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Commit afe4cd18 authored by Christoph von Oy's avatar Christoph von Oy
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Implemented input profile processor

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Model_Library @ 45ee9ad7
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Subproject commit 7fc1f3718bd483913c76c1f2922dcd1415cfbff0
Subproject commit 45ee9ad75756f37ecddd11dee4902c97bb590bbb
Tooling @ d7e201c3
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Subproject commit d7e201c3af6d28b04300dbf3c6fca4900852dc10
runme_community.py
+ 49
− 11
View file @ afe4cd18
import time
import pandas as pd
import Tooling.input_profile_processor.input_profile_processor
import Model_Library.Prosumer.main as main
import Model_Library.District.main_district as main_district
from functools import partial
......@@ -7,9 +8,9 @@ from multiprocessing import Pool
from tqdm import tqdm
import os
def process_each_prosumer(prosumer_name, prosumer_specification, t_start, t_horizon, t_step, prosumer_strategy):
def process_each_prosumer(prosumer_name, prosumer_specification, t_start, t_horizon, t_step, prosumer_strategy, input_profiles):
before_setup = time.time()
prosumer = main.Main(prosumer_name, prosumer_specification, t_start, t_horizon, t_step)
prosumer = main.Main(prosumer_name, prosumer_specification, t_start, t_horizon, t_step, input_profiles)
after_setup = time.time()
print("process_each_prosumer:\tProsumer Construction [s]: \t" + str(after_setup - before_setup))
......@@ -26,6 +27,27 @@ t_start = pd.Timestamp("2019-05-10 00:00:00") # start time of simulation
t_horizon = 240 # number of time steps to be simulated
t_step = 1 # length of a time step in hours
input_profile_dict = {'irradiance_1': ['irradiance', 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'],
'temperature_1': ['air_temperature', 'input_files/data/temperature/temperature.csv'],
'demand_electric_1': ['elec_demand', 'generate', 3000],
'demand_heat_1': ['therm_demand', 'generate', 6000, 'temperature_1'],
'demand_hot_water_1': ['hot_water_demand', 'generate', 1500, 'temperature_1'],
'irradiance_2': ['irradiance', 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'],
'temperature_2': ['air_temperature', 'input_files/data/temperature/temperature.csv'],
'demand_electric_2': ['elec_demand', 'generate', 3000],
'demand_heat_2': ['therm_demand', 'generate', 6000, 'temperature_2'],
'demand_hot_water_2': ['hot_water_demand', 'generate', 1500, 'temperature_2'],
'irradiance_3': ['irradiance', 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'],
'temperature_3': ['air_temperature', 'input_files/data/temperature/temperature.csv'],
'demand_electric_3': ['elec_demand', 'generate', 0],
'demand_heat_3': ['therm_demand', 'generate', 0, 'temperature_3'],
'demand_hot_water_3': ['hot_water_demand', 'generate', 0, 'temperature_3'],
'elec_price_1': ['elec_price', 'input_files/data/prices/day-ahead/hourly_price.csv']}
input_profiles = Tooling.input_profile_processor.input_profile_processor.process_input_profiles(input_profile_dict, t_start, t_horizon, t_step)
after_input_processing = time.time()
print("runme:\t\t\tProfile Processing [s]: \t" + str(after_input_processing - start))
# 'data_path': path to file specifying where input profiles are located
# 'topology_path': path to matrices that define the prosumer topology
# 'config_path': path to global configurations like prices, injection prices, emission costs, etc.
......@@ -34,30 +56,40 @@ prosumer_dict = {'SCN2_CAT1_PV11_3000_6000':{'elec_demand': 3000,
'hot_water_demand': 1500,
'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV11',
'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV11/config.csv',
'data_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV11/data_path.csv'},
'data_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV11/data_path.csv',
'profiles': {'irradiance': 'irradiance_1',
'air_temperature': 'temperature_1',
'elec_demand': 'demand_electric_1',
'therm_demand': 'demand_heat_1',
'hot_water_demand': 'demand_hot_water_1'}},
'SCN0_CAT1_3000_6000': {'elec_demand': 3000,
'therm_demand': 6000,
'hot_water_demand': 1500,
'topology_path': 'input_files/models/prosumer_models/SCN0_CAT1',
'config_path': 'input_files/models/prosumer_models/SCN0_CAT1/config.csv',
'data_path': 'input_files/models/prosumer_models/SCN0_CAT1/data_path.csv'}}
'data_path': 'input_files/models/prosumer_models/SCN0_CAT1/data_path.csv',
'profiles': {'irradiance': 'irradiance_2',
'air_temperature': 'temperature_2',
'elec_demand': 'demand_electric_2',
'therm_demand': 'demand_heat_2',
'hot_water_demand': 'demand_hot_water_2'}}}
ps_strategy = ['annuity']
parallel_processing = False
before_optimization = time.time()
print("runme:\t\t\tProsumer Setup [s]: \t\t" + str(before_optimization - start))
print("runme:\t\t\tProsumer Setup [s]: \t\t" + str(before_optimization - after_input_processing))
# Run multiple independent prosumers in parallel on multiple cores
final_prosumer_dict = dict.fromkeys(prosumer_dict.keys())
if parallel_processing:
count_processes = len(prosumer_dict.keys())
pool = Pool(os.cpu_count())
parallel_func = partial(process_each_prosumer, t_start = t_start, t_horizon = t_horizon, t_step = t_step, prosumer_strategy = ps_strategy)
parallel_func = partial(process_each_prosumer, t_start = t_start, t_horizon = t_horizon, t_step = t_step, prosumer_strategy = ps_strategy, input_profiles = input_profiles)
mapped_values = list(tqdm(pool.map(parallel_func, list(prosumer_dict.keys()), list(prosumer_dict.values())), total = count_processes))
# Normal processing, one core only
else:
for prosumer_name in list(prosumer_dict.keys()):
final_prosumer_dict[prosumer_name] = process_each_prosumer(prosumer_name, prosumer_dict[prosumer_name], t_start, t_horizon, t_step, ps_strategy)
final_prosumer_dict[prosumer_name] = process_each_prosumer(prosumer_name, prosumer_dict[prosumer_name], t_start, t_horizon, t_step, ps_strategy, input_profiles)
after_optimization = time.time()
print("runme:\t\t\tProsumer Optimization [s]: \t" + str(after_optimization - before_optimization))
......@@ -68,24 +100,30 @@ ca_dict = {'ca_bat': {'elec_demand': 0,
'hot_water_demand': 0,
'topology_path': 'input_files/models/district_models/example_CA',
'config_path': 'input_files/models/district_models/example_CA/config.csv',
'data_path': 'input_files/models/district_models/example_CA/data_path.csv'}}
'data_path': 'input_files/models/district_models/example_CA/data_path.csv',
'profiles': {'irradiance': 'irradiance_3',
'air_temperature': 'temperature_3',
'elec_demand': 'demand_electric_3',
'therm_demand': 'demand_heat_3',
'hot_water_demand': 'demand_hot_water_3'}}}
ca_strategy = 'sizing_max_operational_profit'
before_community_assets = time.time()
print("runme:\t\t\tCommunity Assets Setup [s]: \t" + str(before_community_assets - start_community))
comm_assets = main.Main_CA(ca_dict, t_start, t_horizon, t_step)
comm_assets = main.Main_CA(ca_dict, t_start, t_horizon, t_step, input_profiles)
after_community_assets = time.time()
print("runme:\t\t\tCommunity Assets Constr. [s]: \t" + str(after_community_assets - before_community_assets))
comm_dict = {'community': {'topology_path': 'input_files/models/district_models/example_community',
'config_path': 'input_files/models/district_models/example_community/config.csv',
'data_path': 'input_files/models/district_models/example_community/data_path.csv'}}
'data_path': 'input_files/models/district_models/example_community/data_path.csv',
'profiles': {'elec_price': 'elec_price_1'}}}
comm_strategy = ['max_operational_profit']
before_community_optimization = time.time()
print("runme:\t\t\tComm. Optimization Setup [s]: \t" + str(before_community_optimization - after_community_assets))
community_main = main_district.MainDistrict(final_prosumer_dict, comm_assets, t_start, t_horizon, t_step, comm_dict, ca_strategy, comm_strategy)
community_main = main_district.MainDistrict(final_prosumer_dict, comm_assets, t_start, t_horizon, t_step, comm_dict, ca_strategy, comm_strategy, input_profiles)
after_community_optimization = time.time()
print("runme:\t\t\tCommunity Optimization [s]: \t" + str(after_community_optimization - before_community_optimization))
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