config_model.yml

# Path to data folder
data_path: 'D:/ULg_PhD_work/datasets/resite_ip/'
#data_path: '/data/dcradu/resite_ip/'
#data_path: '../../EVO/dcradu/resite_ip/'

# Spatial resolution (in degrees) of the potential sites.
spatial_resolution: 0.25
# Start time and end time of the analysis.
time_slice: ['2017-01-01T00:00', '2019-12-31T23:00']
# Technologies to deploy.
regions: ['EU']
technologies: ['wind_onshore']
deployments: [[560]]

siting_params:
  smooth_measure: 'mean'
  # Defines how \alpha is considered in space and time.
  alpha: 'load_central'
  # Normalization procedures (detailed in tools.py). (min, max)
  norm_type: 'max'
  # Time-window length used to compute the criticality indicator. Integer value.
  delta: 1
  # Solution method: BB or HEU or RAND or GRED.
  solution_method:
    BB:
      # Branch & Bound
      set: True
      mir: True
      c: [1, 112, 224, 336, 448, 560]
      solver: 'gurobi'
      mipgap: 0.01
      timelimit: 3600
      threads: 1
    MIRSA:
      # Simulated Annealing with Local Search
      set: False
      c: [1, 112, 224, 336, 448, 560]
      neighborhood: 1
      no_iterations: 2000
      no_epochs: 500
      initial_temp: 200.
      no_runs: 1
      algorithm: 'SALS' # 'GLS' (Greedy Local Search)
    GRED:
      set: False
      p: 0.10 # [0.05, 0.10, 0.15, 0.20]
      c: [1, 112, 224, 336, 448, 560]
      no_runs: 1
      algorithm: 'STGH' # TGH, STGH
    RAND:
      # Random Search
      set: False
      c: [1, 112, 224, 336, 448, 560]
      no_iterations: 50
      no_epochs: 500
      no_runs: 1
      algorithm: 'RS'