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Simulation of EV sessions

Source: R/simulation.R
simulate_sessions.Rd

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} (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

Value

tibble

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.

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
)
#> # A tibble: 55 × 11
#>    Session Timecycle Profile  ConnectionStartDateTime ConnectionEndDateTime
#>    <chr>   <chr>     <chr>    <dttm>                  <dttm>               
#>  1 S1      Workday   Visit    2024-09-24 06:30:00     2024-09-24 10:36:00  
#>  2 S2      Workday   Visit    2024-09-24 06:30:00     2024-09-24 10:36:00  
#>  3 S3      Workday   Worktime 2024-09-24 07:15:00     2024-09-24 16:48:00  
#>  4 S4      Workday   Worktime 2024-09-24 07:30:00     2024-09-24 16:05:00  
#>  5 S5      Workday   Worktime 2024-09-24 07:30:00     2024-09-24 16:05:00  
#>  6 S6      Workday   Visit    2024-09-24 07:45:00     2024-09-24 13:29:00  
#>  7 S7      Workday   Visit    2024-09-24 07:45:00     2024-09-24 09:23:00  
#>  8 S8      Workday   Worktime 2024-09-24 08:15:00     2024-09-24 17:11:00  
#>  9 S9      Workday   Worktime 2024-09-24 08:30:00     2024-09-24 15:00:00  
#> 10 S10     Workday   Worktime 2024-09-24 08:30:00     2024-09-24 15:00:00  
#> # ℹ 45 more rows
#> # ℹ 6 more variables: ChargingStartDateTime <dttm>, ChargingEndDateTime <dttm>,
#> #   Power <dbl>, Energy <dbl>, ConnectionHours <dbl>, ChargingHours <dbl>