Skip to content
Snippets Groups Projects
Commit 274319f8 authored by jbr-lpe's avatar jbr-lpe
Browse files

input data was added and Model_Library was committed

parent a8781f30
No related branches found
No related tags found
No related merge requests found
Subproject commit 8a7c698a1617f703da046815c832fb3a928a04e7 Subproject commit 63548cade582a3b21c7f0e120142c6bd5f0ed299
This diff is collapsed.
timestamp,day_ahead_price timestamp,day_ahead_price
01-01-2019 00:00:00,0.4188 01-01-2019 00:00:00,0.3188
01-01-2019 01:00:00,0.386 01-01-2019 01:00:00,0.486
01-01-2019 02:00:00,0.3655 01-01-2019 02:00:00,0.2655
01-01-2019 03:00:00,0.3232 01-01-2019 03:00:00,0.2232
01-01-2019 04:00:00,0.3085 01-01-2019 04:00:00,0.2085
01-01-2019 05:00:00,0.3014 01-01-2019 05:00:00,0.2014
01-01-2019 06:00:00,0.3017 01-01-2019 06:00:00,0.2017
01-01-2019 07:00:00,0.30 01-01-2019 07:00:00,0.20
01-01-2019 08:00:00,0.3065 01-01-2019 08:00:00,0.2065
01-01-2019 09:00:00,0.3065 01-01-2019 09:00:00,0.2065
01-01-2019 10:00:00,0.3027 01-01-2019 10:00:00,0.2027
01-01-2019 11:00:00,0.3034 01-01-2019 11:00:00,0.2034
01-01-2019 12:00:00,0.3099 01-01-2019 12:00:00,0.2099
01-01-2019 13:00:00,0.3004 01-01-2019 13:00:00,0.2004
01-01-2019 14:00:00,0.3075 01-01-2019 14:00:00,0.2075
01-01-2019 15:00:00,0.3211 01-01-2019 15:00:00,0.2211
01-01-2019 16:00:00,0.3598 01-01-2019 16:00:00,0.2598
01-01-2019 17:00:00,0.404 01-01-2019 17:00:00,0.304
01-01-2019 18:00:00,0.4405 01-01-2019 18:00:00,0.3405
01-01-2019 19:00:00,0.4315 01-01-2019 19:00:00,0.3315
01-01-2019 20:00:00,0.4345 01-01-2019 20:00:00,0.3345
01-01-2019 21:00:00,0.4068 01-01-2019 21:00:00,0.3068
01-01-2019 22:00:00,0.4027 01-01-2019 22:00:00,0.3027
01-01-2019 23:00:00,0.3485 01-01-2019 23:00:00,0.2485
02-01-2019 00:00:00,0.354 02-01-2019 00:00:00,0.254
02-01-2019 01:00:00,0.3198 02-01-2019 01:00:00,0.2198
02-01-2019 02:00:00,0.305 02-01-2019 02:00:00,0.205
02-01-2019 03:00:00,0.2879 02-01-2019 03:00:00,0.1879
02-01-2019 04:00:00,0.2842 02-01-2019 04:00:00,0.1842
02-01-2019 05:00:00,0.2875 02-01-2019 05:00:00,0.1875
02-01-2019 06:00:00,0.3416 02-01-2019 06:00:00,0.2416
02-01-2019 07:00:00,0.4207 02-01-2019 07:00:00,0.3207
02-01-2019 08:00:00,0.4489 02-01-2019 08:00:00,0.3489
02-01-2019 09:00:00,0.4526 02-01-2019 09:00:00,0.3526
02-01-2019 10:00:00,0.4557 02-01-2019 10:00:00,0.3557
02-01-2019 11:00:00,0.4509 02-01-2019 11:00:00,0.3509
02-01-2019 12:00:00,0.4516 02-01-2019 12:00:00,0.3516
02-01-2019 13:00:00,0.449 02-01-2019 13:00:00,0.349
02-01-2019 14:00:00,0.4406 02-01-2019 14:00:00,0.3406
02-01-2019 15:00:00,0.4484 02-01-2019 15:00:00,0.3484
02-01-2019 16:00:00,0.444 02-01-2019 16:00:00,0.344
02-01-2019 17:00:00,0.4605 02-01-2019 17:00:00,0.3605
02-01-2019 18:00:00,0.4672 02-01-2019 18:00:00,0.4672
02-01-2019 19:00:00,0.4526 02-01-2019 19:00:00,0.4526
02-01-2019 20:00:00,0.3932 02-01-2019 20:00:00,0.3932
......
...@@ -30,13 +30,14 @@ import Model_Library.District.main as main_district ...@@ -30,13 +30,14 @@ import Model_Library.District.main as main_district
from enum import Enum from enum import Enum
import json import json
class SimulationScope(Enum): class SimulationScope(Enum):
PROSUMER = 1 PROSUMER = 1
DISTRICT = 2 DISTRICT = 2
simulation_scope = SimulationScope.PROSUMER simulation_scope = SimulationScope.PROSUMER
t_start = pd.Timestamp("2019-05-10 00:00:00") # start time of simulation t_start = pd.Timestamp("2019-05-10 00:00:00") # start time of simulation
global_dynamic = GlobalDynamic([3600 for i in range(3)]) global_dynamic = GlobalDynamic([3600 for i in range(20)])
dynamic = global_dynamic.root() dynamic = global_dynamic.root()
input_profile_dict = {'irradiance_1': {'type': 'irradiance', 'file': 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'}, input_profile_dict = {'irradiance_1': {'type': 'irradiance', 'file': 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'},
...@@ -56,7 +57,8 @@ input_profile_dict = {'irradiance_1': {'type': 'irradiance', 'file': 'input_file ...@@ -56,7 +57,8 @@ input_profile_dict = {'irradiance_1': {'type': 'irradiance', 'file': 'input_file
'demand_hot_water_3': {'type': 'hot_water_demand', 'generate': {'yearly_demand': 0, 'temperature': 'temperature_3'}}, 'demand_hot_water_3': {'type': 'hot_water_demand', 'generate': {'yearly_demand': 0, 'temperature': 'temperature_3'}},
'elec_price_1': {'type': 'elec_price', 'file': 'input_files/data/prices/day-ahead/hourly_price.csv'}, 'elec_price_1': {'type': 'elec_price', 'file': 'input_files/data/prices/day-ahead/hourly_price.csv'},
'market_buy_price_1': {'type': 'elec_price', 'file': 'input_files/data/market-prices/market_buy_prices.csv'}, 'market_buy_price_1': {'type': 'elec_price', 'file': 'input_files/data/market-prices/market_buy_prices.csv'},
'market_sell_price_1': {'type': 'injection_price', 'file': 'input_files/data/market-prices/market_sell_prices.csv'}} 'market_sell_price_1': {'type': 'injection_price', 'file': 'input_files/data/market-prices/market_sell_prices.csv'},
'power_factors_1': {'type': 'power_factors', 'file': 'input_files/data/irradiance/pv_factors_altertheim_sarah_2023.csv'}}
input_profiles = Tooling.input_profile_processor.input_profile_processor.process_input_profiles(input_profile_dict, t_start, dynamic) input_profiles = Tooling.input_profile_processor.input_profile_processor.process_input_profiles(input_profile_dict, t_start, dynamic)
...@@ -69,7 +71,7 @@ prosumer_profiles = {'SCN2_CAT1_PV11_3000_6000': {'pv_roof': {'irradiance': 'irr ...@@ -69,7 +71,7 @@ prosumer_profiles = {'SCN2_CAT1_PV11_3000_6000': {'pv_roof': {'irradiance': 'irr
'jbr_lpe_test_3000_6000': {'pv_roof': {'irradiance': 'irradiance_1', 'temperature': 'temperature_1'}, 'jbr_lpe_test_3000_6000': {'pv_roof': {'irradiance': 'irradiance_1', 'temperature': 'temperature_1'},
'elec_cns': {'consumption': 'demand_electric_1'}, 'elec_cns': {'consumption': 'demand_electric_1'},
'market_1': {'elec_price': 'market_buy_price_1', 'injection_price': 'market_sell_price_1'}, 'market_1': {'elec_price': 'market_buy_price_1', 'injection_price': 'market_sell_price_1'},
'market_2': {'elec_price': 'market_buy_price_1'}}} 'market_2': {'elec_price': 0, 'injection_price': 0.9, 'power_factors': 'power_factors_1'}}}
prosumer_dict = dict() prosumer_dict = dict()
for prosumer_name, prosumer_path in prosumer_paths.items(): for prosumer_name, prosumer_path in prosumer_paths.items():
with open(prosumer_path) as f: with open(prosumer_path) as f:
......
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment