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Battery optimal charging/discharging profile

Source: R/optimization.R
add_battery_optimization.Rd

See the formulation of the optimization problems in the documentation website.

Usage

add_battery_optimization(
  opt_data,
  opt_objective = "grid",
  Bcap,
  Bc,
  Bd,
  SOCmin = 0,
  SOCmax = 100,
  SOCini = NULL,
  window_days = 1,
  window_start_hour = 0,
  flex_window_hours = 24,
  lambda = 0,
  charge_eff = 1,
  discharge_eff = 1
)

Arguments

opt_data

tibble, optimization contextual data. The first column must be named datetime (mandatory) containing the date time sequence where the optimization algorithm is applied. The other columns can be (optional):

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

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

  • export_capacity: maximum exported 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 (€/kWh). This is used when opt_objective = "cost".

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

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

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

opt_objective

character or numeric. Optimization objective can be "grid" (default), "cost" or "curtail", or a number between 0 and 1 to perform combined optimization where 0 == "cost" and 1 == "grid".

Bcap

numeric, capacity of the battery (in kWh)

Bc

numeric, maximum charging power (in kW)

Bd

numeric, maximum discharging power (in kW)

SOCmin

numeric, minimum State-of-Charge of the battery

SOCmax

numeric, maximum State-of-Charge of the battery

SOCini

numeric, required State-of-Charge at the beginning/end of optimization window

window_days

integer, number of days to consider as optimization window.

window_start_hour

integer, starting hour of the optimization window.

flex_window_hours

integer, flexibility window length, in hours. This optional feature lets you apply flexibility only during few hours from the window_start_hour. It must be lower than window_days*24 hours.

lambda

numeric, penalty on change for the battery compared to the previous time slot.

charge_eff

numeric, battery charging efficiency (from 0 to 1, default 1).

discharge_eff

numeric, battery discharging efficiency (from 0 to 1, default 1).

Value

numeric vector

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
opt_data <- flextools::energy_profiles %>%
  filter(lubridate::isoweek(datetime) == 18) %>%
  rename(
    production = "solar"
  ) %>%
  select(any_of(c(
    "datetime", "production", "building", "price_imported", "price_exported"
  ))) %>%
  mutate(
    static = .data$building
  )
  opt_battery <- opt_data %>%
    add_battery_optimization(
      opt_objective = 0.5,
      Bcap = 50, Bc = 4, Bd = 4,
      window_start_hour = 5
    )
#> ⚠️ Optimization warning: optimization not feasible in some windows. Removing grid constraints.
#> ⚠️ Optimization warning: dual infeasible. Disabling battery for some windows.