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Commit 12a35e2c authored by David Radu's avatar David Radu
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jl update on rand runs.

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config_model.yml
+ 61
− 38
View file @ 12a35e2c
# Spatial resolution (in degrees) of the potential sites.
spatial_resolution: 0.5
spatial_resolution: 0.28
# Path towards the various input data.
path_resource_data: '../input_data/resource_data'
......@@ -26,7 +26,7 @@ legacy_layer: False
distance_layer: True
# 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']
# List of regions to be considered in the optimization. To note that a list of pre-defined regions is available
# in tools.py, within the `return_coordinates_from_countries` function. Yet, if additional regions are defined,
# an associated load signal should be assigned in the `read_load_data` function.
......@@ -42,11 +42,11 @@ 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: 'min'
norm_type: 'max'
# Time-window length used to compute the criticality indicator. Integer value.
delta: 1
# Geographical coverage threshold used to compute the criticality indicator. Float value between 0.0 and 1.0
c: [8, 16, 24, 32, 40, 48, 62]
#c: [8, 16, 24, 32, 40, 48, 62]
#c: [20, 40, 60, 80, 100, 120, 171]
#c: [40, 80, 120, 160, 200, 240, 330]
......@@ -64,6 +64,7 @@ no_epochs: 500
initial_temp: 200.
no_runs: 20
algorithm: 'SALS' #'GLS'
which_sol: 'max' #'rand'
# Solution method: BB or HEU.
solution_method: 'HEU'
......@@ -71,12 +72,12 @@ solution_method: 'HEU'
# Dict of deployments per partition (ordered as the 'regions' list above)
#deployment_vector: {'EU':{'wind_offshore':62}}
c: [32]
deployment_vector: {'UK':{'wind_offshore':10},
'NL':{'wind_offshore':7},
'NL':{'wind_offshore':8},
'FR':{'wind_offshore':7},
'DE':{'wind_offshore':5},
'DK':{'wind_offshore':4},
'DK':{'wind_offshore':5},
'NO':{'wind_offshore':4},
'PL':{'wind_offshore':4},
'IE':{'wind_offshore':3},
......@@ -91,42 +92,64 @@ deployment_vector: {'UK':{'wind_offshore':10},
'LV':{'wind_offshore':1},
'EE':{'wind_offshore':1}}
#deployment_vector: {'UK':{'wind_offshore':29},
# 'NL':{'wind_offshore':22},
# 'FR':{'wind_offshore':21},
# 'DE':{'wind_offshore':13},
# 'DK':{'wind_offshore':13},
# 'NO':{'wind_offshore':11},
# 'PL':{'wind_offshore':11},
# 'IE':{'wind_offshore':8},
# 'IT':{'wind_offshore':8},
# 'SE':{'wind_offshore':8},
# 'FI':{'wind_offshore':6},
# 'ES':{'wind_offshore':5},
# 'EL':{'wind_offshore':4},
# 'PT':{'wind_offshore':4},
# 'BE':{'wind_offshore':3},
#c: [41]
#deployment_vector: {'UK':{'wind_offshore':14},
# 'NL':{'wind_offshore':10},
# 'FR':{'wind_offshore':10},
# 'DE':{'wind_offshore':6},
# 'DK':{'wind_offshore':6},
# 'NO':{'wind_offshore':5},
# 'PL':{'wind_offshore':5},
# 'IE':{'wind_offshore':4},
# 'IT':{'wind_offshore':4},
# 'SE':{'wind_offshore':4},
# 'FI':{'wind_offshore':3},
# 'ES':{'wind_offshore':3},
# 'EL':{'wind_offshore':2},
# 'PT':{'wind_offshore':2},
# 'BE':{'wind_offshore':1},
# 'LT':{'wind_offshore':1},
# 'LV':{'wind_offshore':1},
# 'EE':{'wind_offshore':1}}
#c: [62]
#deployment_vector: {'UK':{'wind_offshore':21},
# 'NL':{'wind_offshore':16},
# 'FR':{'wind_offshore':15},
# 'DE':{'wind_offshore':10},
# 'DK':{'wind_offshore':9},
# 'NO':{'wind_offshore':8},
# 'PL':{'wind_offshore':8},
# 'IE':{'wind_offshore'6},
# 'IT':{'wind_offshore':6},
# 'SE':{'wind_offshore':6},
# 'FI':{'wind_offshore':4},
# 'ES':{'wind_offshore':4},
# 'EL':{'wind_offshore':3},
# 'PT':{'wind_offshore':3},
# 'BE':{'wind_offshore':2},
# 'LT':{'wind_offshore':2},
# 'LV':{'wind_offshore':2},
# 'LV':{'wind_offshore':1},
# 'EE':{'wind_offshore':1}}
#deployment_vector: {'UK':{'wind_offshore':58},
# 'NL':{'wind_offshore':44},
# 'FR':{'wind_offshore':41},
# 'DE':{'wind_offshore':26},
# 'DK':{'wind_offshore':26},
# 'NO':{'wind_offshore':22},
# 'PL':{'wind_offshore':21},
# 'IE':{'wind_offshore':16},
# 'IT':{'wind_offshore':15},
# 'SE':{'wind_offshore':15},
# 'FI':{'wind_offshore':11},
# 'ES':{'wind_offshore':10},
# 'EL':{'wind_offshore':8},
# 'PT':{'wind_offshore':7},
#c: [136]
#deployment_vector: {'UK':{'wind_offshore':48},
# 'NL':{'wind_offshore':36},
# 'FR':{'wind_offshore':35},
# 'DE':{'wind_offshore':22},
# 'DK':{'wind_offshore':21},
# 'NO':{'wind_offshore':18},
# 'PL':{'wind_offshore':17},
# 'IE':{'wind_offshore':14},
# 'IT':{'wind_offshore':12},
# 'SE':{'wind_offshore':12},
# 'FI':{'wind_offshore':9},
# 'ES':{'wind_offshore':8},
# 'EL':{'wind_offshore':6},
# 'PT':{'wind_offshore':6},
# 'BE':{'wind_offshore':3},
# 'LT':{'wind_offshore':3},
# 'LV':{'wind_offshore':3},
# 'LV':{'wind_offshore':2},
# 'EE':{'wind_offshore':1}}
# Keeping files at the end of the run.
......
src/jl/main_heuristics.jl
+ 1
− 2
View file @ 12a35e2c
......@@ -41,7 +41,6 @@ function main_call(index_dict, deployment_dict, D, c, N, I, E, T_init, R, run)
throw(ArgumentError)
end
x = x_sol[findmax(LB_sol)[2], :]
return x_sol
return x
end
src/main.py
+ 27
− 6
View file @ 12a35e2c
......@@ -71,17 +71,38 @@ elif solution_method == 'HEU':
for c in parameters['c']:
print('Running heuristic for c value of', c)
output_folder = init_folder(parameters, input_dict, suffix='_c' + str(c))
with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
yaml.dump(parameters, outfile, default_flow_style=False, sort_keys=False)
jl_selected = fn(jl_dict['index_dict'], jl_dict['deployment_dict'], jl_dict['criticality_matrix'], c,
parameters['neighborhood'], parameters['no_iterations'], parameters['no_epochs'],
parameters['initial_temp'], parameters['no_runs'], parameters['algorithm'])
jl_locations, jl_objective = retrieve_location_dict_jl(jl_selected, parameters, input_dict, indices)
retrieve_site_data(parameters, input_dict, output_folder, jl_locations, jl_objective,
suffix=None)
if parameters['which_sol'] == 'max':
print(jl_selected[:, 0])
jl_selected = jl_selected[max(jl_selected[:, 0]), :]
print(jl_selected[max(jl_selected[:, 0]), :])
output_folder = init_folder(parameters, input_dict, suffix='_c' + str(c))
with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
yaml.dump(parameters, outfile, default_flow_style=False, sort_keys=False)
jl_locations, jl_objective = retrieve_location_dict_jl(jl_selected, parameters, input_dict, indices)
retrieve_site_data(parameters, input_dict, output_folder, jl_locations, jl_objective, suffix=None)
else: # 'rand'
seed = 1
print(jl_selected.shape[0])
for i in range(jl_selected.shape[0]):
output_folder = init_folder(parameters, input_dict, suffix='_c' + str(c) + '_seed' + str(seed))
seed += 1
with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
yaml.dump(parameters, outfile, default_flow_style=False, sort_keys=False)
jl_selected_seed = jl_selected[i, :]
print(jl_selected_seed)
jl_locations, jl_objective = retrieve_location_dict_jl(jl_selected_seed, parameters, input_dict, indices)
retrieve_site_data(parameters, input_dict, output_folder, jl_locations, jl_objective, suffix=None)
else:
raise ValueError(' This solution method is not available. Retry.')
......
src/tools.py
+ 1
− 1
View file @ 12a35e2c
......@@ -939,7 +939,7 @@ def retrieve_site_data(model_parameters, model_data, output_folder, site_coordin
pickle.dump(tech_dict, open(join(output_folder, 'init_coordinates_dict.p'), 'wb'))
with open("objective.txt", "w") as file:
with open(join(output_folder, 'objective.txt'), "w") as file:
print(objective, file=file)
return output_location
......
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