Calculate an optimal trajectory for the reservoir levels based on Bellman values and reward functions.

Calculate an optimal trajectory for the reservoir levels based on Bellman values and reward functions.

Usage

getOptimalTrend(
  level_init,
  watervalues,
  mcyears,
  reservoir_capacity,
  penalty_low,
  penalty_high,
  penalty_final_level,
  final_level,
  max_hydro_weekly,
  seed = 0,
  efficiency,
  mix_scenario = TRUE,
  reward
)

Arguments

level_init

Initial level of the reservoir in MWh generated by get_initial_level_year_per_year().

watervalues

Data frame aggregated watervalues generated by Grid_Matrix().

mcyears

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

reservoir_capacity

Double. Reservoir capacity for the given area in MWh given by get_reservoir_capacity().

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.

penalty_final_level

Penalties (for both bottom and top rule curves) to force final level

final_level

Double. Final level (in percent between 0 and 100) if final level is constrained. If you want initial level, use get_initial_level().

max_hydro_weekly

Maximum weekly pumping and generating power generated by the function get_max_hydro() with timeStep="weekly".

seed

If scenario are mixed, seed to make results reproducible.

efficiency

Double between 0 and 1. Pumping efficiency ratio. Get it with getPumpEfficiency().

mix_scenario

Should scenario be mix from one week to another ?

reward

Output reward of get_Reward()

Value

Data frame with level ("lev") and optimal transition ("constraint") for each ("week") and each ("scenario"). For each ("week"), the ("scenario") used is given by ("mcYear").