresponds to an EV user profile.

resolution

integer, time resolution (in minutes) of the sessions datetime variables. If dttm_seq is defined this parameter is ignored.

mc.cores

integer, number of cores to use. Must be at least one, and parallelization requires at least two cores.

Details

Note that the time resolution of variables ConnectionStartDateTime and ChargingStartDateTime must coincide with resolution parameter. For example, if a charging session in sessions starts charging at 15:32 and resolution = 15, the load of this session won't be computed. To solve this, the function automatically aligns charging sessions' start time according to resolution, so following the previous example the session would start at 15:30.

Value

time-series tibble with first column of type datetime

Examples

suppressMessages(library(lubridate))
suppressMessages(library(dplyr))

# Get demand with the complete datetime sequence from the sessions
sessions <- head(evsim::california_ev_sessions, 100)
demand <- get_demand(
  sessions,
  by = "Session",
  resolution = 60
)
demand %>% plot_ts(ylab = "EV demand (kW)", legend_show = "onmouseover")

# Get demand with a custom datetime sequence and resolution of 15 minutes
sessions <- head(evsim::california_ev_sessions_profiles, 100)
dttm_seq <- seq.POSIXt(
  as_datetime(dmy(08102018)) %>% force_tz(tz(sessions$ConnectionStartDateTime)),
  as_datetime(dmy(11102018)) %>% force_tz(tz(sessions$ConnectionStartDateTime)),
  by = "15 mins"
)
demand <- get_demand(
  sessions,
  dttm_seq = dttm_seq,
  by = "Profile",
  resolution = 15
)
demand %>% plot_ts(ylab = "EV demand (kW)", legend_show = "onmouseover")

Energy GMM

Description

Get energy Gaussian Mixture Models from parameters

Usage

get_energy_models_from_parameters(time_cycle_parameters, energy_log = FALSE)

Arguments

time_cycle_parameters

tibble with Gaussian Mixture Models parameters. This tibble must have the following columns: profile, ratio (in %), start_mean (in hours), start_sd (in hours), duration_mean (in hours), duration_sd (in hours), energy_mean (in kWh), energy_sd (in kWh).

energy_log

logical, true if connection models have logarithmic transformations

Value

energy GMM tibble

Get estimated profiles

Description

Get estimated profiles

Usage

get_estimated_connections(n, profile_models, log)

Arguments

n

number of sessions

profile_models

models of the profile

log

Logical, true if models have logarithmic transformation

Value

list with sessions connection values

Estimate energy given energy models tibble

Description

Estimate energy given energy models tibble

Usage

get_estimated_energy(power_vct, energy_models, energy_log)

Arguments

power_vct

numeric vector of power values of simulated sessions

energy_models

energy models tibble

energy_log

Logical, true if models have logarithmic transformation

Value

list of numeric vectors

Get evmodel parameters in a list

Description

Every time cycle is an element of the returned list, containing a list with the user profile as elements, each one containing the ratio and the corresponding tables with the statistic parameters of connection and energy GMM.

Usage

get_evmodel_parameters(evmodel)

Arguments

evmodel

object of class evmodel

Value

list

Examples

get_evmodel_parameters(evsim::california_ev_model)

Get evmodel parameters in a list of summary tables

Description

Every time cycle is an element of the returned list, containing a table with a user profile in every row and the mean and standard deviation values of the GMM variables (connection duration, connection start time and energy). If the energy models were built by charging rate, the average mean and sd are provided without taking into account different charging rates (this information is lost in this summary).

Usage

get_evmodel_summary(evmodel)

Arguments

evmodel

object of class evmodel

Value

list

Examples

get_evmodel_summary(evsim::california_ev_model)

Time-series EV occupancy

Description

Obtain time-series of simultaneously connected EVs from sessions data set

Usage

get_occupancy(
  sessions,
  dttm_seq = NULL,
  by = "Profile",
  resolution = 15,
  mc.cores = 1
)

Arguments

sessions

tibble, sessions data set in standard format marked by {evprof} package

dttm_seq

sequence of datetime values that will be the datetime variable of the returned time-series data frame.

by

character, being 'Profile' or 'Session'. When by='Profile' each column corresponds to an EV user profile.

resolution

integer, time resolution (in minutes) of the sessions datetime variables. If dttm_seq is defined this parameter is ignored.

mc.cores

integer, number of cores to use. Must be at least one, and parallelization requires at least two cores.

Details

Note that the time resolution of variable ConnectionStartDateTime must coincide with resolution parameter. For example, if a charging session in sessions starts charging at 15:32 and resolution = 15, the load of this session won't be computed. To solve this, the function automatically aligns charging sessions' start time according to resolution, so following the previous example the session would start at 15:30.

Value

time-series tibble with first column of type datetime

Examples

library(lubridate)
library(dplyr)

# Get occupancy with the complete datetime sequence from the sessions
sessions <- head(evsim::california_ev_sessions, 100)
connections <- get_occupancy(
  sessions,
  by = "ChargingStation",
  resolution = 60
)
connections %>%
  plot_ts(ylab = "Vehicles connected", legend_show = "onmouseover")

# Get occupancy with a custom datetime sequence and resolution of 15 minutes
sessions <- head(evsim::california_ev_sessions_profiles, 100)
dttm_seq <- seq.POSIXt(
  as_datetime(dmy(08102018)) %>% force_tz(tz(sessions$ConnectionStartDateTime)),
  as_datetime(dmy(11102018)) %>% force_tz(tz(sessions$ConnectionStartDateTime)),
  by = "15 mins"
)
connections <- get_occupancy(
  sessions,
  dttm_seq = dttm_seq,
  by = "Profile"
)
connections %>%
  plot_ts(ylab = "Vehicles connected", legend_show = "onmouseover")

User profiles distribution

Description

Get the user profiles distribution from the original data set used to build the model

Usage

get_user_profiles_distribution(evmodel)

Arguments

evmodel

object of class evmodel

Value

tibble

Examples

get_user_profiles_distribution(evsim::california_ev_model)

Is the sessions data set aligned in time?

Description

Checks if sessions time variables (only connection/charging start times) are aligned with a specific time resolution.

Usage

is_aligned(sessions, resolution)

Arguments

sessions

tibble, sessions data set in standard format marked by evprof package

resolution

integer, time resolution (in minutes) of the time slots

Value

logical

Parallel processing for windows

Description

mclapply version that works on windows

Usage

mclapply.windows(X, FUN, ..., mc.cores)

Arguments

X

a vector (atomic or list) or an expressions vector. Other objects (including classed objects) will be coerced by as.list.

FUN

the function to be applied to (mclapply) each element of X.

...

For mclapply, optional named arguments to FUN.

mc.cores

integer, number of cores to be used. Could be overruled if number of items in list is lower.

Details

This is a workaround to allow parallel processing in windows since parallel package only works with linux distributions. Source: https://www.r-bloggers.com/2014/07/implementing-mclapply-on-windows-a-primer-on-embarrassingly-parallel-computation-on-multicore-systems-with-r/

Value

list

Plot the occupancy duration curve

Description

This term is based on the "load duration curve" and is useful to see the behavior of occupancy over the time in your charging installation. The steeper the curve, the shorter the duration that higher number of connections are sustained. Conversely, the flatter the curve, the longer the duration that higher number of connections are sustained. This information is crucial for various purposes, such as infrastructure planning, capacity sizing, and resource allocation.

Usage

plot_occupancy_duration_curve(
  sessions,
  dttm_seq = NULL,
  by = "Profile",
  resolution = 15,
  mc.cores = 1
)

Arguments

sessions

tibble, sessions data set in standard format marked by {evprof} package

dttm_seq

sequence of datetime values that will be the datetime variable of the returned time-series data frame.

by

character, being 'Profile' or 'Session'. When by='Profile' each column corresponds to an EV user profile.

resolution

integer, time resolution (in minutes) of the sessions datetime variables. If dttm_seq is defined this parameter is ignored.

mc.cores

integer, number of cores to use. Must be at least one, and parallelization requires at least two cores.

Value

ggplot

Examples

library(dplyr)

sessions <- head(evsim::california_ev_sessions_profiles, 100)
plot_occupancy_duration_curve(
  sessions,
  by = "Profile",
  resolution = 15
)

Interactive plot for time-series tibbles

Description

First column of the df tibble must be a datetime or date variable. The rest of columns must be numeric of the same units. This functions makes use of dygraphs package to generate an HTML Dygraphs plot.

Usage

plot_ts(
  df,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  legend_show = "auto",
  legend_width = 250,
  group = NULL,
  width = NULL,
  height = NULL,
  ...
)

Arguments

df

data.frame or tibble, first column of name datetime being of class datetime and rest of columns being numeric

title

character, title of the plot (accepts HTML code)

xlab

character, X axis label (accepts HTML code)

ylab

character, Y axis label (accepts HTML code)

legend_show

character, when to display the legend. Specify "always" to always show the legend. Specify "onmouseover" to only display it when a user mouses over the chart. Specify "follow" to have the legend show as overlay to the chart which follows the mouse. The default behavior is "auto", which results in "always" when more than one series is plotted and "onmouseover" when only a single series is plotted.

legend_width

integer, width (in pixels) of the div which shows the legend.

group

character, dygraphs group to associate this plot with. The x-axis zoom level of dygraphs plots within a group is automatically synchronized.

width

Width in pixels (optional, defaults to automatic sizing)

height

Height in pixels (optional, defaults to automatic sizing)

...

extra arguments to pass to dygraphs::dyOptions function.

Value

dygraph

Examples

suppressMessages(library(lubridate))
suppressMessages(library(dplyr))

# Get demand with the complete datetime sequence from the sessions
sessions <- head(evsim::california_ev_sessions, 100)
demand <- get_demand(
  sessions,
  by = "Session",
  resolution = 60
)
demand %>% plot_ts()

Prepare the models from the evmodel object ready for the simulation

Description

The ratios and default charging power for every user profile, and the sessions per day for every time cycle are included.

Usage

prepare_model(ev_models, sessions_day, user_profiles)

Arguments

ev_models

tibble with models from an evmodel object

sessions_day

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)

user_profiles

tibble with variables time_cycle, user_profile, ratio and optionally power. The powers must be in kW and the ratios between 0 and 1. The user profiles with a value of power will be simulated with this specific charging power. If power is NA then it is simulated according to the ratios of parameter charging_powers in function. simulate_sessions.

Value

tibble

print method for evmodel object class

Description

print method for evmodel object class

Usage

## S3 method for class 'evmodel'
print(x, ...)

Arguments

x

evmodel object

...

further arguments passed to or from other methods.

Value

nothing but prints information about the evmodel object

Examples

print(california_ev_model)

Read EV model

Description

Read an EV model JSON file and convert it to object of class evmodel

Usage

read_ev_model(file)

Arguments

file

path to the JSON file

Value

object of class evmodel

Examples

ev_model <- california_ev_model # Model of example

save_ev_model(ev_model, file = file.path(tempdir(), "evmodel.json"))

read_ev_model(file = file.path(tempdir(), "evmodel.json"))

Round a numeric value to interval

Description

Round a numeric value to interval

Usage

round_to_interval(dbl, interval)

Arguments

dbl

numeric value

interval

decimal interval (from 0 to 1)

Save the EV model

Description

Save the EV model object of class evmodel to a JSON file

Usage

save_ev_model(evmodel, file)

Arguments

evmodel

object of class evmodel

file

character string with the path or name of the file

Value

nothing but saves the evmodel object in a JSON file

Examples

ev_model <- california_ev_model # Model of example

save_ev_model(ev_model, file = file.path(tempdir(), "evmodel.json"))

Names of standard features of a sessions dataset

Description

A vector with the standard names of sessions features for this package functions.

Usage

sessions_feature_names

Format

A vector

Simulation of EV sessions

Description

Simulate EV charging sessions given the evmodel object and other contextual parameters.

Usage

simulate_sessions(
  evmodel,
  sessions_day,
  user_profiles,
  charging_powers,
  dates,
  resolution
)

Arguments

evmodel

object of class evmodel built with {evprof}

sessions_day

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)

user_profiles

tibble with variables time_cycle, profile, ratio and optionally power. It can also be NULL to use the evmodel original user profiles distribution. The powers must be in kW and the ratios between 0 and 1. The user profiles with a value of power will be simulated with this specific charging power. If power is NA then it is simulated according to the ratios of next parameter charging_powers.

charging_powers

tibble with variables power and ratio. The powers must be in kW and the ratios between 0 and 1. This is used to simulate the charging power of user profiles without a specific charging power in user_profiles parameter.

dates

date sequence that will set the time frame of the simulated sessions

resolution

integer, time resolution (in minutes) of the sessions datetime variables

Details

Some adaptations have been done to the output of the Gaussian models: the minimum simulated energy is considered to be 1 kWh, while the minimum connection duration is 30 minutes.

Value

tibble

Examples

library(dplyr)
library(lubridate)

# Get the example `evmodel`
ev_model <- evsim::california_ev_model

# Simulate EV charging sessions, considering that the Worktime sessions
# during Workdays have 11 kW, while all Visit sessions charge at 3.7kW or
# 11kW, with a distribution of 30% and 70% respectively.

simulate_sessions(
  ev_model,
  sessions_day = tibble(
    time_cycle = c("Workday", "Weekend"),
    n_sessions = c(15, 10)
  ),
  user_profiles = tibble(
    time_cycle = c("Workday", "Workday", "Weekend"),
    profile = c("Visit", "Worktime", "Visit"),
    ratio = c(0.5, 0.5, 1),
    power = c(NA, 11, NA)
  ),
  charging_powers = tibble(
    power = c(3.7, 11),
    ratio = c(0.3, 0.7)
  ),
  dates = seq.Date(today(), today()+days(4), length.out = 4),
  resolution = 15
)