diff --git a/config_model.yml b/config_model.yml
index e3c396419a16ab74f2779e4e7bb9462a790e8d79..e9b99a3c5f7851c4cec5f9047cd5403941d062ff 100644
--- a/config_model.yml
+++ b/config_model.yml
@@ -5,7 +5,7 @@ data_path: '/home/dcradu/data/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']
+time_slice: ['2010-01-01T00:00', '2019-12-31T23:00']
# Technologies to deploy.
regions: ['EU']
technologies: ['wind_onshore']
@@ -20,36 +20,38 @@ siting_params:
# Time-window length used to compute the criticality indicator. Integer value.
delta: 1
# Solution method: BB or HEU or RAND or GRED.
+ c: 112
+
solution_method:
BB:
# Branch & Bound
- set: True
+ set: False
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]
+ set: True
neighborhood: 1
- no_iterations: 2000
+ no_iterations: 1000
no_epochs: 500
initial_temp: 200.
no_runs: 1
- algorithm: 'SALS' # 'GLS' (Greedy Local Search)
- GRED:
+ algorithm: 'RSSA' # 'GLS' (Greedy Local Search), 'RSSA', 'SALS'
+ GRED_DET:
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
+ no_runs: 5
+ algorithm: 'TGH'
+ GRED_STO:
+ set: False
+ p: 55 # 10% of 10150 locations
+ no_runs: 100
+ algorithm: 'STGH'
RAND:
# Random Search
set: False
- c: [1, 112, 224, 336, 448, 560]
no_iterations: 50
no_epochs: 500
no_runs: 1
diff --git a/src/jl/SitingHeuristics.jl b/src/jl/SitingHeuristics.jl
index 99449f35ccb5c2fcf5921fd261e23a492ca5de35..d36e740984d9a2fe11b3089fcf45108023d78753 100644
--- a/src/jl/SitingHeuristics.jl
+++ b/src/jl/SitingHeuristics.jl
@@ -30,7 +30,6 @@ function main_MIRSA(index_dict, deployment_dict, D, c, N, I, E, T_init, R, run)
x_init = solve_MILP_partitioning(D, c, n_partitions, index_dict, "Gurobi")
for r = 1:R
- println("Run ", r, "/", R)
x_sol[r, :], LB_sol[r], obj_sol[r, :] = simulated_annealing_local_search_partition(D, c, n_partitions, N, I, E, x_init, T_init, index_dict)
end
@@ -51,7 +50,6 @@ function main_MIRSA(index_dict, deployment_dict, D, c, N, I, E, T_init, R, run)
x_init = convert.(Float64, x_init)
for r = 1:R
- println("Run ", r, "/", R)
x_sol[r, :], LB_sol[r], obj_sol[r, :] = simulated_annealing_local_search_partition(D, c, n_partitions, N, I, E, x_init, T_init, index_dict)
end
@@ -77,15 +75,14 @@ function main_GRED(deployment_dict, D, c, R, p, run)
x_sol, LB_sol = Array{Float64, 2}(undef, R, L), Array{Float64, 1}(undef, R)
n = convert(Float64, deployment_dict[1])
for r = 1:R
- println("Run ", r, "/", R)
x_sol[r, :], LB_sol[r] = threshold_greedy_algorithm(D, c, n)
end
+
elseif run == "STGH"
x_sol, LB_sol = Array{Float64, 2}(undef, R, L), Array{Float64, 1}(undef, R)
n = convert(Float64, deployment_dict[1])
p = convert(Float64, p)
for r = 1:R
- println("Run ", r, "/", R)
x_sol[r, :], LB_sol[r] = stochastic_threshold_greedy_algorithm(D, c, n, p)
end
else
@@ -111,7 +108,6 @@ function main_RAND(deployment_dict, D, c, I, R, run)
x_sol, LB_sol = Array{Float64, 2}(undef, R, L), Array{Float64, 1}(undef, R)
n = convert(Float64, deployment_dict[1])
for r = 1:R
- println("Run ", r, "/", R)
x_sol[r, :], LB_sol[r] = random_search(D, c, n, I)
end
@@ -148,7 +144,6 @@ function main_LSEA(index_dict, deployment_dict, D, c, N, I, E, run)
x_sol, LB_sol, obj_sol = Array{Float64, 2}(undef, R, L), Array{Float64, 1}(undef, R), Array{Float64, 2}(undef, R, I)
x_init = solve_MILP_partitioning(D, c, n_partitions, index_dict, "Gurobi")
for r = 1:R
- println("Run ", r, "/", R)
x_sol[r, :], LB_sol[r], obj_sol[r, :] = greedy_local_search_partition(D, c, n_partitions, N, I, E, x_init, index_dict)
end
else
diff --git a/src/main.py b/src/main.py
index 1cbc092b70051d2f016f610813dc5b202f487aef..f4ed0745d2f90e2e72ba166518aa7acd94646f83 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,17 +1,41 @@
import pickle
import yaml
from os.path import join, isfile
-from numpy import array, argsort, sum
+from numpy import array, sum
from pyomo.opt import SolverFactory
import time
+import argparse
from helpers import read_inputs, init_folder, custom_log, xarray_to_ndarray, generate_jl_input, get_deployment_vector
from tools import read_database, return_filtered_coordinates, selected_data, return_output, resource_quality_mapping, \
- critical_window_mapping, sites_position_mapping, retrieve_location_dict, retrieve_site_data
+ critical_window_mapping, sites_position_mapping
from models import build_ip_model
+
+def parse_args():
+
+ parser = argparse.ArgumentParser(description='Command line arguments.')
+
+ parser.add_argument('-c', '--threshold', type=int)
+ parser.add_argument('-run_BB', '--run_BB_algorithm', type=bool, default=False)
+ parser.add_argument('-run_MIRSA', '--run_MIRSA_algorithm', type=bool, default=False)
+ parser.add_argument('-run_GRED_DET', '--run_GRED_DET_algorithm', type=bool, default=False)
+ parser.add_argument('-run_GRED_STO', '--run_GRED_STO_algorithm', type=bool, default=False)
+
+ parsed_args = vars(parser.parse_args())
+
+ return parsed_args
+
+
+def single_true(iterable):
+ i = iter(iterable)
+ return any(i) and not any(i)
+
+
if __name__ == '__main__':
+ args = parse_args()
+
model_parameters = read_inputs('../config_model.yml')
siting_parameters = model_parameters['siting_params']
tech_parameters = read_inputs('../config_techs.yml')
@@ -58,50 +82,51 @@ if __name__ == '__main__':
custom_log(' Data read. Building model.')
+ siting_parameters['solution_method']['BB']['set'] = args['run_BB']
+ siting_parameters['solution_method']['MIRSA']['set'] = args['run_MIRSA']
+ siting_parameters['solution_method']['GRED_DET']['set'] = args['run_GRED_DET']
+ siting_parameters['solution_method']['GRED_STO']['set'] = args['run_GRED_STO']
+
+ c = args['c']
+
+ if not single_true([args['run_BB'], args['run_MIRSA'], args['run_GRED_DET'], args['GRED_STO']]):
+ raise ValueError(' More than one run selected in the argparser.')
+
if siting_parameters['solution_method']['BB']['set']:
custom_log(' BB chosen to solve the IP.')
params = siting_parameters['solution_method']['BB']
- if not isinstance(params['c'], list):
- raise ValueError(' Values of c have to be elements of a list for the Branch & Bound set-up.')
-
- for c in params['c']:
+ output_folder = init_folder(model_parameters, c, f"_BB_MIR_str{params['mir']}")
+ with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
+ yaml.dump(model_parameters, outfile, default_flow_style=False, sort_keys=False)
+ with open(join(output_folder, 'config_techs.yaml'), 'w') as outfile:
+ yaml.dump(tech_parameters, outfile, default_flow_style=False, sort_keys=False)
- output_folder = init_folder(model_parameters, c, f"_BB_c{c}")
- with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
- yaml.dump(model_parameters, outfile, default_flow_style=False, sort_keys=False)
- with open(join(output_folder, 'config_techs.yaml'), 'w') as outfile:
- yaml.dump(tech_parameters, outfile, default_flow_style=False, sort_keys=False)
+ # Solver options for the MIP problem
+ opt = SolverFactory(params['solver'])
+ opt.options['MIPGap'] = params['mipgap']
+ opt.options['Threads'] = params['threads']
+ opt.options['TimeLimit'] = params['timelimit']
- # Solver options for the MIP problem
- opt = SolverFactory(params['solver'])
- opt.options['MIPGap'] = params['mipgap']
- opt.options['Threads'] = params['threads']
- opt.options['TimeLimit'] = params['timelimit']
+ instance = build_ip_model(deployment_dict, site_coordinates, criticality_data,
+ c, output_folder, params['mir'])
+ custom_log(' Sending model to solver.')
- instance = build_ip_model(deployment_dict, site_coordinates, criticality_data,
- c, output_folder, params['mir'])
- custom_log(' Sending model to solver.')
+ results = opt.solve(instance, tee=False, keepfiles=False,
+ report_timing=False, logfile=join(output_folder, 'solver_log.log'))
- results = opt.solve(instance, tee=False, keepfiles=False,
- report_timing=False, logfile=join(output_folder, 'solver_log.log'))
+ objective = instance.objective()
+ x_values = array(list(instance.x.extract_values().values()))
- objective = instance.objective()
- x_values = array(list(instance.x.extract_values().values()))
- comp_location_dict = retrieve_location_dict(x_values, model_parameters, site_positions)
- retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
- criticality_data, site_positions, c, comp_location_dict, objective,
- output_folder, benchmarks=False)
+ pickle.dump(x_values, open(join(output_folder, 'solution_matrix.p'), 'wb'))
+ pickle.dump(objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
elif siting_parameters['solution_method']['MIRSA']['set']:
custom_log(' MIRSA chosen to solve the IP. Opening a Julia instance.')
params = siting_parameters['solution_method']['MIRSA']
- if not isinstance(params['c'], list):
- raise ValueError(' Values of c have to be elements of a list for the heuristic set-up.')
-
jl_dict = generate_jl_input(deployment_dict, site_coordinates)
import julia
@@ -109,42 +134,68 @@ if __name__ == '__main__':
from julia import Main
Main.include("jl/SitingHeuristics.jl")
- for c in params['c']:
- print('Running heuristic for c value of', c)
- jl_selected, jl_objective, jl_traj = Main.main_MIRSA(jl_dict['index_dict'], jl_dict['deployment_dict'],
- criticality_data, c, params['neighborhood'],
- params['no_iterations'], params['no_epochs'],
- params['initial_temp'], params['no_runs'],
- params['algorithm'])
- output_folder = init_folder(model_parameters, c, suffix='_MIRSA')
-
- with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
- yaml.dump(model_parameters, outfile, default_flow_style=False, sort_keys=False)
- with open(join(output_folder, 'config_techs.yaml'), 'w') as outfile:
- yaml.dump(tech_parameters, outfile, default_flow_style=False, sort_keys=False)
-
- pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
- pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
- pickle.dump(jl_traj, open(join(output_folder, 'trajectory_matrix.p'), 'wb'))
-
- med_run = argsort(jl_objective)[params['no_runs']//2]
- jl_selected_seed = jl_selected[med_run, :]
- jl_objective_seed = jl_objective[med_run]
-
- jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
- retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
- criticality_data, site_positions, c, jl_locations, jl_objective_seed,
- output_folder, benchmarks=False)
-
- elif siting_parameters['solution_method']['RAND']['set']:
-
- custom_log(' Locations to be chosen via random search.')
- params = siting_parameters['solution_method']['RAND']
-
- if not isinstance(params['c'], list):
- raise ValueError(' Values of c have to provided as list for the RAND set-up.')
- if len(model_parameters['technologies']) > 1:
- raise ValueError(' This method is currently implemented for one single technology only.')
+ start = time.time()
+ jl_selected, jl_objective, jl_traj = Main.main_MIRSA(jl_dict['index_dict'], jl_dict['deployment_dict'],
+ criticality_data, c, params['neighborhood'],
+ params['no_iterations'], params['no_epochs'],
+ params['initial_temp'], params['no_runs'],
+ params['algorithm'])
+ end = time.time()
+ print(f"Average CPU time for c={c}: {round((end-start)/params['no_runs'], 1)} s")
+
+ output_folder = init_folder(model_parameters, c, suffix=f"_MIRSA_{params['algorithm']}")
+
+ with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
+ yaml.dump(model_parameters, outfile, default_flow_style=False, sort_keys=False)
+ with open(join(output_folder, 'config_techs.yaml'), 'w') as outfile:
+ yaml.dump(tech_parameters, outfile, default_flow_style=False, sort_keys=False)
+
+ pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
+ pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
+ pickle.dump(jl_traj, open(join(output_folder, 'trajectory_matrix.p'), 'wb'))
+
+ # elif siting_parameters['solution_method']['RAND']['set']:
+ #
+ # custom_log(' Locations to be chosen via random search.')
+ # params = siting_parameters['solution_method']['RAND']
+ #
+ # if not isinstance(params['c'], list):
+ # raise ValueError(' Values of c have to provided as list for the RAND set-up.')
+ # if len(model_parameters['technologies']) > 1:
+ # raise ValueError(' This method is currently implemented for one single technology only.')
+ #
+ # jl_dict = generate_jl_input(deployment_dict, site_coordinates)
+ #
+ # import julia
+ # j = julia.Julia(compiled_modules=False)
+ # from julia import Main
+ # Main.include("jl/SitingHeuristics.jl")
+ #
+ # for c in params['c']:
+ # print('Running heuristic for c value of', c)
+ #
+ # jl_selected, jl_objective = Main.main_RAND(jl_dict['deployment_dict'], criticality_data,
+ # c, params['no_iterations'], params['no_runs'],
+ # params['algorithm'])
+ #
+ # output_folder = init_folder(model_parameters, c, suffix='_RS')
+ #
+ # pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
+ # pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
+ #
+ # med_run = argsort(jl_objective)[params['no_runs']//2]
+ # jl_selected_seed = jl_selected[med_run, :]
+ # jl_objective_seed = jl_objective[med_run]
+ #
+ # jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
+ # retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
+ # criticality_data, site_positions, c, jl_locations, jl_objective_seed,
+ # output_folder, benchmarks=False)
+
+ elif siting_parameters['solution_method']['GRED_DET']['set']:
+
+ params = siting_parameters['solution_method']['GRED_DET']
+ custom_log(f" GRED_{params['algorithm']} chosen to solve the IP. Opening a Julia instance.")
jl_dict = generate_jl_input(deployment_dict, site_coordinates)
@@ -153,30 +204,20 @@ if __name__ == '__main__':
from julia import Main
Main.include("jl/SitingHeuristics.jl")
- for c in params['c']:
- print('Running heuristic for c value of', c)
-
- jl_selected, jl_objective = Main.main_RAND(jl_dict['deployment_dict'], criticality_data,
- c, params['no_iterations'], params['no_runs'],
- params['algorithm'])
-
- output_folder = init_folder(model_parameters, c, suffix='_RS')
+ start = time.time()
+ jl_selected, jl_objective = Main.main_GRED(jl_dict['deployment_dict'], criticality_data, c,
+ params['no_runs'], params['p'], params['algorithm'])
+ end = time.time()
+ print(f"Average CPU time for c={c}: {round((end-start)/params['no_runs'], 1)} s")
- pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
- pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
+ output_folder = init_folder(model_parameters, c, suffix=f"_GRED_{params['algorithm']}")
- med_run = argsort(jl_objective)[params['no_runs']//2]
- jl_selected_seed = jl_selected[med_run, :]
- jl_objective_seed = jl_objective[med_run]
+ pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
+ pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
- jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
- retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
- criticality_data, site_positions, c, jl_locations, jl_objective_seed,
- output_folder, benchmarks=False)
+ elif siting_parameters['solution_method']['GRED_STO']['set']:
- elif siting_parameters['solution_method']['GRED']['set']:
-
- params = siting_parameters['solution_method']['GRED']
+ params = siting_parameters['solution_method']['GRED_STO']
custom_log(f" GRED_{params['algorithm']} chosen to solve the IP. Opening a Julia instance.")
if not isinstance(params['c'], list):
@@ -189,27 +230,16 @@ if __name__ == '__main__':
from julia import Main
Main.include("jl/SitingHeuristics.jl")
- for c in params['c']:
- print('Running heuristic for c value of', c)
- start = time.time()
- jl_selected, jl_objective = Main.main_GRED(jl_dict['deployment_dict'], criticality_data, c,
- params['no_runs'], params['p'], params['algorithm'])
- end = time.time()
- print(f"Average CPU time: {round((end-start)/params['no_runs'], 1)} s")
-
- output_folder = init_folder(model_parameters, c, suffix=f"_GRED_{params['algorithm']}_p{params['p']}")
-
- pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
- pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
+ start = time.time()
+ jl_selected, jl_objective = Main.main_GRED(jl_dict['deployment_dict'], criticality_data, c,
+ params['no_runs'], params['p'], params['algorithm'])
+ end = time.time()
+ print(f"Average CPU time for c={c}: {round((end-start)/params['no_runs'], 1)} s")
- med_run = argsort(jl_objective)[params['no_runs']//2]
- jl_selected_seed = jl_selected[med_run, :]
- jl_objective_seed = jl_objective[med_run]
+ output_folder = init_folder(model_parameters, c, suffix=f"_GRED_{params['algorithm']}_p{params['p']}")
- jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
- retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
- criticality_data, site_positions, c, jl_locations, jl_objective_seed,
- output_folder, benchmarks=False)
+ pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
+ pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
else:
raise ValueError(' This solution method is not available. ')