Compute Bellman values for several storage based on one simulation

First, an Antares simulation (based on short-term trajectories or trajectories given by the simulation output of simu) is run. Marginal prices and costs of this simulation are used to build univariate and independent reward functions for each area with the function get_Reward() with method_old = F. Finally, Bellman values and water values are computed with Grid_Matrix(). This method is fast and gives good results when storage are not too important in the study. Otherwise, it is better to compute Bellman values storage per storage.

Usage

getBellmanValuesFromOneSimulationMultistock(
  opts,
  path_solver,
  mcyears,
  list_areas,
  list_pumping,
  list_efficiency,
  force_final_level,
  penalty_final_level_low,
  penalty_final_level_high,
  penalty_low,
  penalty_high,
  write_vu = F,
  simu = NULL
)

Arguments

opts

List of study parameters returned by the function antaresRead::setSimulationPath(simulation="input") in input mode.

path_solver

Character containing the Antares Solver path, argument passed to runSimulation.

mcyears

Vector of integer. Monte Carlo years used to compute water values.

list_areas

Vector of areas concerned by simulations.

list_pumping

Named vector of binary to tell if pumping is available in areas.

list_efficiency

Named vector of pumping efficiency.

force_final_level

Binary. Whether final level should be constrained.

penalty_final_level_low

Double. Penalties for both bottom rule curve to constrain final level.

penalty_final_level_high

Double. Penalties for top rule curve to constrain final level.

penalty_low

Double. Penalty for violating the bottom rule curve, comparable to the unsupplied energy cost.

penalty_high

Double. Penalty for violating the top rule curve, comparable to the spilled energy cost.

write_vu

Binary. True to write water values in the Antares study.

simu

List of simulation parameters returned by the function antaresRead::setSimulationPath() with the simulation selected from which to use the storage trajectory to run the simulation.

Value

A dplyr::tibble() similar to aggregated_results from Grid_Matrix() with one additional column "area".