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Smart charging algorithm

Source: R/smart-charging.R
smart_charging.Rd

This function provides a framework to simulate different smart charging methods (i.e. postpone, interrupt and curtail) to reach multiple goals (e.g. grid congestion, net power minimization, cost minimization). See implementation examples and the formulation of the optimization problems in the documentation website.

Usage

smart_charging(
  sessions,
  opt_data,
  opt_objective,
  method,
  window_days,
  window_start_hour,
  responsive = NULL,
  power_th = 0,
  charging_power_min = 0,
  energy_min = 1,
  include_log = FALSE,
  show_progress = FALSE,
  lambda = 0,
  mc.cores = 1
)

Arguments

sessions

tibble, sessions data set containig the following variables: "Session", "Timecycle", "Profile", "ConnectionStartDateTime", "ConnectionHours", "Power" and "Energy".

opt_data

tibble, optimization contextual data. The first column must be named datetime (mandatory) containing the date time sequence where the smart charging algorithm is applied, so only sessions starting within the time sequence of column datetime will be optimized. The other columns can be:

  • static: static power demand (in kW) from other sectors like buildings, offices, etc.

  • grid_capacity: maximum imported power from the grid (in kW), for example the contracted power with the energy company.

  • production: local power generation (in kW). This is used when opt_objective = "grid".

  • price_imported: price for imported energy (Euro/kWh). This is used when opt_objective = "cost".

  • price_exported: price for exported energy (Euro/kWh). This is used when opt_objective = "cost".

  • price_turn_down: price for turn-down energy use (Euro/kWh). This is used when opt_objective = "cost".

  • price_turn_up: price for turn-up energy use (Euro/kWh). This is used when opt_objective = "cost".

If columns of opt_data are user profiles names, these are used as setpoints and no optimization is performed for the corresponding user profiles.

opt_objective

character, optimization objective being "none", "grid", "cost" or a value between 0 (cost) and 1 (grid). See details section for more information about the different objectives.

method

character, scheduling method being "none", "postpone", "curtail" or "interrupt". If none, the scheduling part is skipped and the sessions returned in the results will be identical to the original parameter.

window_days

integer, number of days to consider as optimization window.

window_start_hour

integer, starting hour of the optimization window.

responsive

Named two-level list with the ratio (between 0 and 1) of sessions responsive to smart charging program. The names of the list must exactly match the Time-cycle and User profiles names. For example: list(Monday = list(Worktime = 1, Shortstay = 0.1))

power_th

numeric, power threshold (between 0 and 1) accepted from setpoint. For example, with power_th = 0.1 and setpoint = 100 for a certain time slot, then sessions' demand can reach a value of 110 without needing to schedule sessions.

charging_power_min

numeric. It can be configured in two ways: (1) minimum allowed ratio (between 0 and 1) of nominal power (i.e. Power column in sessions), or (2) specific value of minimum power (in kW) higher than 1 kW.

For example, if charging_power_min = 0.5 and method = 'curtail', sessions' charging power can only be curtailed until the 50% of the nominal charging power. And if charging_power_min = 2, sessions' charging power can be curtailed until 2 kW.

energy_min

numeric, minimum allowed ratio (between 0 and 1) of required energy.

include_log

logical, whether to output the algorithm messages for every user profile and time-slot

show_progress

logical, whether to output the progress bar in the console

lambda

numeric, penalty on change for the flexible load.

mc.cores

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

Value

a list with three elements: optimal setpoints (tibble), sessions schedule (tibble) and log messages (list with character strings). The date-time values in the log list are in the time zone of the opt_data.

Details

An important parameter of this function is opt_data, which defines the time sequence of the smart charging algorithm and the optimization variables. The opt_data parameter is directly related with the opt_objective parameter. There are three different optimization objectives implemented by this function:

  • Minimize grid interaction (opt_objective = "grid"): minimizes the peak of the flexible load and the amount of imported power from the grid. If production is not found in opt_data, only a peak shaving objective will be considered.

  • Minimize the energy cost (opt_objective = "cost"): minimizes the energy cost. In this case, the columns grid_capacity, price_imported, price_exported, price_turn_up and price_turn_down of tibble opt_data are important. If these variables are not configured, default values of grid_capacity = Inf, price_imported = 1, price_exported = 0, price_turn_up = 0 and price_turn_down = 0 are considered to just minimize the imported energy.

  • Combined optimization (opt_objective between 0 and 1): minimizes both the net power peaks and energy cost.

  • No optimization (opt_objective = "none"): this will skip optimization and at least one user profile name must be in an opt_data column to be considered as a setpoint for the scheduling algorithm. The user profiles that don't appear in opt_data will not be optimized.

Examples

# Example: we will use the example data set of charging sessions
# from the `evsim` package.

# The user profiles of this data set are `Visit` and `Worktime`,
# identified in two different time cycles `Workday` and `Weekend`.
# These two variables in the `sessions` tibble, `Profile` and `Timecycle`,
# are required for the `smart_charging` function and give more versatility
# to the smart charging context. For example, we may want to only coordinate
# `Worktime` sessions instead of all sessions.

# For this example we want the following:
# - Curtail only `Worktime` sessions, which have a responsiveness rate of
# 0.9 (i.e. 90% of Worktime users accept to postpone the session).
# - Minimize the power peak of the sessions (peak shaving)
# - Time series resolution of 15 minutes
# - Optimization window of 24 hours from 6:00AM to 6:00 AM
# - The energy charged can be reduced up to 50% of the original requirement

library(dplyr)

# Use first 50 sessions
sessions <- evsim::california_ev_sessions_profiles %>%
  slice_head(n = 50) %>%
  evsim::adapt_charging_features(time_resolution = 15)
sessions_demand <- evsim::get_demand(sessions, resolution = 15)

# Don't require any other variable than datetime, since we don't
# care about local generation (just peak shaving objective)
opt_data <- tibble(
  datetime = sessions_demand$datetime,
  production = 0
)
sc_results <- smart_charging(
  sessions, opt_data, opt_objective = "grid", method = "curtail",
  window_days = 1, window_start_hour = 6,
  responsive = list(Workday = list(Worktime = 0.9)),
  energy_min = 0.5
)