From de2686f8004811a2432abb311f3a576776af0b39 Mon Sep 17 00:00:00 2001
From: Jonas Paul Brucksch <jonas.brucksch@isea.rwth-aachen.de>
Date: Mon, 26 Sep 2022 10:33:48 +0200
Subject: [PATCH] - adds .lp files to gitignore
- changes according to my Todos
- deletes old "runme_community" file and renames the "runme_community_new" file to "runme_community"
---
.gitignore | 2 +-
Model_Library | 2 +-
runme_community.py | 328 +++++++----------------------------------
runme_community_new.py | 315 ---------------------------------------
4 files changed, 53 insertions(+), 594 deletions(-)
delete mode 100644 runme_community_new.py
diff --git a/.gitignore b/.gitignore
index df67790660..b2525f5410 100644
--- a/.gitignore
+++ b/.gitignore
@@ -22,7 +22,7 @@ var/
*.egg-info/
.installed.cfg
*.egg
-
+.lp
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
diff --git a/Model_Library b/Model_Library
index 58a920d79c..e865185fd9 160000
--- a/Model_Library
+++ b/Model_Library
@@ -1 +1 @@
-Subproject commit 58a920d79c827c5a9da7203ba3ebe95cbac64608
+Subproject commit e865185fd95a787af218873e1f58b23fc9f3f849
diff --git a/runme_community.py b/runme_community.py
index 519e700164..39f1b9ae7b 100644
--- a/runme_community.py
+++ b/runme_community.py
@@ -1,36 +1,16 @@
# runme.py is the central script to execute the optimization of the community.
-"""
-The FEN-Tool is an optimisation tool for prosumer, district, and interconnected city models.
-Copyright (C) 2022. Mauricio Celi Cortés, Jingyu Gong, Jonas van Ouwerkerk, Felix Wege, Nie Yi, Jonas Brucksch
-
-This program is free software; you can redistribute it and/or
-modify it under the terms of the GNU Lesser General Public License
-as published by the Free Software Foundation; either version 3 of
-the License, or (at your option) any later version.
-
-This program is distributed in the hope that it will be useful, but
-WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-Lesser General Public License for more details.
-
-You should have received a copy of the GNU Lesser General Public
-License along with this library; if not, write to the Free Software
-Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
-02110-1301 USA.
-
-Project host: RWTH Aachen University, Aachen, Germany
-Project Website: https://www.fenaachen.net/projekte/fen-ineed-dc
-"""
# Importing the necessary files
import numpy as np
+import Model_Library.District.scripts as scripts
from datetime import timedelta
import math
import time
import pandas as pd
import Model_Library.Prosumer.scripts as scripts
import Model_Library.Prosumer.main as main
+import Model_Library.Prosumer.scripts.extract_inputs as extract_inputs
from functools import partial
from multiprocessing import Pool
from tqdm import tqdm
@@ -39,8 +19,7 @@ import argparse
from Model_Library.Prosumer.scripts.results_evaluation.results_evaluation import Plot_savings
-import Model_Library.Prosumer.main_ca as main_ca
-import Model_Library.District.model as comm_model
+import Model_Library.District.main_district as main_district
def process_each_prosumer(prosumer_name, prosumer_dict, data_source, commentary, no_process_bar_rh):
@@ -109,7 +88,6 @@ def process_each_prosumer(prosumer_name, prosumer_dict, data_source, commentary,
# Calculate number of rolling horizon intervals and loop through them
for t in tqdm(pd.date_range(t_start, t_end - pd.Timedelta(hours=t_rh_shift + 1), freq=str(t_rh_shift) + 'H'), disable=no_process_bar_rh):
- # ToDo: replace first value with perfect value (can be done in runme)
# set end date for current loop
t_end_loop = t + pd.Timedelta(hours=t_rh_horizon)
@@ -181,8 +159,26 @@ if __name__ == "__main__":
'data_path': topology_path[i] + '/data_path.csv',
'rolling_horizon': rolling_horizon[i]}"""
- prosumer_dict={'SCN2_CAT1_PV14_HP_3000_6000_1': {'elec_demand': 3000, 'therm_demand': 6000, 'hot_water_demand': 1500, 'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP', 'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP/config.csv', 'data_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP/data_path.csv', 'rolling_horizon': False},
- 'SCN2_CAT1_PV14_HP_3000_6000_2': {'elec_demand': 1500, 'therm_demand': 6000, 'hot_water_demand': 1500, 'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP', 'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP/config.csv', 'data_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP/data_path.csv', 'rolling_horizon': False}}
+ prosumer_dict={'SCN2_CAT1_PV11_3000_6000': {'elec_demand': 3000, 'therm_demand': 6000, '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',
+ 'rolling_horizon': False},
+ 'SCN2_CAT1_PV12_BA_6000_6000': {'elec_demand': 6000, 'therm_demand': 6000, 'hot_water_demand': 1500,
+ 'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV12_BA',
+ 'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV12_BA/config.csv',
+ 'data_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV12_BA/data_path.csv',
+ 'rolling_horizon': False},
+ 'SCN0_CAT1_6000_6000': {'elec_demand': 6000, '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',
+ 'rolling_horizon': False},
+ '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',
+ 'rolling_horizon': False}}
# PLEASE CHANGE HERE
# Select data source
# Options: '1': import from database, '2': import from local folder
@@ -234,18 +230,25 @@ data_source = 2 # [1]: datasource from data bank; [2]: datasource from local
commentary = False
t_start = pd.Timestamp("2019-07-01 00:00:00")
-t_end = pd.Timestamp("2019-7-10 23:00:00") + pd.Timedelta(hours=1)
+t_end = pd.Timestamp("2019-07-10 23:00:00") + pd.Timedelta(hours=1)
t_step = 1
t_rh_horizon = (t_end - t_start) / pd.Timedelta(hours=1)
t_horizon = (pd.Timestamp(t_end) - pd.Timestamp(t_start)) / np.timedelta64(t_step, 'h')
+"------------------------Communnity Asset---------------------------"
+"-------------------------------------------------------------------"
-topology_path = 'input_files/district_models/jbr_test_ca'
+topology_path = 'input_files/models/district_models/example_CA'
config_path = topology_path + '/config.csv'
data_path = topology_path + '/data_path.csv'
-ca_dict = {'ca_bat': {'topology_path': topology_path, 'config_path': config_path, 'data_path': data_path}}
-ca_dict = {}
+ca_dict = {'ca_bat': {'elec_demand': 0,
+ 'therm_demand': 0,
+ 'hot_water_demand': 0,
+ 'topology_path': topology_path,
+ 'config_path': config_path,
+ 'data_path': data_path}}
+#ca_dict = {}
# PLEASE CHANGE HERE
# Prediction settings
predictions = {'demand_electric': 'SameHourYesterday',
@@ -263,14 +266,8 @@ storage_states = {}
interim_results = {}
final_iteration = False
-# ca_strategy = 'sizing_min_costs'
-# ca_strategy = 'sizing_max_wholesale_profit'
ca_strategy = 'sizing_max_operational_profit'
-comm_strategy_list = ['max_operational_profit']
-
-tax_model = 'no internal taxes' # no internal taxes
-
# create time steps list
time_steps = []
for t in pd.date_range(pd.Timestamp(t_start),
@@ -284,255 +281,32 @@ t_rh_shift = 0
t_current_value_length = (t_end-t_start)/np.timedelta64(t_step, 'h')
t_history = t_horizon/24
-"""---------------------------COMMUNITY-LEVEL----------------------"""
-"""----------------------------------------------------------------"""
-
# initialize community component in the same way prosumers are.
# The difference is that they are not directly optimized
-start_ca_init = time.time()
-comm_assets = main_ca.Main(data_source, ca_dict, t_start, t_end, t_step, predictions, t_current_value_length, t_end,
+comm_assets = main.Main_CA(data_source, ca_dict, t_start, t_end, t_step, predictions, t_current_value_length, t_end,
t_history, commentary, storage_states, t_rh_shift, aggregation=False)
-end_ca_init = time.time()
-topology_path = 'input_files/models/district_models/jbr_test_comm'
+
+"""---------------------------COMMUNITY-LEVEL----------------------"""
+"""----------------------------------------------------------------"""
+
+topology_path = 'input_files/models/district_models/example_community'
config_path = topology_path + '/config.csv'
data_path_comm = topology_path + '/data_path.csv'
comm_dict = {'community': {'topology_path': topology_path, 'config_path': config_path, 'data_path': data_path_comm}}
-# Initialize community
-# injection price: 2019 # changes with size!
-# bis10kwp: 8.16 (01.01.21), bis 40: 7.93
-# (https://www.solaranlagen-portal.com/photovoltaik/wirtschaftlichkeit/einspeiseverguetung)
-# network usage fees: all from Regionetz
-# levy reduction: just 40% of EEG is calculated for self consumed electricity
-# (as in https://www.buendnis-buergerenergie.de/fileadmin/user_upload/2020-03-06_EnergyBrainpool_Impulspapier-Energy-Sharing.pdf)
-# general levies: https://www.netztransparenz.de/
-# consession fees: city of Aachen ( < 500000 people) # (ct/kWh)
-# https://www.acteno.de/ecms/de/energieanwendungen-4-0/konzessionsabgabe.html
-# electrixity tax reduction: assumption from https://www.buendnis-buergerenergie.de/fileadmin/user_upload/2020-03-06_EnergyBrainpool_Impulspapier-Energy-Sharing.pdf
-comm_config = {'injection_price': pd.Series(data=0.0793, index=time_steps),
- 'injection/pvpeak': 0.7,
- 'gas_price': 0.0606, # 2019 https://www.verivox.de/gas/verbraucherpreisindex/
- 'network_usage_capacity_fee_low': 14.79, # in €/kWa if yearly full load hours are less than 2500 h
- 'network_usage_energy_fee_low': 0.0506, # in €/kWh if yearly full load hours are less than 2500 h
- # high tariffs are not used for now.
- # The program does not know if the community has less or more than 2500 full load hours
- 'network_usage_capacity_fee_high': 50.54, # in €/kWa if yearly full load hours are more than 2500 h
- 'network_usage_energy_fee_high': 0.0363, # in €/kWh if yearly full load hours are more than 2500 h
- 'levies_int': 0.0276,
- 'levies_ext': 0.0496, # in ct/kWh (KWK Umlage + Offshore Umlage + AbLV Umlage + StromNEV Umlage + EEG)
- 'concession': 0.0199,
- 'electricity_tax_int': 0,
- 'electricity_tax_ext': 0.0205, # in ct/kWh if not self consumed
- 'VAT': 0.19, # in %
- 'heat_price': 0,
- 'cooling_price': 0,
- 'injection_price_gas': 0,
- 'injection_price_heat': 0,
- 'injection_price_cooling': 0,
- 'elec_emission': 0.401, # 2019
- 'gas_emission': 0.21,
- 'yearly_interest': 0.03, # letmathe
- 'planning_horizon': 20}
-
-comm_data_path_dict = dict.fromkeys('community')
-comm_data_path_dict['community'] = scripts.extract_inputs.get_path(data_path_comm)
-comm_data = scripts.extract_inputs.run(comm_data_path_dict, 'community', 1)
-elec_price = comm_data['elec_price'].iloc[:,0]
-
-comm_config['spot_price'] = elec_price
-comm_config['injection_price'] = elec_price
-
-if tax_model == 'no internal taxes':
- comm_config['elec_price_int_low'] = comm_config['spot_price']
- comm_config['elec_price_int_high'] = comm_config['spot_price']
-
-elif tax_model == 'internal taxes':
- comm_config['elec_price_int_low'] = (comm_config['spot_price'] + comm_config['network_usage_energy_fee_low']
- + comm_config['levies_int'] + comm_config['concession'] + comm_config[
- 'electricity_tax_int']) \
- * (1 + comm_config['VAT'])
- comm_config['elec_price_int_high'] = (comm_config['spot_price'] + comm_config['network_usage_energy_fee_high']
- + comm_config['levies_int'] + comm_config['concession'] + comm_config[
- 'electricity_tax_int']) \
- * (1 + comm_config['VAT'])
-
-else:
- print('No valid tax model')
-
-comm_config['elec_price_ext_low'] = (comm_config['spot_price'] + comm_config['network_usage_energy_fee_low']
- + comm_config['levies_ext'] + comm_config['concession'] + comm_config[
- 'electricity_tax_ext']) \
- * (1 + comm_config['VAT'])
-comm_config['elec_price_ext_high'] = (comm_config['spot_price'] + comm_config['network_usage_energy_fee_high']
- + comm_config['levies_ext'] + comm_config['concession'] + comm_config[
- 'electricity_tax_ext']) \
- * (1 + comm_config['VAT'])
-
-# Create object of BaseCommunity-class
-#ToDo JBR: has to happen in community main
-start_comm_init = time.time()
-
-community = comm_model.BaseComm('TestComm', {'Prosumer': final_prosumer_dict, 'Comm_Comps': comm_assets.comm_assets},
- time_steps, {'Prosumer': final_prosumer_dict.keys(), 'Comm_Comps': comm_assets.comm_assets.keys()},
- t_step, comm_config, ca_strategy)
-end_comm_init = time.time()
-
-"""---------------------ROLLING HORIZON (RH) ------------------------------------------------------------------------"""
-"""-----------------------------------------------------------------------------------------------------------------"""
-
-t_overlap_default = 0 # deafult: 0; nr of timesteps the starting point of each interval is shifted into the prior interval
-t_interval_default = 4*24 # default: 4*24; number of time steps in one interval of the RH
-
-t_start_rh = time_steps[0] # start of
-t_last = 0
-
-t_interval = t_interval_default / t_step
-last_t_interval = t_interval_default / t_step
-
-last_t_overlap = t_overlap_default / t_step
-t_overlap = t_overlap_default / t_step
-
-start_rh = time.time()
-n_change = 0
-
-while t_last * t_step < t_horizon:
- print('-------------------' + str(t_last) + '-----------------')
-
- # PREPARE INTERVAL
- if (t_last + t_interval - t_overlap) * t_step < t_horizon and (t_last + t_interval) * t_step <= t_horizon:
- # interval does not reach overall end
- t_horizon_rh = int(t_interval) # number of time steps in this RH-interval
- t_fix = max(t_horizon_rh - int(t_overlap), 1) # number of fixed time steps of this RH-interval
- elif (t_last + t_interval - t_overlap) * t_step < t_horizon and (t_last + t_interval) * t_step > t_horizon:
- # interval reaches end of horizon, but just without overlap
- t_horizon_rh = int(len(time_steps) - t_last) # interval length is adapted
- t_fix = max(int(t_interval) - int(t_overlap), 1) # but not the number of fixed time steps
- else:
- # fixed time steps hit overall time horizon --> adapt also the fixed time steps
- t_horizon_rh = int(len(time_steps) - t_last)
- t_fix = t_horizon_rh
-
- # extract relevant timesteps values from overall time series
- time_steps_rh = time_steps[t_last:t_last + t_horizon_rh] # whole interval size
- time_steps_fix = time_steps[t_last:t_last + t_fix] # without overlap
-
- # STEP 1 --> SCHEDULE EXTRACTION
- start_schedule = time.time()
- for ps in final_prosumer_dict.values():
- valid = ps.calc_rh_step_schedule(time_steps_rh)
- if not valid:
- n_change += 1
- t_interval = int(math.floor(t_interval / 2))
- if t_interval == 0:
- t_interval = 1
- if t_interval < t_overlap:
- t_overlap = t_interval - 1
- break
-
- if not valid:
- continue
-
- for cc in community.comm_assets.values():
- valid = cc.calc_rh_step_schedule(time_steps_rh)
- if not valid:
- n_change += 1
- t_interval = int(math.floor(t_interval / 2))
- if t_interval == 0:
- t_interval = 1
- if t_interval < t_overlap:
- t_overlap = t_interval - 1
- break
-
- if not valid:
- continue
-
- end_schedule = time.time()
-
- # STEP 2 --> DF IDENTIFICATION
- start_flex_calc = time.time()
- for ps in final_prosumer_dict.values():
- ps.calc_flex(time_steps_rh)
-
- for cc in community.comm_assets.values():
- cc.calc_flex(time_steps_rh)
-
- # aggregate prosumer DFs in community
- community.aggregate_temp(time_steps_rh, final_prosumer_dict, community.comm_assets)
- end_flex_calc = time.time()
-
- # COMMUNITY OPTIMIZATION
- start_opt = time.time()
- valid = community.run_optimization(time_steps_rh, comm_strategy_list, timelimit=200)
-
- if not valid:
- n_change += 1
- t_interval = int(math.floor(t_interval / 2))
- if t_interval == 0:
- t_interval = 1
- if t_interval < t_overlap:
- t_overlap = t_interval - 1
- continue
- end_opt = time.time()
-
- # VALIDATION
- start_validation = time.time()
- valid = community.validate_result(time_steps_fix)
- if valid:
- # fix results...
- for ps in final_prosumer_dict.values():
- ps.fix_results(time_steps_fix)
- for cc in community.comm_assets.values():
- cc.fix_results(time_steps_fix)
- community.aggregate_fix(time_steps_fix)
-
- # ...and go to next interval
- t_last = int(t_last + max(t_interval - t_overlap, 1))
- t_interval = t_interval_default * 1 / t_step
- t_overlap = t_overlap_default * 1 / t_step
-
- elif not valid:
- # nr of interval changes
- n_change += 1
- t_interval = int(math.floor(t_interval / 2))
- if t_interval == 0:
- t_interval = 1
- if t_interval < t_overlap:
- t_overlap = t_interval - 1
-
- if not valid and t_interval < 1:
- print('Unteres Limit erreicht')
- end_validation = time.time()
-
-end_rh = time.time()
+comm_strategy = ['max_operational_profit']
+
+community_main = main_district.MainDistrict(final_prosumer_dict,
+ comm_assets,
+ time_steps,
+ t_step,
+ comm_dict,
+ ca_strategy,
+ comm_strategy,
+ t_horizon)
# ------------POST-PROCESS-----------------
-start_analysis = time.time()
-community.analyze_results_new()
-# community.plot_grid_exchange()
-end_analysis = time.time()
-
-start_write = time.time()
-folder_name = 'EC_' + str(len(final_prosumer_dict)) + '_' + str(t_horizon / 24) + '_' + str(t_interval_default) + '_' + str(t_overlap_default)
-flag = True
-iter = 0
-while flag:
- #ToDo JBR: apply Felix changes
- try:
- os.mkdir('output_files/' + folder_name)
- flag = False
- except FileExistsError:
- iter += 1
- folder_name = folder_name + '_' + str(iter)
-#ToDO jbr: fix this later when the new main is working
-"""
-prosumers.show_results_agents(final_prosumer_dict.keys(), folder_name)
-if comm_assets:
- comm_assets.show_results_agents(comm_assets.comm_assets.keys(), folder_name)
-#Todo JBR: change to community main
-prosumers.export_comm(community, time_steps, folder_name)
-"""
-end_write = time.time()
-end_total = time.time()
+
diff --git a/runme_community_new.py b/runme_community_new.py
deleted file mode 100644
index cb2fea46b0..0000000000
--- a/runme_community_new.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# runme.py is the central script to execute the optimization of the community.
-
-# Importing the necessary files
-
-import numpy as np
-import Model_Library.District.scripts as scripts
-from datetime import timedelta
-import math
-import time
-import pandas as pd
-import Model_Library.Prosumer.scripts as scripts
-import Model_Library.Prosumer.main as main
-import Model_Library.Prosumer.scripts.extract_inputs as extract_inputs
-from functools import partial
-from multiprocessing import Pool
-from tqdm import tqdm
-import os
-import argparse
-
-from Model_Library.Prosumer.scripts.results_evaluation.results_evaluation import Plot_savings
-
-import Model_Library.Prosumer.main_ca as main_ca
-import Model_Library.District.main_district as main_district
-
-
-def process_each_prosumer(prosumer_name, prosumer_dict, data_source, commentary, no_process_bar_rh):
- try:
- # PLEASE CHANGE HERE
- # Set the simulation time frame and optional rolling horizon configurations:
- # 't_start': start date of simulations, Unit: JJJJ-MM-DD hh:mm:ss
- # 't_end': end date of simulations, Unit: JJJJ-MM-DD hh:mm:ss
- # 't_step': granularity of optimization model, Unit: hours
- # Rolling horizon (RH) can be set by:
- # 't_rh_horizon': width of rolling horizon intervals, Unit: hours, MINIMUM VALUE: 2 !!!
- # 't_rh_shift': rolling horizon shift between intervals, Unit: hours
- # 't_current_value_length': number of values at beginning of rolling horizon interval that are replaced by real values, Unit: hours
- # 't_history': number of days before actual simulation interval for the demand generator to be able to make required predictions
- if prosumer_dict[prosumer_name]['rolling_horizon']:
- t_start = pd.Timestamp("2019-09-01 00:00:00")
- t_end = pd.Timestamp("2019-09-01 5:00:00")
- t_step = 1
- t_rh_horizon = 3
- t_rh_shift = 1
- t_current_value_length = 2
- t_history = 14 # days
-
- # PLEASE CHANGE HERE
- # Prediction settings
- predictions = {'demand_electric': 'SameHourYesterday',
- 'demand_heat': 'SameHourYesterday',
- 'day_ahead_price': 'SameHourYesterday',
- 'intraday_price': 'SameHourYesterday',
- 'solar_radiation': 'Perfect',
- # currently the method generate_g_t_series takes the same t_start as the prediction -> no historical
- # data for the prediction available: easy fix would be to set a minus time delta in the t_start
- # argument of generate_g_t_series
- 'temperature': 'SameHourYesterday'}
- else:
- t_start = pd.Timestamp("2019-07-01 00:00:00")
- t_end = pd.Timestamp("2019-7-10 23:00:00") + pd.Timedelta(hours=1)
- t_step = 1
- t_rh_horizon = (t_end - t_start) / pd.Timedelta(hours=1)
- t_rh_shift = t_rh_horizon - 1
- t_current_value_length = t_rh_horizon
- t_history = 0 # days
-
- # PLEASE CHANGE HERE
- # Prediction settings
- predictions = {'demand_electric': 'Perfect',
- 'demand_heat': 'Perfect',
- 'day_ahead_price': 'Perfect',
- 'intraday_price': 'Perfect',
- 'solar_radiation': 'Perfect',
- # currently the method generate_g_t_series takes the same t_start as the prediction -> no historical
- # data for the prediction available: easy fix would be to set a minus time delta in the t_start
- # argument of generate_g_t_series
- 'temperature': 'Perfect'}
-
- # Fixed variables - DO NOT CHANGE
- storage_states = {}
- interim_results = {}
- final_iteration = False
-
- # Set aggregation options
- parser = argparse.ArgumentParser(description='Start optimization from DB or local data')
- parser.add_argument('-a', '--aggregate', action="store_true", dest="aggregate",
- help="activating aggregation of input time series", default=False)
- options = parser.parse_args()
-
- # Calculate number of rolling horizon intervals and loop through them
- for t in tqdm(pd.date_range(t_start, t_end - pd.Timedelta(hours=t_rh_shift + 1), freq=str(t_rh_shift) + 'H'), disable=no_process_bar_rh):
- # ToDo: replace first value with perfect value (can be done in runme)
- # set end date for current loop
- t_end_loop = t + pd.Timedelta(hours=t_rh_horizon)
-
- # exceptions that occur at global end of simulation horizon
- if t_end_loop > t_end:
- t_end_loop = t_end
- if t_current_value_length > (t_end_loop - t) / pd.Timedelta(hours=1):
- t_current_value_length = (t_end_loop - t) / pd.Timedelta(hours=1)
-
- # Set flag for final iteration
- if t == t_end - pd.Timedelta(hours=t_rh_shift + 1):
- final_iteration = True
-
- # Start main programme
- prosumer = main.Main(data_source, {prosumer_name: prosumer_dict[prosumer_name]}, t, t_end_loop, t_step,
- predictions, t_current_value_length, t_end,
- t_history, commentary, storage_states, t_rh_shift, aggregation=options.aggregate)
-
- # Run optimization
- prosumer.run_optimization(prosumer.prosumer_name_list)
-
- # Show results - Results are only plotted after last iteration of rolling horizon
- prosumer.show_results(prosumer.prosumer_name_list, interim_results, final_iteration)
-
- # Get storage states from this iteration
- storage_states = prosumer.charge_status
-
- # Get interim results of current rolling horizon interval
- interim_results = prosumer.interim_results
-
- return prosumer.prosumer[prosumer_name]
- except ValueError:
- print(prosumer_name+" could not be optimized!")
-
-# # MAIN PROGRAM -------------------------------------------------------------------------------------------------------
-if __name__ == "__main__":
- # Initialization scenario path and global variables for the prosumer optimization
- # Start timer
- start = time.time()
-
- # PLEASE CHANGE HERE
- # Path to local data - this is only used when selecting local mode
- # 'topology_path': path to matrices that define the prosumer topology
- # 'config_path': path to global configurations like prices, injection prices, emission costs, etc.
- #topology_path = 'input_files/models/Study_Base'
- #config_path = topology_path + '/config.csv'
- #data_path = topology_path + '/data_path.csv'
- #prosumer_name = 'office'
- #prosumer_dict = {prosumer_name: {'topology_path': topology_path, 'config_path': config_path, 'data_path': data_path}}
- topology_path = ['input_files/models/prosumer_models/SCN0_CAT1']
- prosumer_name = ['SCN0_CAT1', 'SCN0_CAT1']
- rolling_horizon = [False]
- elec_demand = [1500, 10000]
- therm_demand = [5000, 20000]
- hot_water_demand = 1500#[1500, 1500]
- step_elec_demand = 500
- step_therm_demand = 500
- #step_hot_water_demand = 0
- prosumer_dict = {}
- """ for i in range(len(prosumer_name)):
- for j in range(elec_demand[0], elec_demand[1], step_elec_demand):
- for k in range(therm_demand[0], therm_demand[1], step_therm_demand):
- #for l in range(hot_water_demand[0], hot_water_demand[1], step_hot_water_demand):
- prosumer_dict[prosumer_name[i]+'_'+str(j)+'_'+str(k)] = {'elec_demand': j,
- 'therm_demand': k,
- 'hot_water_demand': hot_water_demand,
- 'topology_path': topology_path[i],
- 'config_path': topology_path[i] + '/config.csv',
- 'data_path': topology_path[i] + '/data_path.csv',
- 'rolling_horizon': rolling_horizon[i]}"""
-
- prosumer_dict={'SCN2_CAT1_PV11_3000_6000': {'elec_demand': 3000, 'therm_demand': 6000, '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',
- 'rolling_horizon': False},
- 'SCN2_CAT1_PV12_BA_6000_6000': {'elec_demand': 6000, 'therm_demand': 6000, 'hot_water_demand': 1500,
- 'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV12_BA',
- 'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV12_BA/config.csv',
- 'data_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV12_BA/data_path.csv',
- 'rolling_horizon': False},
- 'SCN0_CAT1_6000_6000': {'elec_demand': 6000, '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',
- 'rolling_horizon': False},
- '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',
- 'rolling_horizon': False}}
- # PLEASE CHANGE HERE
- # Select data source
- # Options: '1': import from database, '2': import from local folder
- data_source = 2
- reference_results = {}
- commentary = False
- no_process_bar_rh = True
- parallel_processing = False
-
- # Timer output
- tic = time.time()
- # Start program
- # 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, prosumer_dict=prosumer_dict, data_source=data_source, commentary=commentary, no_process_bar_rh=no_process_bar_rh)
- mapped_values = list(tqdm(pool.map(parallel_func, list(prosumer_dict.keys())), 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_name, prosumer_dict=prosumer_dict, data_source=data_source, commentary=commentary, no_process_bar_rh=no_process_bar_rh)
-
- # Timer output
- toc = time.time()
-
- if reference_results:
- for topology in prosumer_name:
- Plot_savings(reference_results, topology)
-
- # Timer output
- end = time.time()
- # Additional console output
- if commentary:
- print("============ Execution Times =============")
- print("Pre-processing [s]: \t" + str(tic - start))
- #print("(Interaction with database [s]:\t" + str(prosumer.connect_with_db) + ")")
- print("Optimization [s]: \t" + str(toc - tic))
- print("Post-processing [s]: \t" + str(end - toc))
- print("----------------------------------------")
- print("Total [s]: \t" + str((end - toc) + (toc - tic) + (tic - start)))
- print("==========================================")
-
-'-----------------COMMUNITY-PART----------------------------------'
-start_total = time.time()
-
-data_source = 2 # [1]: datasource from data bank; [2]: datasource from local
-commentary = False
-
-t_start = pd.Timestamp("2019-07-01 00:00:00")
-t_end = pd.Timestamp("2019-07-10 23:00:00") + pd.Timedelta(hours=1)
-t_step = 1
-t_rh_horizon = (t_end - t_start) / pd.Timedelta(hours=1)
-
-t_horizon = (pd.Timestamp(t_end) - pd.Timestamp(t_start)) / np.timedelta64(t_step, 'h')
-
-"------------------------Communnity Asset---------------------------"
-"-------------------------------------------------------------------"
-
-topology_path = 'input_files/models/district_models/example_CA'
-config_path = topology_path + '/config.csv'
-data_path = topology_path + '/data_path.csv'
-ca_dict = {'ca_bat': {'elec_demand': 0,
- 'therm_demand': 0,
- 'hot_water_demand': 0,
- 'topology_path': topology_path,
- 'config_path': config_path,
- 'data_path': data_path}}
-#ca_dict = {}
-# PLEASE CHANGE HERE
-# Prediction settings
-predictions = {'demand_electric': 'SameHourYesterday',
- 'demand_heat': 'SameHourYesterday',
- 'day_ahead_price': 'SameHourYesterday',
- 'intraday_price': 'SameHourYesterday',
- 'solar_radiation': 'Perfect',
- # currently the method generate_g_t_series takes the same t_start as the prediction -> no historical
- # data for the prediction available: easy fix would be to set a minus time delta in the t_start
- # argument of generate_g_t_series
- 'temperature': 'SameHourYesterday'}
-
-# Fixed variables - DO NOT CHANGE
-storage_states = {}
-interim_results = {}
-final_iteration = False
-
-ca_strategy = 'sizing_max_operational_profit'
-
-# create time steps list
-time_steps = []
-for t in pd.date_range(pd.Timestamp(t_start),
- pd.Timestamp(t_start) + timedelta(hours=t_horizon) - timedelta(hours=t_step),
- freq=str(t_step)+'H'):
- time_steps.append(t)
-
-t_start = pd.Timestamp(t_start)
-t_end = pd.Timestamp(t_start) + timedelta(hours=t_horizon) - timedelta(hours=t_step)
-t_rh_shift = 0
-t_current_value_length = (t_end-t_start)/np.timedelta64(t_step, 'h')
-t_history = t_horizon/24
-
-# initialize community component in the same way prosumers are.
-# The difference is that they are not directly optimized
-comm_assets = main.Main_CA(data_source, ca_dict, t_start, t_end, t_step, predictions, t_current_value_length, t_end,
- t_history, commentary, storage_states, t_rh_shift, aggregation=False)
-
-
-"""---------------------------COMMUNITY-LEVEL----------------------"""
-"""----------------------------------------------------------------"""
-
-topology_path = 'input_files/models/district_models/example_community'
-config_path = topology_path + '/config.csv'
-data_path_comm = topology_path + '/data_path.csv'
-comm_dict = {'community': {'topology_path': topology_path, 'config_path': config_path, 'data_path': data_path_comm}}
-comm_strategy = ['max_operational_profit']
-
-community_main = main_district.MainDistrict(final_prosumer_dict,
- comm_assets,
- time_steps,
- t_step,
- comm_dict,
- ca_strategy,
- comm_strategy,
- t_horizon)
-
-# ------------POST-PROCESS-----------------
-
-
-
-
-
--
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