Package 'evsim' reference manual

Title:	Electric Vehicle Charging Sessions Simulation
Description:	Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations. The simulation methodology is published in Cañigueral et al. (2023, ISBN:0957-4174) <doi:10.1016/j.eswa.2023.120318>.
Authors:	Marc Cañigueral [aut, cre]
Maintainer:	Marc Cañigueral <[email protected]>
License:	GPL-3
Version:	1.6.1
Built:	2025-01-26 05:41:45 UTC
Source:	https://github.com/mcanigueral/evsim

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

Arguments

`sessions`	tibble, sessions data set in standard format marked by `{evprof}` package (see this article)
`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)


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
)
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 (see this article)
`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))


# 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))

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)
expand_sessions(sessions, resolution)

Arguments

`sessions`	tibble, sessions data set in standard format marked by `evprof` package (see this article)
`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
)

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

Arguments

`sessions`	tibble, sessions data set in standard format marked by `{evprof}` package (see this article)t
`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")


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

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
)
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"
)


# 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"
)

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
)
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 (see this article)
`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)")

# 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)")

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)")

# 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)")

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)
get_evmodel_parameters(evmodel)

Arguments

evmodel

object of class evmodel

Value

list

Examples

get_evmodel_parameters(evsim::california_ev_model)

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)
get_evmodel_summary(evmodel)

Arguments

evmodel

object of class evmodel

Value

list

Examples

get_evmodel_summary(evsim::california_ev_model)

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
)
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 (see this article)
`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")

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)
get_user_profiles_distribution(evmodel)

Arguments

evmodel

object of class evmodel

Value

tibble

Examples

get_user_profiles_distribution(evsim::california_ev_model)

get_user_profiles_distribution(evsim::california_ev_model)

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
)
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 (see this article)
`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
)

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,
  ...
)
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()

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()

Read EV model

Description

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

Usage

read_ev_model(file)
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"))

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"))

Save the EV model

Description

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

Usage

save_ev_model(evmodel, file)
save_ev_model(evmodel, file)

Arguments

`evmodel`	object of class `evmodel` (see this link for more information)
`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"))

ev_model <- california_ev_model # Model of example

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

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

Arguments

`evmodel`	object of class `evmodel` built with `{evprof}` (see this link for more information)
`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
)

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
)

Package 'evsim'

Help Index

Adapt charging features

Description

Usage

Arguments

Details

Value

Examples

Assign a charging station to EV charging sessions

Description

Usage

Arguments

Value

Examples

Expand sessions along time slots

Description

Usage

Arguments

Details

Value

Examples

Charging rates distribution

Description

Usage

Arguments

Value

Examples

Create the custom EV model

Description

Usage

Arguments

Value

Examples

Time-series EV demand

Description

Usage

Arguments

Details

Value

Examples

Get evmodel parameters in a list

Description

Usage

Arguments

Value

Examples

Get evmodel parameters in a list of summary tables

Description

Usage

Arguments

Value

Examples

Time-series EV occupancy

Description

Usage

Arguments

Details

Value

Examples

User profiles distribution

Description

Usage

Arguments

Value

Examples

Plot the occupancy duration curve

Description

Usage

Arguments

Value

Examples

Interactive plot for time-series tibbles

Description

Usage

Arguments

Value

Examples

Read EV model

Description

Get `evmodel` parameters in a list

Get `evmodel` parameters in a list of summary tables