testing candidate sites selection

config_model.yml

@@ -3,16 +3,13 @@
 data_path: '/data/dcradu/resite_ip/'
 
 # Spatial resolution (in degrees) of the potential sites.
-spatial_resolution: 0.28
+spatial_resolution: 0.25
 # Start time and end time of the analysis.
-time_slice: ['2016-01-01T00:00', '2018-12-31T23:00']
+time_slice: ['2018-01-01T00:00', '2018-01-31T23:00']
 # Technologies to deploy.
-regions: ['GB', 'NL', 'FR', 'DE', 'DK', 'NO', 'PL', 'IE', 'IT', 'SE', 'FI', 'ES', 'GR', 'PT', 'BE', 'LT', 'LV', 'EE']
+regions: ['GB', 'NL', 'FR', 'DE', 'DK', 'NO', 'PL', 'IE', 'IT', 'SE', 'FI', 'ES', 'GR', 'PT', 'BE', 'LT', 'LV', 'EE', 'HR']
 technologies: ['wind_offshore']
-deployments: [[48], [36], [35], [22], [21], [18], [17], [14], [12], [12], [9], [8], [6], [6], [3], [3], [2], [1]]
-# deployments: [[21], [16], [15], [10], [9], [8], [8], [6], [6], [6], [4], [4], [3], [3], [2], [2], [1], [1]]
-# deployments: [[14], [10], [10], [6], [6], [5], [5], [4], [4], [4], [3], [3], [2], [2], [1], [1], [1], [1]]
-# deployments: [[10], [8], [7], [5], [5], [4], [4], [3], [3], [3], [2], [2], [2], [2], [1], [1], [1], [1]]
+deployments: [[31], [23], [22], [14], [14], [12], [11], [9], [8], [8], [6], [5], [4], [4], [3], [2], [2], [1], [1]]
 
 siting_params:
   smooth_measure: 'mean'

config_techs.yml

@@ -35,13 +35,13 @@ wind_offshore:
   protected_areas_selection: ['Ia', 'Ib', 'II', 'V']
   protected_areas_distance_threshold: 5.
   depth_threshold_low: 0.
-  depth_threshold_high: 999.
+  depth_threshold_high: 99.
   altitude_threshold: 0.
   terrain_slope_threshold: 1.
   forestry_ratio_threshold: 1.
-  latitude_threshold: 70.
+  latitude_threshold: 65.
   distance_threshold_min: 22.2
-  distance_threshold_max: 222.2 # 111.
+  distance_threshold_max: 111.0 # 111.
 
 wind_floating:
   where: 'offshore'

src/helpers.py

@@ -346,9 +346,9 @@ def return_coordinates_from_shapefiles(resource_dataset, shapefiles_region):
 
 def retrieve_load_data_partitions(data_path, date_slice, alpha, delta, regions, norm_type):
 
-    load_data_fn = join(data_path, 'input/load_data', 'load_2009_2018.csv')
+    load_data_fn = join(data_path, 'input/load_data', 'load_entsoe_2006_2020_full.csv')
     load_data = read_csv(load_data_fn, index_col=0)
-    load_data.index = date_range('2009-01-01T00:00', '2018-12-31T23:00', freq='H')
+    load_data.index = to_datetime(load_data.index)
     load_data_sliced = load_data.loc[date_slice[0]:date_slice[1]]
 
     regions_list = return_region_divisions(regions, data_path)

src/main.py

@@ -35,6 +35,11 @@ if __name__ == '__main__':
     criticality_data = xarray_to_ndarray(critical_window_mapping(time_windows_data, model_parameters))
     site_positions = sites_position_mapping(time_windows_data)
 
+    import pickle
+    pickle.dump(site_coordinates, open(join(data_path, 'input/site_coordinates.p'), 'wb'), protocol=4)
+    import sys
+    sys.exit()
+
     custom_log(' Data read. Building model.')
 
     if siting_parameters['solution_method']['BB']['set']:

src/tools.py

@@ -141,6 +141,7 @@ def filter_locations_by_layer(regions, start_coordinates, model_params, tech_par
     elif which == 'resource_quality':
 
         database = read_database(data_path, model_params['spatial_resolution'])
+        print(database)
 
         if tech_params['resource'] == 'wind':
             array_resource = xu.sqrt(database.u100 ** 2 +
@@ -148,7 +149,7 @@ def filter_locations_by_layer(regions, start_coordinates, model_params, tech_par
         elif tech_params['resource'] == 'solar':
             array_resource = database.ssrd / 3600.
         else:
-            raise ValueError (" This resource is not available.")
+            raise ValueError(" This resource is not available.")
 
         array_resource_mean = array_resource.mean(dim='time')
         mask_resource = array_resource_mean.where(array_resource_mean.data < tech_params['resource_threshold'])