Computes the total reward for a specific candidate following the reward function

Usage

total_cost_loop(
  area,
  mc_years,
  candidate_pool,
  candidates_types,
  storage_vol,
  df_reward,
  cvar,
  opts,
  penalty_low,
  penalty_high,
  penalty_final_level,
  storage_annual_cost,
  final_level,
  compute_optimal_traj = F,
  max_hydro_weekly,
  inflow
)

Arguments

area: Character. Name of the area where the total reward is computed
mc_years: Vector of integers. Monte Carlo years to compute reward
candidate_pool: Vector of integers. Capacity of the cluster candidates
candidates_types: Data_frame with column names : c("index", "name", "type", "TOTEX", "Marg_price"). It is a parameter of MultiStock_H2_Investment_reward_compute_once.
storage_vol: Integer. Volume of the storage candidate
df_reward: List. Rewards and decision space from the output of the function antaresWaterValues::get_Reward().
cvar: Numeric in [0,1]. The probability used in cvar algorithm.
opts: List of study parameters returned by the function antaresRead::setSimulationPath(simulation="input") in input mode.
penalty_low: Integer. Penalty for lower guide curve.
penalty_high: Integer. Penalty for higher guide curve.
penalty_final_level: Integer. Penalty for higher and lower final level.
storage_annual_cost: Numeric. Total annual cost of the storage candidate in eur/MWh
final_level: Numeric in [0, 100]. Final and initial level in percentage of H2 storage.
compute_optimal_traj: Boolean.
max_hydro_weekly: Data.frame. Generated by get_max_hydro()
inflow: Generated by get_inflow()

Value

The total reward for the specified candidates in euros. Because of the Bellman values it is usually negative and large