init commit

config_model.yml

 # Path to data folder
-#data_path: 'D:/ULg_PhD_work/datasets/resite_ip/'
-data_path: '/data/dcradu/resite_ip/'
+data_path: 'D:/ULg_PhD_work/datasets/resite_ip/'
+#data_path: '/data/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: ['2010-01-01T00:00', '2019-12-31T23:00']
+time_slice: ['2018-01-01T00:00', '2018-01-01T23:00']
 # Technologies to deploy.
 regions: ['GB', 'NL', 'FR', 'DE', 'DK', 'NO', 'PL', 'IE', 'IT', 'SE', 'FI', 'ES', 'GR', 'PT', 'BE', 'LT', 'LV', 'EE', 'HR']
-technologies: ['wind_offshore']
-deployments: [[80], [60], [57], [36], [35], [30], [28], [22], [20], [20], [15], [13], [10], [9], [6], [4], [3], [1], [1]]
+technologies: ['wind_offshore', 'wind_onshore']
+deployments: [[80, 15], [60, 8], [57, 36], [36, 70], [35, 5], [30, 10], [28, 11], [22, 6], [20, 14], [20, 12], [15, 5],
+              [13, 35], [10, 6], [9, 7], [6, 4], [4, 1], [3, 1], [1, 1], [1, 2]]
+load_coverage: 0.5
 
 siting_params:
   # Defines how \alpha is considered in space and time.
@@ -21,7 +23,7 @@ siting_params:
   # Time-window length used to compute the criticality indicator. Integer value.
   delta: 1                          # \in \mathbb{N}
   # Threshold
-  c: 0.1                            # \in [0, 1]
+  c: 0.25                            # \in [0, 1]
   # Solution method: BB or HEU or RAND or GRED.
   solution_method:
     neighborhood: 1

config_model_partitioned.yml

-# Path to data folder
-#data_path: 'D:/ULg_PhD_work/datasets/resite_ip/'
-data_path: '/data/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: ['2010-01-01T00:00', '2019-12-31T23:00']
-resampling_rate: 3
-# Technologies to deploy.
-regions: ['GB', 'NL', 'FR', 'DE', 'DK', 'NO', 'PL', 'IE', 'IT', 'SE', 'FI', 'ES', 'GR', 'PT', 'BE', 'LT', 'LV', 'EE', 'HR']
-technologies: ['wind_offshore']
-deployments: [[80], [60], [57], [36], [35], [30], [28], [22], [20], [20], [15], [13], [10], [9], [6], [4], [3], [1], [1]]
-load_coverage: 0.3
-
-siting_params:
-  # Defines how \alpha is considered in space and time.
-  alpha:
-    method: 'load' # 'potentials'
-    coverage: 'system' # 'partition'
-    smoothing: 'mean' # 'median'
-    norm: 'min' # 'max'
-  # Time-window length used to compute the criticality indicator. Integer value.
-  delta: 1                          # \in \mathbb{N}
-  # Threshold
-  c: 0.1                            # \in [0, 1]
-  # Solution method: BB or HEU or RAND or GRED.
-  solution_method:
-    neighborhood: 1
-    no_iterations: 5000
-    no_epochs: 500
-    initial_temp: 100.
-    no_runs: 30

config_model_unpartitioned.yml

-# Path to data folder
-#data_path: 'D:/ULg_PhD_work/datasets/resite_ip/'
-data_path: '/data/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: ['2010-01-01T00:00', '2019-12-31T23:00']
-resampling_rate: 3
-# Technologies to deploy.
-regions: ['EU']
-technologies: ['wind_offshore']
-deployments: [[468]]
-load_coverage: 0.3
-
-siting_params:
-  # Defines how \alpha is considered in space and time.
-  alpha:
-    method: 'load' # 'potentials'
-    coverage: 'system' # 'partition'
-    smoothing: 'mean' # 'median'
-    norm: 'min' # 'max'
-  # Time-window length used to compute the criticality indicator. Integer value.
-  delta: 1                          # \in \mathbb{N}
-  # Threshold
-  c: 0.1                            # \in [0, 1]
-  # Solution method: BB or HEU or RAND or GRED.
-  solution_method:
-    neighborhood: 1
-    no_iterations: 5000
-    no_epochs: 500
-    initial_temp: 100.
-    no_runs: 30

config_techs.yml

 # Config file for various conversion technologies.
 wind_onshore:
   where: 'onshore'
-  filters: ['resource_quality', 'population_density', 'orography', 'forestry', 'water_mask', 'latitude']
+  filters: ['population_density', 'orography', 'forestry', 'water_mask', 'latitude', 'legacy']
   converter_IV: 'V110'
   converter_III: 'E103'
   converter_II: 'V90'
@@ -21,7 +21,7 @@ wind_onshore:
   latitude_threshold: 65.
   legacy_min: 0.1
   power_density: 5. # MW/sqkm
-  land_utilization_factor: 0.5
+  land_utilization_factor: 0.3
 
 wind_offshore:
   where: 'offshore'

src/main.py

 import yaml
 import julia
-from os.path import join, isfile
+from os.path import join
 from numpy import argmax, ceil, float64
-import argparse
 import pickle
 
 from copy import deepcopy
@@ -17,86 +16,37 @@ logging.disable(logging.CRITICAL)
 logger = logging.getLogger(__name__)
 
 
-def parse_args():
-
-    parser = argparse.ArgumentParser(description='Command line arguments.')
-
-    parser.add_argument('--k', type=str, default=None)
-    parser.add_argument('--c', type=float)
-    parser.add_argument('--alpha_method', type=str, default=None)
-    parser.add_argument('--alpha_coverage', type=str, default=None)
-    parser.add_argument('--delta', type=int, default=None)
-    parser.add_argument('--resampling_rate', type=str)
-    parser.add_argument('--maxdepth', type=str)
-
-    parsed_args = vars(parser.parse_args())
-
-    return parsed_args
-
-
 if __name__ == '__main__':
 
-    args = parse_args()
-
     logger.info('Starting data pre-processing.')
 
-    model_parameters = read_inputs(f"../config_model_{args['k']}.yml")
-    model_parameters['resampling_rate'] = args['resampling_rate']
+    model_parameters = read_inputs(f"../config_model.yml")
+    model_parameters['resampling_rate'] = 1
     siting_parameters = model_parameters['siting_params']
     tech_parameters = read_inputs('../config_techs.yml')
 
-    siting_parameters['alpha']['method'] = args['alpha_method']
-    siting_parameters['alpha']['coverage'] = args['alpha_coverage']
-    siting_parameters['delta'] = int(args['delta'])
-    siting_parameters['c'] = args['c']
-
     data_path = model_parameters['data_path']
     spatial_resolution = model_parameters['spatial_resolution']
     time_horizon = model_parameters['time_slice']
 
     database = read_database(data_path, spatial_resolution)
 
-    if isfile(join(data_path, f"input/capacity_factors_data_{args['k']}"
-                              f"_{args['resampling_rate']}h_{args['maxdepth']}m.p")):
-
-        capacity_factors_data = \
-            pickle.load(open(join(data_path,
-                                  f"input/capacity_factors_data_{args['k']}_{args['resampling_rate']}h_"
-                                  f"{args['maxdepth']}m.p"), 'rb'))
-        site_coordinates = \
-            pickle.load(open(join(data_path, f"input/site_coordinates_{args['k']}_{args['maxdepth']}m.p"), 'rb'))
-        legacy_coordinates = \
-            pickle.load(open(join(data_path, f"input/legacy_coordinates_{args['k']}_{args['maxdepth']}m.p"), 'rb'))
-        logger.info('Input files read from disk.')
-
-    else:
-
-        site_coordinates, legacy_coordinates = return_filtered_coordinates(database, model_parameters, tech_parameters)
-        truncated_data = selected_data(database, site_coordinates, time_horizon)
-        capacity_factors_data = return_output(truncated_data, data_path)
-
-        resampled_data = deepcopy(capacity_factors_data)
-        rate = model_parameters['resampling_rate']
-        for region in capacity_factors_data.keys():
-            for tech in capacity_factors_data[region].keys():
-                resampled_data[region][tech] = \
-                    capacity_factors_data[region][tech].resample(time=f"{rate}H").mean(dim='time')
-
-        pickle.dump(resampled_data,
-                    open(join(data_path, f"input/capacity_factors_data_{args['k']}_{args['resampling_rate']}h_"
-                                         f"{args['maxdepth']}m.p"), 'wb'), protocol=4)
-        pickle.dump(site_coordinates,
-                    open(join(data_path, f"input/site_coordinates_{args['k']}_"
-                                         f"{args['maxdepth']}m.p"), 'wb'), protocol=4)
-        pickle.dump(legacy_coordinates,
-                    open(join(data_path, f"input/legacy_coordinates_{args['k']}_"
-                                         f"{args['maxdepth']}m.p"), 'wb'), protocol=4)
-        logger.info('Input files written to disk.')
+    site_coordinates, legacy_coordinates = return_filtered_coordinates(database, model_parameters, tech_parameters)
+    truncated_data = selected_data(database, site_coordinates, time_horizon)
+    capacity_factors_data = return_output(truncated_data, data_path)
+
+    resampled_data = deepcopy(capacity_factors_data)
+    rate = model_parameters['resampling_rate']
+    for region in capacity_factors_data.keys():
+        for tech in capacity_factors_data[region].keys():
+            resampled_data[region][tech] = \
+                capacity_factors_data[region][tech].resample(time=f"{rate}H").mean(dim='time')
 
     time_windows_data = resource_quality_mapping(capacity_factors_data, siting_parameters)
     site_positions = sites_position_mapping(time_windows_data)
     deployment_dict = capacity_to_cardinality(database, model_parameters, tech_parameters, site_coordinates,
                                               legacy_coordinates)
+
     site_potential_data = get_potential_per_site(time_windows_data, tech_parameters, spatial_resolution)
     criticality_data = xarray_to_ndarray(critical_window_mapping(time_windows_data, site_potential_data,
                                                                  deployment_dict, model_parameters))
@@ -105,8 +55,7 @@ if __name__ == '__main__':
     total_no_locs = sum(deployment_dict[r][t] for r in deployment_dict.keys() for t in deployment_dict[r].keys())
     c = int(ceil(siting_parameters['c'] * total_no_locs))
 
-    output_folder = init_folder(model_parameters, total_no_locs, c,
-                                suffix=f"_{args['alpha_method']}_{args['alpha_coverage']}_d{args['delta']}")
+    output_folder = init_folder(model_parameters, total_no_locs, c, suffix='_test')
 
     logger.info('Data pre-processing finished. Opening Julia instance.')