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Battery optimal charging/discharging profile

Source: R/battery_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,
  cycle_cost = 0
)

Arguments

opt_data

tibble, optimization contextual data. The first column must be named datetime (mandatory). Optional columns:

  • static: static power demand (kW)

  • production: local generation (kW)

  • import_capacity: max grid import (kW)

  • export_capacity: max grid export (kW)

  • price_imported: energy import price (required for cost/combined)

  • price_exported: energy export price (required for cost/combined)

opt_objective

character or numeric. "grid" (default), "capacity", "cost", or a numeric weight w where w=1 is pure grid and w=0 is pure cost.

Bcap

numeric, battery capacity (kWh)

Bc

numeric, maximum charging power (kW)

Bd

numeric, maximum discharging power (kW)

SOCmin

numeric, minimum State-of-Charge (%)

SOCmax

numeric, maximum State-of-Charge (%)

SOCini

numeric, initial State-of-Charge (%). Defaults to SOCmin.

window_days

integer, optimization window length in days.

window_start_hour

integer, start hour of each optimization window.

flex_window_hours

numeric, flexibility window length (hours).

lambda

numeric, ramping penalty weight. Penalises rapid changes in battery power between consecutive time slots.

charge_eff

numeric, charging efficiency in (0, 1]. Default 1 (lossless). Embeds round-trip losses in the SOC constraints for accurate energy accounting.

discharge_eff

numeric, discharging efficiency in (0, 1]. Default 1 (lossless). See charge_eff.

cycle_cost

numeric, degradation cost per kWh cycled (Euro/kWh). Default 0. Adds a linear penalty on battery discharge so the optimizer trades off energy cost savings against battery wear. When positive, the problem is solved as a pure LP (no binary variables) which is substantially faster than the default MILP.

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", static = "building") %>%
  select(any_of(c(
    "datetime", "production", "static", "price_imported", "price_exported"
  )))
opt_battery <- opt_data %>%
  add_battery_optimization(
    opt_objective = "grid",
    Bcap = 50, Bc = 4, Bd = 4,
    window_start_hour = 5
  )