From 46ad39711404c59e6dbaa4028d451b5fb7dabc17 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Wed, 4 Oct 2023 10:56:00 +0200 Subject: [PATCH 01/37] add example for tsam use --- examples/tsam/basic_tsam_example.py | 274 ++++++++++++++++++++++++++++ 1 file changed, 274 insertions(+) create mode 100644 examples/tsam/basic_tsam_example.py diff --git a/examples/tsam/basic_tsam_example.py b/examples/tsam/basic_tsam_example.py new file mode 100644 index 000000000..b84c29c51 --- /dev/null +++ b/examples/tsam/basic_tsam_example.py @@ -0,0 +1,274 @@ +# -*- coding: utf-8 -*- + +""" +General description +------------------- + +A basic example to show how to model a simple energy system with oemof.solph. + +The following energy system is modeled: + +.. code-block:: text + + input/output bgas bel + | | | + | | | + wind(FixedSource) |------------------>| + | | | + pv(FixedSource) |------------------>| + | | | + rgas(Commodity) |--------->| | + | | | + demand(Sink) |<------------------| + | | | + | | | + pp_gas(Transformer) |<---------| | + |------------------>| + | | | + storage(Storage) |<------------------| + |------------------>| + + +Data +---- +basic_example.csv + + +Installation requirements +------------------------- +This example requires oemof.solph (v0.5.x), install by: + + pip install oemof.solph[examples] + +License +------- +`MIT license `_ +""" +########################################################################### +# imports +########################################################################### + +import logging +import os +import pprint as pp +import warnings +from datetime import datetime + +import matplotlib.pyplot as plt +import pandas as pd +from oemof.tools import logger + +from oemof.solph import EnergySystem +from oemof.solph import Model +from oemof.solph import buses +from oemof.solph import components as cmp +from oemof.solph import create_time_index +from oemof.solph import flows +from oemof.solph import helpers +from oemof.solph import processing +from oemof.solph import views +from oemof.solph._helpers import aggregate_time_series +from oemof.solph._helpers import plotTS +from oemof.solph._helpers import determine_aggregation_parameters +def main(number_of_typical_periods:int): + # ************************************************************************* + # ********** PART 1 - Define and optimise the energy system *************** + # ************************************************************************* + + # Read data file + + filename = os.path.join(os.getcwd(), "basic_example.csv") + try: + data = pd.read_csv(filename) + except FileNotFoundError: + msg = "Data file not found: {0}. Only one value used!" + warnings.warn(msg.format(filename), UserWarning) + data = pd.DataFrame({"pv": [0.3], "wind": [0.6], "demand_el": [500]}) + + # First there need to be a pre-processing, because our aggregate_time_series function only + # works with timeseries. + df = pd.DataFrame(data) + start_time = pd.Timestamp.now().replace(year=2023, month=1, day=1,hour=0, minute=0, second=0, microsecond=0) + df["datetime"] = start_time + pd.to_timedelta(df["timestep"] - 1, unit="H") + df.set_index("datetime", inplace=True) + + data_dict_to_aggregate = {"wind" : {"timeseries" : df["wind"], "weighted_factor" : 1}, + "pv" : {"timeseries" : df["pv"], "weighted_factor" : 1}, + "demand_el" : {"timeseries" : df["demand_el"], "weighted_factor" : 1}, + } + + number_of_time_steps_per_periods =24 + number_of_segments_per_period = 24*4 + aggregation_ratio = ( number_of_typical_periods * number_of_time_steps_per_periods ) / 8760 + segmentation = False + if False: + determine_aggregation_parameters(data_dict_to_aggregate = data_dict_to_aggregate, + number_of_time_steps_per_periods = number_of_time_steps_per_periods, + segmentation = False) + if segmentation: + data_dict_aggregated, objective_weighting, clusterClass = aggregate_time_series(data_dict_to_aggregate = data_dict_to_aggregate, + number_of_typical_periods = number_of_typical_periods, + number_of_time_steps_per_periods = number_of_time_steps_per_periods, + number_of_segments_per_period= number_of_segments_per_period, + segmentation = segmentation + ) + else: + data_dict_aggregated, objective_weighting, clusterClass = aggregate_time_series(data_dict_to_aggregate = data_dict_to_aggregate, + number_of_typical_periods = number_of_typical_periods, + number_of_time_steps_per_periods = number_of_time_steps_per_periods, + segmentation = segmentation + ) + + solver = "cbc" # 'glpk', 'gurobi',.... + debug = False # Set number_of_timesteps to 3 to get a readable lp-file. + solver_verbose = False # show/hide solver output + + # initiate the logger (see the API docs for more information) + logger.define_logging( + logfile="oemof_example.log", + screen_level=logging.INFO, + file_level=logging.INFO, + ) + + logging.info("Initialize the energy system") + date_time_index = data_dict_aggregated["demand_el"]["timeseries"].index + + energysystem = EnergySystem( + timeindex=date_time_index, + infer_last_interval=False, + + ) + + ########################################################################## + # Create oemof object + ########################################################################## + + logging.info("Create oemof objects") + + # The bus objects were assigned to variables which makes it easier to + # connect components to these buses (see below). + + # create natural gas bus + bgas = buses.Bus(label="natural_gas") + + # create electricity bus + bel = buses.Bus(label="electricity") + + # adding the buses to the energy system + energysystem.add(bgas, bel) + + # create excess component for the electricity bus to allow overproduction + energysystem.add(cmp.Sink(label="excess_bel", inputs={bel: flows.Flow()})) + + # create source object representing the gas commodity (annual limit) + energysystem.add( + cmp.Source( + label="rgas", + outputs={bgas: flows.Flow()}, + ) + ) + + # create fixed source object representing wind power plants + energysystem.add( + cmp.Source( + label="wind", + outputs={bel: flows.Flow(fix=data_dict_aggregated["wind"]["timeseries"], + + nominal_value=1000000)}, + ) + ) + + # create fixed source object representing pv power plants + energysystem.add( + cmp.Source( + label="pv", + outputs={bel: flows.Flow(fix=data_dict_aggregated["pv"]["timeseries"], nominal_value=582000)}, + ) + ) + + # create simple sink object representing the electrical demand + energysystem.add( + cmp.Sink( + label="demand", + inputs={bel: flows.Flow(fix=data_dict_aggregated["demand_el"]["timeseries"], nominal_value=1)}, + ) + ) + + # create simple transformer object representing a gas power plant + energysystem.add( + cmp.Transformer( + label="pp_gas", + inputs={bgas: flows.Flow()}, + outputs={bel: flows.Flow(nominal_value=10e10, variable_costs=50)}, + conversion_factors={bel: 0.58}, + ) + ) + + ########################################################################## + # Optimise the energy system and plot the results + ########################################################################## + + logging.info("Optimise the energy system") + + # initialise the operational model + model = Model(energysystem, objective_weighting= objective_weighting) + + # This is for debugging only. It is not(!) necessary to solve the problem + # and should be set to False to save time and disc space in normal use. For + # debugging the timesteps should be set to 3, to increase the readability + # of the lp-file. + if debug: + filename = os.path.join( + helpers.extend_basic_path("lp_files"), "basic_example.lp" + ) + logging.info("Store lp-file in {0}.".format(filename)) + model.write(filename, io_options={"symbolic_solver_labels": True}) + + # if tee_switch is true solver messages will be displayed + logging.info("Solve the optimization problem") + model.solve(solver=solver, solve_kwargs={"tee": solver_verbose}) + + logging.info("Store the energy system with the results.") + + # The processing module of the outputlib can be used to extract the results + # from the model transfer them into a homogeneous structured dictionary. + + # add results to the energy system to make it possible to store them. + energysystem.results["main"] = processing.results(model) + energysystem.results["meta"] = processing.meta_results(model) + + # The default path is the '.oemof' folder in your $HOME directory. + # The default filename is 'es_dump.oemof'. + # You can omit the attributes (as None is the default value) for testing + # cases. You should use unique names/folders for valuable results to avoid + # overwriting. + + # store energy system with results + energysystem.dump(dpath=None, filename=None) + + # ************************************************************************* + # ********** PART 2 - Processing the results ****************************** + # ************************************************************************* + + logging.info("**** The script can be divided into two parts here.") + logging.info("Restore the energy system and the results.") + energysystem = EnergySystem() + energysystem.restore(dpath=None, filename=None) + + # define an alias for shorter calls below (optional) + results = energysystem.results["main"] + + electricity_bus = views.node(results, "electricity") + + # print the solver results + print("********* Meta results *********") + pp.pprint(energysystem.results["meta"]) + print("") + + # print the sums of the flows around the electricity bus + print("********* Main results *********") + +if __name__ == "__main__": + main() + + From 6d13e19d7144c2caa8d425aff0c1a98ca2bc62f2 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Wed, 4 Oct 2023 11:05:12 +0200 Subject: [PATCH 02/37] add aggregate time series in _helpers --- src/oemof/solph/_helpers.py | 153 ++++++++++++++++++++++++++++++++++++ 1 file changed, 153 insertions(+) diff --git a/src/oemof/solph/_helpers.py b/src/oemof/solph/_helpers.py index fc245aca2..ac9324d14 100644 --- a/src/oemof/solph/_helpers.py +++ b/src/oemof/solph/_helpers.py @@ -19,7 +19,12 @@ from oemof.tools import debugging +from tsam.timeseriesaggregation import TimeSeriesAggregation +from tsam.hyperparametertuning import HyperTunedAggregations +from typing import Dict, List +import copy +import pandas as pd def check_node_object_for_missing_attribute(obj, attribute): """Raises a predefined warning if object does not have attribute. @@ -45,3 +50,151 @@ def warn_if_missing_attribute(obj, attribute): msg.format(attribute, obj.label, type(obj)), debugging.SuspiciousUsageWarning, ) + +def aggregate_time_series( + data_dict_to_aggregate: Dict[str, Dict[str, any]], + number_of_typical_periods: int = 7, + number_of_time_steps_per_periods : int = 24, + number_of_segments_per_period :int = 16, + segmentation : bool = False, + cluster_method : str = 'hierarchical', + sort_values : bool = True, + minutes_per_time_step : int = 60, + ): + """Aggregate timeseries with the tsam-package. + Firstly controls format of input data. + Secondly does time series aggregation with tsam. + Thirdly changes datetime index of dataframe of results. + + Arguments + --------- + #todo: Few explanations are copied 1to1 from tsam. Here has to be correctly linked to source! + data_dict_to_aggregate : Dictionary of a dictionary with a time series and a weighted factor. The "timeseries" has + to have the datetime as index and the timeseries parameters as columns. Format: + data_dict_to_aggregate = {"name" : {"timeseries" : pd.Series, "weighted_factor" : float} + number_of_typical_periods : (int) Number of typical Periods - equivalent to the number of clusters. + number_of_time_steps_per_periods: (int) Value which defines the length of a cluster period. + number_of_segments_per_period: (int) Number of segments in which the typical periods should be subdivided - + equivalent to the number of inner-period clusters. + segmentation: (bool) Decision variable if typical periods should be subdivided + cluster_method: (str) Chosen clustering method + Options are: + * 'averaging' + * 'k_means' + * 'k_medoids' + * 'k_maxoids' + * 'hierarchical' + * 'adjacent_periods' + sort_values : (bool) #todo understand what this variable does + """ + entry_format_timeseries = None + for entry_name, entry_data in data_dict_to_aggregate.items(): + if not isinstance(entry_data, dict): + raise ValueError(f"Entry '{entry_name}' should have a dictionary as its value.") + + required_keys = ["timeseries", "weighted_factor"] + missing_keys = [key for key in required_keys if key not in entry_data] + + if entry_format_timeseries is None: + entry_format_timeseries = entry_data["timeseries"].index + else: + if not entry_format_timeseries.equals(entry_data["timeseries"].index): + raise ValueError(f"TimeSeries Format of at least'{entry_name}' is unequal to: {previous_entry_name}") + previous_entry_name = entry_name + if missing_keys: + raise ValueError(f"Entry '{entry_name}' is missing the following keys: {', '.join(missing_keys)}") + + if not isinstance(entry_data["timeseries"], pd.Series): + raise ValueError(f"Timeseries for entry '{entry_name}' should be a pd.Series.") + + if not all(isinstance(timestamp, (int, float)) for timestamp in entry_data["timeseries"]): + raise ValueError(f"Timeseries for entry '{entry_name}' should contain only numeric timestamps.") + + if not isinstance(entry_data["weighted_factor"], (float, int)): + raise ValueError(f"Weighted factor for entry '{entry_name}' should be a float.") + + if segmentation: + if number_of_segments_per_period > number_of_time_steps_per_periods: + ValueError(f"Number of segments per period equal to'{number_of_segments_per_period}' has to be smaller than number of time steps per periods equal to {number_of_time_steps_per_periods}") + + hours_per_period = number_of_time_steps_per_periods * minutes_per_time_step / 60 + time_series_data = pd.DataFrame() + weighted_factors_dict = {} + for key, value in data_dict_to_aggregate.items(): + if 'timeseries' in value: + time_series_data[key] = value['timeseries'] + if 'weighted_factor' in value: + weighted_factors_dict[key] = value['weighted_factor'] + if segmentation: + clusterClass = TimeSeriesAggregation( + timeSeries=time_series_data, + noTypicalPeriods=number_of_typical_periods, + segmentation=segmentation, + noSegments=number_of_segments_per_period, + hoursPerPeriod=hours_per_period, + clusterMethod=cluster_method, + sortValues=sort_values, + weightDict=weighted_factors_dict + ) + data = pd.DataFrame.from_dict(clusterClass.clusterPeriodDict) + else: + clusterClass = TimeSeriesAggregation( + timeSeries=time_series_data, + noTypicalPeriods=number_of_typical_periods, + hoursPerPeriod=hours_per_period, + clusterMethod=cluster_method, + sortValues=sort_values, + weightDict=weighted_factors_dict, + ) + data = pd.DataFrame.from_dict(clusterClass.clusterPeriodDict) + + hours_of_time_series = entry_format_timeseries.__len__() + periods_name = clusterClass.clusterPeriodIdx + periods_order = clusterClass.clusterOrder + periods_total_occurrence = [ + (periods_order == typical_period_name).sum() for typical_period_name in periods_name + ] + start_date = entry_format_timeseries[0] + if not sum(periods_total_occurrence) * hours_per_period == 8760: + + print("aggregated timeseries has: " +str(int(sum(periods_total_occurrence) * hours_per_period))+ " timesteps") + print("unaggregated timeseries has: " +str(hours_of_time_series)+ " timesteps") + print("therefore the occurrence of the typical periods for the objective weighting will be customized") + customize_factor = hours_of_time_series / int(sum(periods_total_occurrence) * hours_per_period) + result_list = [float(occurrence) * customize_factor for occurrence in periods_total_occurrence] + periods_total_occurrence = result_list + + + current_timestamp = pd.to_datetime(start_date) + previous_period = 0 + objective_weighting = [] + extended_time_series = [] + minute_resolution_of_one_hour = 60 + if segmentation: + for period, timestep, segmented_timestep in data.index: + if previous_period == period: + extended_time_series.append(current_timestamp) + else: + extended_time_series.append(current_timestamp) + previous_period = period + objective_weighting.append(periods_total_occurrence[period] * segmented_timestep) + current_timestamp += pd.Timedelta(minutes=minute_resolution_of_one_hour * segmented_timestep) + else: + for period, timestep in data.index: + if previous_period == period: + extended_time_series.append(current_timestamp) + else: + extended_time_series.append(current_timestamp) + previous_period = period + objective_weighting.append(periods_total_occurrence[period]) + current_timestamp += pd.Timedelta(minutes=minute_resolution_of_one_hour) + + data.index = extended_time_series + data_dict_aggregated = {} + for name in data: + if len(data[name]) == len(objective_weighting): + data_dict_aggregated[name] = { "timeseries" : data[name]} + else: + raise ValueError(f"Aggregated timeseries for: '{data[name]}' has a different length as " + f"objective weighting list") + return data_dict_aggregated, objective_weighting, clusterClass From 5cdf1f2177002a0d5108d33ad3e11fb2c608ca9e Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Wed, 4 Oct 2023 11:29:34 +0200 Subject: [PATCH 03/37] add basic_example csv --- examples/tsam/basic_example.csv | 8761 +++++++++++++++++++++++++++ examples/tsam/basic_tsam_example.py | 11 +- 2 files 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+8757,228880.1477697,0,0.7667 +8758,226554.0080573,0,0.7523 +8759,236343.1793469,0,0.73739 +8760,235840.1054805,0,0.70744 diff --git a/examples/tsam/basic_tsam_example.py b/examples/tsam/basic_tsam_example.py index b84c29c51..11effbd8e 100644 --- a/examples/tsam/basic_tsam_example.py +++ b/examples/tsam/basic_tsam_example.py @@ -52,9 +52,7 @@ import os import pprint as pp import warnings -from datetime import datetime -import matplotlib.pyplot as plt import pandas as pd from oemof.tools import logger @@ -62,15 +60,13 @@ from oemof.solph import Model from oemof.solph import buses from oemof.solph import components as cmp -from oemof.solph import create_time_index from oemof.solph import flows from oemof.solph import helpers from oemof.solph import processing from oemof.solph import views from oemof.solph._helpers import aggregate_time_series -from oemof.solph._helpers import plotTS -from oemof.solph._helpers import determine_aggregation_parameters -def main(number_of_typical_periods:int): + +def main(): # ************************************************************************* # ********** PART 1 - Define and optimise the energy system *************** # ************************************************************************* @@ -99,7 +95,7 @@ def main(number_of_typical_periods:int): number_of_time_steps_per_periods =24 number_of_segments_per_period = 24*4 - aggregation_ratio = ( number_of_typical_periods * number_of_time_steps_per_periods ) / 8760 + number_of_typical_periods = 12 segmentation = False if False: determine_aggregation_parameters(data_dict_to_aggregate = data_dict_to_aggregate, @@ -267,6 +263,7 @@ def main(number_of_typical_periods:int): # print the sums of the flows around the electricity bus print("********* Main results *********") + print(electricity_bus["sequences"].sum(axis=0)) if __name__ == "__main__": main() From 2aeb2d83e22b68974ee53e8fb8a5edb586751029 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Wed, 4 Oct 2023 10:56:00 +0200 Subject: [PATCH 04/37] add example for tsam use --- examples/tsam/basic_tsam_example.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/examples/tsam/basic_tsam_example.py b/examples/tsam/basic_tsam_example.py index 11effbd8e..b84c29c51 100644 --- a/examples/tsam/basic_tsam_example.py +++ b/examples/tsam/basic_tsam_example.py @@ -52,7 +52,9 @@ import os import pprint as pp import warnings +from datetime import datetime +import matplotlib.pyplot as plt import pandas as pd from oemof.tools import logger @@ -60,13 +62,15 @@ from oemof.solph import Model from oemof.solph import buses from oemof.solph import components as cmp +from oemof.solph import create_time_index from oemof.solph import flows from oemof.solph import helpers from oemof.solph import processing from oemof.solph import views from oemof.solph._helpers import aggregate_time_series - -def main(): +from oemof.solph._helpers import plotTS +from oemof.solph._helpers import determine_aggregation_parameters +def main(number_of_typical_periods:int): # ************************************************************************* # ********** PART 1 - Define and optimise the energy system *************** # ************************************************************************* @@ -95,7 +99,7 @@ def main(): number_of_time_steps_per_periods =24 number_of_segments_per_period = 24*4 - number_of_typical_periods = 12 + aggregation_ratio = ( number_of_typical_periods * number_of_time_steps_per_periods ) / 8760 segmentation = False if False: determine_aggregation_parameters(data_dict_to_aggregate = data_dict_to_aggregate, @@ -263,7 +267,6 @@ def main(): # print the sums of the flows around the electricity bus print("********* Main results *********") - print(electricity_bus["sequences"].sum(axis=0)) if __name__ == "__main__": main() From 52c4d1ce3a496f5b7c8514f2fceb7d47740a0a3e Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Wed, 4 Oct 2023 11:29:34 +0200 Subject: [PATCH 05/37] add basic_example csv --- examples/tsam/basic_tsam_example.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/examples/tsam/basic_tsam_example.py b/examples/tsam/basic_tsam_example.py index b84c29c51..11effbd8e 100644 --- a/examples/tsam/basic_tsam_example.py +++ b/examples/tsam/basic_tsam_example.py @@ -52,9 +52,7 @@ import os import pprint as pp import warnings -from datetime import datetime -import matplotlib.pyplot as plt import pandas as pd from oemof.tools import logger @@ -62,15 +60,13 @@ from oemof.solph import Model from oemof.solph import buses from oemof.solph import components as cmp -from oemof.solph import create_time_index from oemof.solph import flows from oemof.solph import helpers from oemof.solph import processing from oemof.solph import views from oemof.solph._helpers import aggregate_time_series -from oemof.solph._helpers import plotTS -from oemof.solph._helpers import determine_aggregation_parameters -def main(number_of_typical_periods:int): + +def main(): # ************************************************************************* # ********** PART 1 - Define and optimise the energy system *************** # ************************************************************************* @@ -99,7 +95,7 @@ def main(number_of_typical_periods:int): number_of_time_steps_per_periods =24 number_of_segments_per_period = 24*4 - aggregation_ratio = ( number_of_typical_periods * number_of_time_steps_per_periods ) / 8760 + number_of_typical_periods = 12 segmentation = False if False: determine_aggregation_parameters(data_dict_to_aggregate = data_dict_to_aggregate, @@ -267,6 +263,7 @@ def main(number_of_typical_periods:int): # print the sums of the flows around the electricity bus print("********* Main results *********") + print(electricity_bus["sequences"].sum(axis=0)) if __name__ == "__main__": main() From e24f1d1fde8d5010e995d74e9d51c6798730b6a0 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 9 Oct 2023 12:04:10 +0200 Subject: [PATCH 06/37] functions to generate correct objective weighting for cost function are integrated into the exmaple --- ...hnologies_but_storage_using_mp_and_tsam.py | 128 ++++++++++++++---- 1 file changed, 99 insertions(+), 29 deletions(-) diff --git a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py index c2dc90ba0..98f2676c9 100644 --- a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py +++ b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py @@ -88,7 +88,50 @@ from oemof.tools import logger from oemof import solph - +def check_equal_timesteps_after_aggregation(hours_per_period : int, + hours_of_input_time_series: int, + periods_total_occurrence: list): + + if not sum(periods_total_occurrence) * hours_per_period == 8760: + #todo: prints can be deleted in future + print("aggregated timeseries has: " + str(int(sum(periods_total_occurrence) * hours_per_period)) + " timesteps") + print("unaggregated timeseries has: " + str(hours_of_input_time_series) + " timesteps") + print("therefore the occurrence of the typical periods for the objective weighting will be customized") + customize_factor = hours_of_input_time_series / int(sum(periods_total_occurrence) * hours_per_period) + result_list = [float(occurrence) * customize_factor for occurrence in periods_total_occurrence] + periods_total_occurrence = result_list + return periods_total_occurrence + else: + return periods_total_occurrence + +def set_aggregated_timeseries_and_objective_weighting(segmentation, + periods_total_occurrence, + aggregated_period_dict, + first_time_stamp): + previous_period = 0 + objective_weighting = [] + aggregated_time_series = [] + current_timestamp=first_time_stamp + if segmentation: + for period, timestep, segmented_timestep in aggregated_period_dict.index: + if previous_period == period: + aggregated_time_series.append(current_timestamp) + else: + aggregated_time_series.append(current_timestamp) + previous_period = period + objective_weighting.append(periods_total_occurrence[period] * segmented_timestep) + current_timestamp += pd.Timedelta(minutes=60 * segmented_timestep) + else: + for period, timestep in aggregated_period_dict.index: + if previous_period == period: + aggregated_time_series.append(current_timestamp) + else: + aggregated_time_series.append(current_timestamp) + previous_period = period + objective_weighting.append(periods_total_occurrence[period]) + current_timestamp += pd.Timedelta(minutes=60) + aggregated_time_series = pd.DatetimeIndex(aggregated_time_series) + return aggregated_time_series, objective_weighting def main(): # Read data file @@ -124,38 +167,65 @@ def main(): typical_periods = 40 hours_per_period = 24 + segmentation = False + if segmentation: + print("segmentation hasn't been added so far") + + + else: + aggregation1 = tsam.TimeSeriesAggregation( + timeSeries=data.iloc[:8760], + noTypicalPeriods=typical_periods, + hoursPerPeriod=hours_per_period, + clusterMethod="k_means", + sortValues=False, + rescaleClusterPeriods=False, + extremePeriodMethod="replace_cluster_center", + addPeakMin=["wind", "pv"], + representationMethod="durationRepresentation", + ) + + aggregation2 = tsam.TimeSeriesAggregation( + timeSeries=data.iloc[8760:], + noTypicalPeriods=typical_periods, + hoursPerPeriod=hours_per_period, + clusterMethod="hierarchical", + sortValues=False, + rescaleClusterPeriods=False, + extremePeriodMethod="replace_cluster_center", + addPeakMin=["wind", "pv"], + representationMethod="durationRepresentation", + ) - aggregation1 = tsam.TimeSeriesAggregation( - timeSeries=data.iloc[:8760], - noTypicalPeriods=typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod="k_means", - sortValues=False, - rescaleClusterPeriods=False, - extremePeriodMethod="replace_cluster_center", - addPeakMin=["wind", "pv"], - representationMethod="durationRepresentation", - ) aggregation1.createTypicalPeriods() - aggregation2 = tsam.TimeSeriesAggregation( - timeSeries=data.iloc[8760:], - noTypicalPeriods=typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod="hierarchical", - sortValues=False, - rescaleClusterPeriods=False, - extremePeriodMethod="replace_cluster_center", - addPeakMin=["wind", "pv"], - representationMethod="durationRepresentation", - ) aggregation2.createTypicalPeriods() - t1_agg = pd.date_range( - "2022-01-01", periods=typical_periods * hours_per_period, freq="H" - ) - t2_agg = pd.date_range( - "2033-01-01", periods=typical_periods * hours_per_period, freq="H" - ) + periods_total_occurrence1 = [ + (aggregation1.clusterOrder == typical_period_name).sum() for typical_period_name in + aggregation1.clusterPeriodIdx] + periods_total_occurrence2 = [ + (aggregation2.clusterOrder == typical_period_name).sum() for typical_period_name in + aggregation2.clusterPeriodIdx] + periods_total_occurrence1 = check_equal_timesteps_after_aggregation(hours_per_period=hours_per_period, + hours_of_input_time_series=t1.__len__(), + periods_total_occurrence=periods_total_occurrence1 + ) + periods_total_occurrence2 = check_equal_timesteps_after_aggregation(hours_per_period = hours_per_period, + hours_of_input_time_series = t2.__len__(), + periods_total_occurrence=periods_total_occurrence2 + ) + #before timeseries generation was based on freq="H" (hourly), now you have to set the number of minutes of one timestep + t1_agg, objective_weighting1 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, + periods_total_occurrence = periods_total_occurrence1, + aggregated_period_dict=pd.DataFrame.from_dict(aggregation1.clusterPeriodDict), + first_time_stamp=pd.to_datetime(t1[0]) + ) + t2_agg, objective_weighting2 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, + periods_total_occurrence = periods_total_occurrence2, + aggregated_period_dict=pd.DataFrame.from_dict(aggregation2.clusterPeriodDict), + first_time_stamp=pd.to_datetime(t1[0]) + ) + tindex_agg = t1_agg.append(t2_agg) energysystem = solph.EnergySystem( From a97fd467cf8264cea515ab96b3f05e0d07949932 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 9 Oct 2023 12:09:24 +0200 Subject: [PATCH 07/37] added objetive weighting fo model --- ..._optimize_all_technologies_but_storage_using_mp_and_tsam.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py index 98f2676c9..823241be8 100644 --- a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py +++ b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py @@ -225,6 +225,7 @@ def main(): aggregated_period_dict=pd.DataFrame.from_dict(aggregation2.clusterPeriodDict), first_time_stamp=pd.to_datetime(t1[0]) ) + objective_weighting = objective_weighting1 + objective_weighting2 tindex_agg = t1_agg.append(t2_agg) @@ -372,7 +373,7 @@ def main(): logging.info("Optimise the energy system") # initialise the operational model - om = solph.Model(energysystem) + om = solph.Model(energysystem,objective_weighting= objective_weighting) # if tee_switch is true solver messages will be displayed logging.info("Solve the optimization problem") From 89d1f5e05dae14effedd86c1a093a6edc45363c5 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 9 Oct 2023 17:15:06 +0200 Subject: [PATCH 08/37] add plot SOC for example5 --- ...ll_technologies_but_storage_using_mp_and_tsam.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py index 823241be8..802d55228 100644 --- a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py +++ b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py @@ -82,10 +82,12 @@ import pprint as pp import warnings import tsam.timeseriesaggregation as tsam +import matplotlib.pyplot as plt import pandas as pd from oemof.tools import economics from oemof.tools import logger +from oemof.solph import views from oemof import solph def check_equal_timesteps_after_aggregation(hours_per_period : int, @@ -373,7 +375,9 @@ def main(): logging.info("Optimise the energy system") # initialise the operational model - om = solph.Model(energysystem,objective_weighting= objective_weighting) + om = solph.Model(energysystem, + objective_weighting= objective_weighting + ) # if tee_switch is true solver messages will be displayed logging.info("Solve the optimization problem") @@ -399,6 +403,13 @@ def main(): meta_results = solph.processing.meta_results(om) pp.pprint(meta_results) + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(storage, None)]["sequences"] / storage.nominal_storage_capacity + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + plt.show() + my_results = electricity_bus["period_scalars"] # installed capacity of storage in GWh From fc8d150e8ea2dbd1e0fd5593d75f10b1b66d422e Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 9 Oct 2023 17:15:38 +0200 Subject: [PATCH 09/37] add no investment storage example for simpler debugging --- ...mize_all_technologies_using_mp_and_tsam.py | 428 ++++++++++++++++++ 1 file changed, 428 insertions(+) create mode 100644 examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py diff --git a/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py b/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py new file mode 100644 index 000000000..bb6236f0c --- /dev/null +++ b/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py @@ -0,0 +1,428 @@ +# -*- coding: utf-8 -*- + +""" +General description +------------------- +This example shows how to perform a capacity optimization for +an energy system with storage. The following energy system is modeled: + +.. code-block:: text + + input/output bgas bel + | | | + | | | + wind(FixedSource) |------------------>| + | | | + pv(FixedSource) |------------------>| + | | | + gas_resource |--------->| | + (Commodity) | | | + | | | + demand(Sink) |<------------------| + | | | + | | | + pp_gas(Converter) |<---------| | + |------------------>| + | | | + storage(Storage) |<------------------| + |------------------>| + +The example exists in four variations. The following parameters describe +the main setting for the optimization variation 1: + +- optimize wind, pv, gas_resource and storage +- set investment cost for wind, pv and storage +- set gas price for kWh + +Results show an installation of wind and the use of the gas resource. +A renewable energy share of 51% is achieved. + +.. tip:: + + Have a look at different parameter settings. There are four variations + of this example in the same folder. + +Code +---- +Download source code: :download:`v1_invest_optimize_all_technologies.py ` + +.. dropdown:: Click to display code + + .. literalinclude:: /../examples/storage_investment/v1_invest_optimize_all_technologies.py + :language: python + :lines: 80- + +Data +---- +Download data: :download:`storage_investment.csv ` + + +Installation requirements +------------------------- + +This example requires oemof.solph (v0.5.x), install by: + +.. code:: bash + + pip install oemof.solph[examples] + + +License +------- +`MIT license `_ + +""" + +############################################################################### +# Imports +############################################################################### + +import logging +import os +import pprint as pp +import warnings +import tsam.timeseriesaggregation as tsam +import matplotlib.pyplot as plt + +import pandas as pd +from oemof.tools import economics +from oemof.tools import logger +from oemof.solph import views + +from oemof import solph +def check_equal_timesteps_after_aggregation(hours_per_period : int, + hours_of_input_time_series: int, + periods_total_occurrence: list): + + if not sum(periods_total_occurrence) * hours_per_period == 8760: + #todo: prints can be deleted in future + print("aggregated timeseries has: " + str(int(sum(periods_total_occurrence) * hours_per_period)) + " timesteps") + print("unaggregated timeseries has: " + str(hours_of_input_time_series) + " timesteps") + print("therefore the occurrence of the typical periods for the objective weighting will be customized") + customize_factor = hours_of_input_time_series / int(sum(periods_total_occurrence) * hours_per_period) + result_list = [float(occurrence) * customize_factor for occurrence in periods_total_occurrence] + periods_total_occurrence = result_list + return periods_total_occurrence + else: + return periods_total_occurrence + +def set_aggregated_timeseries_and_objective_weighting(segmentation, + periods_total_occurrence, + aggregated_period_dict, + first_time_stamp): + previous_period = 0 + objective_weighting = [] + aggregated_time_series = [] + current_timestamp=first_time_stamp + if segmentation: + for period, timestep, segmented_timestep in aggregated_period_dict.index: + if previous_period == period: + aggregated_time_series.append(current_timestamp) + else: + aggregated_time_series.append(current_timestamp) + previous_period = period + objective_weighting.append(periods_total_occurrence[period] * segmented_timestep) + current_timestamp += pd.Timedelta(minutes=60 * segmented_timestep) + else: + for period, timestep in aggregated_period_dict.index: + if previous_period == period: + aggregated_time_series.append(current_timestamp) + else: + aggregated_time_series.append(current_timestamp) + previous_period = period + objective_weighting.append(periods_total_occurrence[period]) + current_timestamp += pd.Timedelta(minutes=60) + aggregated_time_series = pd.DatetimeIndex(aggregated_time_series) + return aggregated_time_series, objective_weighting + +def main(): + # Read data file + filename = os.path.join(os.getcwd(), "storage_investment.csv") + try: + data = pd.read_csv(filename) + except FileNotFoundError: + msg = "Data file not found: {0}. Only one value used!" + warnings.warn(msg.format(filename), UserWarning) + data = pd.DataFrame( + {"pv": [0.3, 0.5], "wind": [0.6, 0.8], "demand_el": [500, 600]} + ) + + data = pd.concat([data, data], ignore_index=True) + data["wind"].iloc[8760 - 24 : 8760] = 0 + data["wind"].iloc[8760 * 2 - 24 : 8760] = 0 + data["pv"].iloc[8760 - 24 : 8760] = 0 + data["pv"].iloc[8760 * 2 - 24 : 8760] = 0 + + ########################################################################## + # Initialize the energy system and read/calculate necessary parameters + ########################################################################## + + logger.define_logging() + logging.info("Initialize the energy system") + + t1 = pd.date_range("2022-01-01", periods=8760, freq="H") + t2 = pd.date_range("2023-01-01", periods=8760, freq="H") + tindex = t1.append(t2) + + data.index = tindex + del data["timestep"] + + typical_periods = 2 + hours_per_period = 24*30 + segmentation = False + if segmentation: + print("segmentation hasn't been added so far") + + + else: + aggregation1 = tsam.TimeSeriesAggregation( + timeSeries=data.iloc[:8760], + noTypicalPeriods=typical_periods, + hoursPerPeriod=hours_per_period, + clusterMethod="k_means", + sortValues=False, + rescaleClusterPeriods=False, + extremePeriodMethod="replace_cluster_center", + addPeakMin=["wind", "pv"], + representationMethod="durationRepresentation", + ) + + aggregation2 = tsam.TimeSeriesAggregation( + timeSeries=data.iloc[8760:], + noTypicalPeriods=typical_periods, + hoursPerPeriod=hours_per_period, + clusterMethod="hierarchical", + sortValues=False, + rescaleClusterPeriods=False, + extremePeriodMethod="replace_cluster_center", + addPeakMin=["wind", "pv"], + representationMethod="durationRepresentation", + ) + + aggregation1.createTypicalPeriods() + aggregation2.createTypicalPeriods() + + periods_total_occurrence1 = [ + (aggregation1.clusterOrder == typical_period_name).sum() for typical_period_name in + aggregation1.clusterPeriodIdx] + periods_total_occurrence2 = [ + (aggregation2.clusterOrder == typical_period_name).sum() for typical_period_name in + aggregation2.clusterPeriodIdx] + periods_total_occurrence1 = check_equal_timesteps_after_aggregation(hours_per_period=hours_per_period, + hours_of_input_time_series=t1.__len__(), + periods_total_occurrence=periods_total_occurrence1 + ) + periods_total_occurrence2 = check_equal_timesteps_after_aggregation(hours_per_period = hours_per_period, + hours_of_input_time_series = t2.__len__(), + periods_total_occurrence=periods_total_occurrence2 + ) + #before timeseries generation was based on freq="H" (hourly), now you have to set the number of minutes of one timestep + t1_agg, objective_weighting1 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, + periods_total_occurrence = periods_total_occurrence1, + aggregated_period_dict=pd.DataFrame.from_dict(aggregation1.clusterPeriodDict), + first_time_stamp=pd.to_datetime(t1[0]) + ) + t2_agg, objective_weighting2 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, + periods_total_occurrence = periods_total_occurrence2, + aggregated_period_dict=pd.DataFrame.from_dict(aggregation2.clusterPeriodDict), + first_time_stamp=pd.to_datetime(t1[0]) + ) + objective_weighting = objective_weighting1 + objective_weighting2 + + tindex_agg = t1_agg.append(t2_agg) + + energysystem = solph.EnergySystem( + timeindex=tindex_agg, + timeincrement=[1] * len(tindex_agg), + periods=[t1_agg, t2_agg], + tsa_parameters=[ + { + "timesteps_per_period": aggregation1.hoursPerPeriod, + "order": aggregation1.clusterOrder, + "occurrences": aggregation1.clusterPeriodNoOccur, + "timeindex": aggregation1.timeIndex, + }, + { + "timesteps_per_period": aggregation2.hoursPerPeriod, + "order": aggregation2.clusterOrder, + "occurrences": aggregation2.clusterPeriodNoOccur, + "timeindex": aggregation2.timeIndex, + }, + ], + infer_last_interval=False, + ) + + price_gas = 5 + + ########################################################################## + # Create oemof objects + ########################################################################## + + logging.info("Create oemof objects") + # create natural gas bus + bgas = solph.Bus(label="natural_gas") + + # create electricity bus + bel = solph.Bus(label="electricity") + + energysystem.add(bgas, bel) + + # create excess component for the electricity bus to allow overproduction + excess = solph.components.Sink( + label="excess_bel", inputs={bel: solph.Flow()} + ) + + # create source object representing the gas commodity (annual limit) + gas_resource = solph.components.Source( + label="rgas", outputs={bgas: solph.Flow(variable_costs=price_gas)} + ) + + wind_profile = pd.concat( + [ + aggregation1.typicalPeriods["wind"], + aggregation2.typicalPeriods["wind"], + ], + ignore_index=True, + ) + wind_profile.iloc[-24:] = 0 + + # create fixed source object representing wind power plants + wind = solph.components.Source( + label="wind", + outputs={ + bel: solph.Flow( + fix=wind_profile, + nominal_value=300000 + ) + }, + ) + + pv_profile = pd.concat( + [aggregation1.typicalPeriods["pv"], aggregation2.typicalPeriods["pv"]], + ignore_index=True, + ) + pv_profile.iloc[-24:] = 0 + + # create fixed source object representing pv power plants + pv = solph.components.Source( + label="pv", + outputs={ + bel: solph.Flow( + fix=pd.concat( + [ + aggregation1.typicalPeriods["pv"], + aggregation2.typicalPeriods["pv"], + ], + ignore_index=True, + ), + nominal_value=100000 + ) + }, + ) + + # create simple sink object representing the electrical demand + demand = solph.components.Sink( + label="demand", + inputs={ + bel: solph.Flow( + fix=pd.concat( + [ + aggregation1.typicalPeriods["demand_el"], + aggregation2.typicalPeriods["demand_el"], + ], + ignore_index=True, + ), + nominal_value=1, + ) + }, + ) + # create simple Converter object representing a gas power plant + pp_gas = solph.components.Converter( + label="pp_gas", + inputs={bgas: solph.Flow()}, + outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=0)}, + conversion_factors={bel: 0.58}, + ) + + # create storage object representing a battery + storage = solph.components.GenericStorage( + label="storage", + nominal_storage_capacity=5000, + inputs={bel: solph.Flow(variable_costs=0.0001, nominal_value=200)}, + outputs={bel: solph.Flow(nominal_value=200)}, + loss_rate=0.01, + inflow_conversion_factor=1, + outflow_conversion_factor=0.8, + ) + + energysystem.add(excess, gas_resource, wind, pv, demand, pp_gas, storage) + + ########################################################################## + # Optimise the energy system + ########################################################################## + + logging.info("Optimise the energy system") + + # initialise the operational model + om = solph.Model(energysystem, + objective_weighting= objective_weighting + ) + + # if tee_switch is true solver messages will be displayed + logging.info("Solve the optimization problem") + om.solve(solver="cbc", solve_kwargs={"tee": True}) + + ########################################################################## + # Check and plot the results + ########################################################################## + + # check if the new result object is working for custom components + results = solph.processing.results(om) + print(results) + + # Concatenate flows: + flows = pd.concat([flow["sequences"] for flow in results.values()], axis=1) + flows.columns = [ + f"{oemof_tuple[0]}-{oemof_tuple[1]}" for oemof_tuple in results.keys() + ] + print(flows) + + electricity_bus = solph.views.node(results, "electricity") + + meta_results = solph.processing.meta_results(om) + pp.pprint(meta_results) + + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(storage, None)]["sequences"] / storage.nominal_storage_capacity + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + plt.show() + + my_results = electricity_bus["period_scalars"] + + # installed capacity of storage in GWh + my_results["storage_invest_GWh"] = ( + results[(storage, None)]["period_scalars"]["invest"] / 1e6 + ) + + # installed capacity of wind power plant in MW + my_results["wind_invest_MW"] = ( + results[(wind, bel)]["period_scalars"]["invest"] / 1e3 + ) + + # resulting renewable energy share + print( + "res_share:", + ( + 1 + - results[(pp_gas, bel)]["sequences"].sum() + / results[(bel, demand)]["sequences"].sum() + ), + ) + + pp.pprint(my_results) + + +if __name__ == "__main__": + main() From faea66784ea7256cfbef8172dc4ada68125bebd8 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 9 Oct 2023 18:04:02 +0200 Subject: [PATCH 10/37] correct inter_storage_level equation --- src/oemof/solph/components/_generic_storage.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/oemof/solph/components/_generic_storage.py b/src/oemof/solph/components/_generic_storage.py index 8ff93fdc6..e99e881d6 100644 --- a/src/oemof/solph/components/_generic_storage.py +++ b/src/oemof/solph/components/_generic_storage.py @@ -688,7 +688,7 @@ def _inter_storage_balance_rule(block, n, i): m.timeincrement[t] * m.es.tsa_parameters[p]["timesteps_per_period"] ) - expr += -self.storage_content_intra[ + expr += self.storage_content_intra[ n, p, k, m.es.tsa_parameters[p]["timesteps_per_period"] ] return expr == 0 From c0828ba02f8cee5a52539dcdc2459992abe53b50 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Tue, 10 Oct 2023 11:28:20 +0200 Subject: [PATCH 11/37] revert "correction" inter_storage_level equation --- src/oemof/solph/components/_generic_storage.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/oemof/solph/components/_generic_storage.py b/src/oemof/solph/components/_generic_storage.py index e99e881d6..8ff93fdc6 100644 --- a/src/oemof/solph/components/_generic_storage.py +++ b/src/oemof/solph/components/_generic_storage.py @@ -688,7 +688,7 @@ def _inter_storage_balance_rule(block, n, i): m.timeincrement[t] * m.es.tsa_parameters[p]["timesteps_per_period"] ) - expr += self.storage_content_intra[ + expr += -self.storage_content_intra[ n, p, k, m.es.tsa_parameters[p]["timesteps_per_period"] ] return expr == 0 From 72cd17f48bfa424064ee604a2e086da9cb6ee130 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Wed, 11 Oct 2023 10:55:49 +0200 Subject: [PATCH 12/37] add initial inter storage level = 0 --- src/oemof/solph/components/_generic_storage.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/oemof/solph/components/_generic_storage.py b/src/oemof/solph/components/_generic_storage.py index 8ff93fdc6..4fe6e655c 100644 --- a/src/oemof/solph/components/_generic_storage.py +++ b/src/oemof/solph/components/_generic_storage.py @@ -528,6 +528,10 @@ def _storage_content_bound_rule(block, n, t): # set the initial intra storage content # first timestep in intra storage is always zero for n in group: + # todo: Discussion, should the storage level of every period be zero + self.storage_content_inter[n, 0] = 0 + self.storage_content_inter[n, 0].fix() + for p, k in m.TYPICAL_CLUSTERS: self.storage_content_intra[n, p, k, 0] = 0 self.storage_content_intra[n, p, k, 0].fix() From ef417a894e072ba4bb072d54b50355487a5f9b28 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Thu, 12 Oct 2023 10:55:42 +0200 Subject: [PATCH 13/37] updated no investment example, so loadprofiles force use of long term storage --- ...mize_all_technologies_using_mp_and_tsam.py | 121 ++++++++++-------- 1 file changed, 71 insertions(+), 50 deletions(-) diff --git a/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py b/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py index bb6236f0c..33896fcfa 100644 --- a/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py +++ b/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py @@ -153,6 +153,12 @@ def main(): data["pv"].iloc[8760 - 24 : 8760] = 0 data["pv"].iloc[8760 * 2 - 24 : 8760] = 0 + # add a season without electricity production to simulate the possible advantage using a seasonal storages + data["wind"].iloc[2920 * 4:5 * 2920 + 1] = 0 + data["wind"].iloc[2920: 2 * 2920 + 1] = 0 + data["pv"].iloc[2920:2 * 2920 + 1] = 0 + data["pv"].iloc[2920 * 4:5 * 2920 + 1] = 0 + ########################################################################## # Initialize the energy system and read/calculate necessary parameters ########################################################################## @@ -167,8 +173,8 @@ def main(): data.index = tindex del data["timestep"] - typical_periods = 2 - hours_per_period = 24*30 + typical_periods = 60 + hours_per_period = 24 segmentation = False if segmentation: print("segmentation hasn't been added so far") @@ -179,8 +185,8 @@ def main(): timeSeries=data.iloc[:8760], noTypicalPeriods=typical_periods, hoursPerPeriod=hours_per_period, - clusterMethod="k_means", - sortValues=False, + clusterMethod="hierarchical", + sortValues=True, rescaleClusterPeriods=False, extremePeriodMethod="replace_cluster_center", addPeakMin=["wind", "pv"], @@ -192,22 +198,26 @@ def main(): noTypicalPeriods=typical_periods, hoursPerPeriod=hours_per_period, clusterMethod="hierarchical", - sortValues=False, + sortValues=True, rescaleClusterPeriods=False, extremePeriodMethod="replace_cluster_center", addPeakMin=["wind", "pv"], representationMethod="durationRepresentation", ) + aggregation1.createTypicalPeriods() aggregation2.createTypicalPeriods() - - periods_total_occurrence1 = [ - (aggregation1.clusterOrder == typical_period_name).sum() for typical_period_name in - aggregation1.clusterPeriodIdx] - periods_total_occurrence2 = [ - (aggregation2.clusterOrder == typical_period_name).sum() for typical_period_name in - aggregation2.clusterPeriodIdx] + if False: + periods_total_occurrence1 = [ + (aggregation1.clusterOrder == typical_period_name).sum() for typical_period_name in + aggregation1.clusterPeriodIdx] + periods_total_occurrence2 = [ + (aggregation2.clusterOrder == typical_period_name).sum() for typical_period_name in + aggregation2.clusterPeriodIdx] + else: + periods_total_occurrence1 = aggregation1.clusterPeriodNoOccur + periods_total_occurrence2 = aggregation1.clusterPeriodNoOccur periods_total_occurrence1 = check_equal_timesteps_after_aggregation(hours_per_period=hours_per_period, hours_of_input_time_series=t1.__len__(), periods_total_occurrence=periods_total_occurrence1 @@ -227,45 +237,43 @@ def main(): aggregated_period_dict=pd.DataFrame.from_dict(aggregation2.clusterPeriodDict), first_time_stamp=pd.to_datetime(t1[0]) ) - objective_weighting = objective_weighting1 + objective_weighting2 + #objective_weighting = objective_weighting1 + objective_weighting2 + objective_weighting = objective_weighting1 - tindex_agg = t1_agg.append(t2_agg) + #tindex_agg = t1_agg.append(t2_agg) + tindex_agg = t1_agg + #todo aggregation1.clusterPeriodNoOccur besser zum objective weighting nutzen energysystem = solph.EnergySystem( timeindex=tindex_agg, - timeincrement=[1] * len(tindex_agg), - periods=[t1_agg, t2_agg], + #timeincrement=[1] * len(tindex_agg), + periods=[t1_agg, + #t2_agg + ], tsa_parameters=[ { "timesteps_per_period": aggregation1.hoursPerPeriod, "order": aggregation1.clusterOrder, "occurrences": aggregation1.clusterPeriodNoOccur, "timeindex": aggregation1.timeIndex, - }, - { - "timesteps_per_period": aggregation2.hoursPerPeriod, - "order": aggregation2.clusterOrder, - "occurrences": aggregation2.clusterPeriodNoOccur, - "timeindex": aggregation2.timeIndex, - }, + } ], infer_last_interval=False, ) - price_gas = 5 + electricity_price = 100 ########################################################################## # Create oemof objects ########################################################################## logging.info("Create oemof objects") - # create natural gas bus - bgas = solph.Bus(label="natural_gas") + # create electricity bus bel = solph.Bus(label="electricity") - energysystem.add(bgas, bel) + energysystem.add( bel) # create excess component for the electricity bus to allow overproduction excess = solph.components.Sink( @@ -273,14 +281,14 @@ def main(): ) # create source object representing the gas commodity (annual limit) - gas_resource = solph.components.Source( - label="rgas", outputs={bgas: solph.Flow(variable_costs=price_gas)} + elect_resource = solph.components.Source( + label="electricity_source", outputs={bel: solph.Flow(variable_costs=electricity_price)} ) wind_profile = pd.concat( [ aggregation1.typicalPeriods["wind"], - aggregation2.typicalPeriods["wind"], + #aggregation2.typicalPeriods["wind"], ], ignore_index=True, ) @@ -292,13 +300,15 @@ def main(): outputs={ bel: solph.Flow( fix=wind_profile, - nominal_value=300000 + nominal_value=1500000 ) }, ) pv_profile = pd.concat( - [aggregation1.typicalPeriods["pv"], aggregation2.typicalPeriods["pv"]], + [aggregation1.typicalPeriods["pv"], + #aggregation2.typicalPeriods["pv"] + ], ignore_index=True, ) pv_profile.iloc[-24:] = 0 @@ -311,11 +321,11 @@ def main(): fix=pd.concat( [ aggregation1.typicalPeriods["pv"], - aggregation2.typicalPeriods["pv"], + #aggregation2.typicalPeriods["pv"], ], ignore_index=True, ), - nominal_value=100000 + nominal_value=900000 ) }, ) @@ -328,34 +338,28 @@ def main(): fix=pd.concat( [ aggregation1.typicalPeriods["demand_el"], - aggregation2.typicalPeriods["demand_el"], + #aggregation2.typicalPeriods["demand_el"], ], ignore_index=True, ), - nominal_value=1, + nominal_value=0.05, ) }, ) - # create simple Converter object representing a gas power plant - pp_gas = solph.components.Converter( - label="pp_gas", - inputs={bgas: solph.Flow()}, - outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=0)}, - conversion_factors={bel: 0.58}, - ) # create storage object representing a battery storage = solph.components.GenericStorage( label="storage", - nominal_storage_capacity=5000, - inputs={bel: solph.Flow(variable_costs=0.0001, nominal_value=200)}, - outputs={bel: solph.Flow(nominal_value=200)}, - loss_rate=0.01, + nominal_storage_capacity=3000000, + initial_storage_level=0, + inputs={bel: solph.Flow(variable_costs=0.0, nominal_value=2000)}, + outputs={bel: solph.Flow(nominal_value=2000)}, + loss_rate=0.000, inflow_conversion_factor=1, - outflow_conversion_factor=0.8, + outflow_conversion_factor=1, ) - energysystem.add(excess, gas_resource, wind, pv, demand, pp_gas, storage) + energysystem.add(excess, elect_resource, wind, pv, demand, storage) ########################################################################## # Optimise the energy system @@ -398,7 +402,24 @@ def main(): ax=ax, kind="line", drawstyle="steps-post" ) plt.show() - + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(wind, bel)]["sequences"] + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + plt.show() + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(pv, bel)]["sequences"] + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + plt.show() + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(bel, demand)]["sequences"] + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + plt.show() my_results = electricity_bus["period_scalars"] # installed capacity of storage in GWh From 6603677e0f1d746fd552ec47ede118b41b667ef7 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Fri, 13 Oct 2023 11:00:30 +0200 Subject: [PATCH 14/37] extended timeindex to delete manual initializing of timeincrement --- ...optimize_all_technologies_but_storage_using_mp_and_tsam.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py index 802d55228..33a197437 100644 --- a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py +++ b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py @@ -225,15 +225,15 @@ def main(): t2_agg, objective_weighting2 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, periods_total_occurrence = periods_total_occurrence2, aggregated_period_dict=pd.DataFrame.from_dict(aggregation2.clusterPeriodDict), - first_time_stamp=pd.to_datetime(t1[0]) + first_time_stamp=pd.to_datetime(t2[0]) ) objective_weighting = objective_weighting1 + objective_weighting2 + t2_agg = t2_agg.append(pd.DatetimeIndex([t2_agg[-1] + pd.DateOffset(hours=1)])) tindex_agg = t1_agg.append(t2_agg) energysystem = solph.EnergySystem( timeindex=tindex_agg, - timeincrement=[1] * len(tindex_agg), periods=[t1_agg, t2_agg], tsa_parameters=[ { From f8d378a984687b1aec845b5d25a612aba9d51bdc Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 30 Oct 2023 09:14:23 +0100 Subject: [PATCH 15/37] v7 added as reference example with new tsam structure --- ...mize_all_technologies_using_mp_and_tsam.py | 5 +- ...vest_tsam_integrated_into_energy_system.py | 356 ++++++++++++++++++ 2 files changed, 359 insertions(+), 2 deletions(-) create mode 100644 examples/storage_investment/v7_no_invest_tsam_integrated_into_energy_system.py diff --git a/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py b/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py index 33896fcfa..8b6ae57fd 100644 --- a/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py +++ b/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py @@ -132,6 +132,8 @@ def set_aggregated_timeseries_and_objective_weighting(segmentation, previous_period = period objective_weighting.append(periods_total_occurrence[period]) current_timestamp += pd.Timedelta(minutes=60) + #time series have to be extended by one, to fit into form of energysystem iput + aggregated_time_series.append(current_timestamp) aggregated_time_series = pd.DatetimeIndex(aggregated_time_series) return aggregated_time_series, objective_weighting @@ -368,8 +370,7 @@ def main(): logging.info("Optimise the energy system") # initialise the operational model - om = solph.Model(energysystem, - objective_weighting= objective_weighting + om = solph.Model(energysystem ) # if tee_switch is true solver messages will be displayed diff --git a/examples/storage_investment/v7_no_invest_tsam_integrated_into_energy_system.py b/examples/storage_investment/v7_no_invest_tsam_integrated_into_energy_system.py new file mode 100644 index 000000000..66b2c66ec --- /dev/null +++ b/examples/storage_investment/v7_no_invest_tsam_integrated_into_energy_system.py @@ -0,0 +1,356 @@ +# -*- coding: utf-8 -*- + +""" +General description +------------------- +This example shows how to perform a capacity optimization for +an energy system with storage. The following energy system is modeled: + +.. code-block:: text + + input/output bgas bel + | | | + | | | + wind(FixedSource) |------------------>| + | | | + pv(FixedSource) |------------------>| + | | | + gas_resource |--------->| | + (Commodity) | | | + | | | + demand(Sink) |<------------------| + | | | + | | | + pp_gas(Converter) |<---------| | + |------------------>| + | | | + storage(Storage) |<------------------| + |------------------>| + +The example exists in four variations. The following parameters describe +the main setting for the optimization variation 1: + +- optimize wind, pv, gas_resource and storage +- set investment cost for wind, pv and storage +- set gas price for kWh + +Results show an installation of wind and the use of the gas resource. +A renewable energy share of 51% is achieved. + +.. tip:: + + Have a look at different parameter settings. There are four variations + of this example in the same folder. + +Code +---- +Download source code: :download:`v1_invest_optimize_all_technologies.py ` + +.. dropdown:: Click to display code + + .. literalinclude:: /../examples/storage_investment/v1_invest_optimize_all_technologies.py + :language: python + :lines: 80- + +Data +---- +Download data: :download:`storage_investment.csv ` + + +Installation requirements +------------------------- + +This example requires oemof.solph (v0.5.x), install by: + +.. code:: bash + + pip install oemof.solph[examples] + + +License +------- +`MIT license `_ + +""" + +############################################################################### +# Imports +############################################################################### + +import logging +import os +import pprint as pp +import warnings +import tsam.timeseriesaggregation as tsam +import matplotlib.pyplot as plt + +import pandas as pd +from oemof.tools import economics +from oemof.tools import logger +from oemof.solph import views + +from oemof import solph + + +def main(): + # Read data file + filename = os.path.join(os.getcwd(), "storage_investment.csv") + try: + data = pd.read_csv(filename) + except FileNotFoundError: + msg = "Data file not found: {0}. Only one value used!" + warnings.warn(msg.format(filename), UserWarning) + data = pd.DataFrame( + {"pv": [0.3, 0.5], "wind": [0.6, 0.8], "demand_el": [500, 600]} + ) + + data = pd.concat([data, data], ignore_index=True) + data["wind"].iloc[8760 - 24 : 8760] = 0 + data["wind"].iloc[8760 * 2 - 24 : 8760] = 0 + data["pv"].iloc[8760 - 24 : 8760] = 0 + data["pv"].iloc[8760 * 2 - 24 : 8760] = 0 + + # add a season without electricity production to simulate the possible advantage using a seasonal storages + # for the first perido + data["wind"].iloc[2920: 2 * 2920 + 1] = 0 + data["pv"].iloc[2920:2 * 2920 + 1] = 0 + + ########################################################################## + # Initialize the energy system and read/calculate necessary parameters + ########################################################################## + + logger.define_logging() + logging.info("Initialize the energy system") + #todo: right now, tsam only determines the timeincrement right, when you pick the + #first periods last timestamp next to the second periods first timestep + #2022-31-12-23:00 --> 2023-01-01-00:00 , than timeincrement in between is equal to 1 + #todo add initial storage level in new periods is equal to zero? + t1 = pd.date_range("2022-01-01", periods=8760, freq="H") + t2 = pd.date_range("2023-01-01", periods=8760, freq="H") + tindex = t1.append(t2) + + data.index = tindex + del data["timestep"] + + typical_periods = 10 + hours_per_period = 24 + segmentation = False + if segmentation: + print("segmentation hasn't been added so far") + + + else: + aggregation1 = tsam.TimeSeriesAggregation( + timeSeries=data.iloc[:8760], + noTypicalPeriods=typical_periods, + hoursPerPeriod=hours_per_period, + clusterMethod="hierarchical", + sortValues=True, + segmentation=False, + rescaleClusterPeriods=False, + extremePeriodMethod="replace_cluster_center", + addPeakMin=["wind", "pv"], + representationMethod="durationRepresentation", + ) + + aggregation2 = tsam.TimeSeriesAggregation( + timeSeries=data.iloc[8760:], + noTypicalPeriods=typical_periods, + hoursPerPeriod=hours_per_period, + clusterMethod="hierarchical", + sortValues=True, + segmentation=False, + rescaleClusterPeriods=False, + extremePeriodMethod="replace_cluster_center", + addPeakMin=["wind", "pv"], + representationMethod="durationRepresentation", + ) + + energysystem = solph.EnergySystem( + tsam_aggregations=[aggregation1, aggregation2], + infer_last_interval=False, + ) + + electricity_price = 100 + + ########################################################################## + # Create oemof objects + ########################################################################## + + logging.info("Create oemof objects") + + + # create electricity bus + bel = solph.Bus(label="electricity") + + energysystem.add( bel) + + # create excess component for the electricity bus to allow overproduction + excess = solph.components.Sink( + label="excess_bel", inputs={bel: solph.Flow()} + ) + + # create source object representing the gas commodity (annual limit) + elect_resource = solph.components.Source( + label="electricity_source", outputs={bel: solph.Flow(variable_costs=electricity_price)} + ) + + wind_profile = pd.concat( + [ + aggregation1.typicalPeriods["wind"], + aggregation2.typicalPeriods["wind"], + ], + ignore_index=True, + ) + wind_profile.iloc[-24:] = 0 + + # create fixed source object representing wind power plants + wind = solph.components.Source( + label="wind", + outputs={ + bel: solph.Flow( + fix=wind_profile, + nominal_value=1500000 + ) + }, + ) + + pv_profile = pd.concat( + [aggregation1.typicalPeriods["pv"], + aggregation2.typicalPeriods["pv"] + ], + ignore_index=True, + ) + pv_profile.iloc[-24:] = 0 + + # create fixed source object representing pv power plants + if False: + pv = solph.components.Source( + label="pv", + outputs={ + bel: solph.Flow( + fix=pd.concat( + [ + aggregation1.typicalPeriods["pv"], + aggregation2.typicalPeriods["pv"], + ], + ignore_index=True, + ), + nominal_value=900000 + ) + }, + ) + + # create simple sink object representing the electrical demand + demand = solph.components.Sink( + label="demand", + inputs={ + bel: solph.Flow( + fix=pd.concat( + [ + aggregation1.typicalPeriods["demand_el"], + aggregation2.typicalPeriods["demand_el"], + ], + ignore_index=True, + ), + nominal_value=0.05, + ) + }, + ) + + # create storage object representing a battery + storage = solph.components.GenericStorage( + label="storage", + nominal_storage_capacity=3000000, + initial_storage_level=0, + inputs={bel: solph.Flow(variable_costs=0.0, nominal_value=2000)}, + outputs={bel: solph.Flow(nominal_value=2000)}, + loss_rate=0.001, + inflow_conversion_factor=1, + outflow_conversion_factor=1, + ) + if False: + energysystem.add(excess, elect_resource, wind, pv, demand, storage) + else: + energysystem.add(excess, elect_resource, wind, demand, storage) + + ########################################################################## + # Optimise the energy system + ########################################################################## + + logging.info("Optimise the energy system") + + # initialise the operational model + om = solph.Model(energysystem + ) + + # if tee_switch is true solver messages will be displayed + logging.info("Solve the optimization problem") + om.solve(solver="cbc", solve_kwargs={"tee": True}) + + ########################################################################## + # Check and plot the results + ########################################################################## + + # check if the new result object is working for custom components + results = solph.processing.results(om) + print(results) + + # Concatenate flows: + flows = pd.concat([flow["sequences"] for flow in results.values()], axis=1) + flows.columns = [ + f"{oemof_tuple[0]}-{oemof_tuple[1]}" for oemof_tuple in results.keys() + ] + print(flows) + + electricity_bus = solph.views.node(results, "electricity") + + meta_results = solph.processing.meta_results(om) + pp.pprint(meta_results) + + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(storage, None)]["sequences"] / storage.nominal_storage_capacity + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + plt.show() + + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(wind, bel)]["sequences"] + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + ax.set_title("Elect. from Wind") + plt.show() + if False: + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(pv, bel)]["sequences"] + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + ax.set_title("Elect. from PV") + plt.show() + + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(bel, demand)]["sequences"] + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + ax.set_title("Demand") + plt.show() + + fig, ax = plt.subplots(figsize=(10, 5)) + storage_results = results[(elect_resource, bel)]["sequences"] + storage_results .plot( + ax=ax, kind="line", drawstyle="steps-post" + ) + ax.set_title("Elect. from Grid") + plt.show() + my_results = electricity_bus["period_scalars"] + + + pp.pprint(my_results) + + +if __name__ == "__main__": + main() From df5dc92ecd25a6def075e9bc986cbace479fbbb3 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 30 Oct 2023 09:15:50 +0100 Subject: [PATCH 16/37] tsam weighting and functions added to energy system --- src/oemof/solph/_energy_system.py | 91 +++++++++++++++++++++++++++++++ 1 file changed, 91 insertions(+) diff --git a/src/oemof/solph/_energy_system.py b/src/oemof/solph/_energy_system.py index 5f6ccc2bb..e134826b6 100644 --- a/src/oemof/solph/_energy_system.py +++ b/src/oemof/solph/_energy_system.py @@ -90,6 +90,8 @@ def __init__( infer_last_interval=None, periods=None, tsa_parameters=None, + tsam_aggregations=None, + tsam_weighting=None, use_remaining_value=False, **kwargs, ): @@ -100,6 +102,47 @@ def __init__( kwargs["groupings"] = GROUPINGS + kwargs.get("groupings", []) + if tsam_aggregations is not None: + aggregated_time_series, tsam_weighting, tsa_parameters = [], [], [] + first_time_stamp = None + if len(tsam_aggregations) > 1: + periods = [] + for aggregation in tsam_aggregations: + if aggregation.segmentation: + print("segmentation hasn't been added so far") + aggregation.clusterPeriodNoOccur # without, no aggregation.timeIndex exists + if first_time_stamp is None: + first_time_stamp = pd.to_datetime(aggregation.timeIndex[0]) + timeseries, weighting, current_timestamp = self._set_aggregated_timeseries_and_weighting( + segmentation=aggregation.segmentation, + periods_total_occurrence=aggregation.clusterPeriodNoOccur, + aggregated_period_dict=pd.DataFrame.from_dict(aggregation.clusterPeriodDict), + first_time_stamp=first_time_stamp + ) + first_time_stamp = current_timestamp + #todo: this function can be deleted in future. It just serves as a control mechanism. + self._check_equal_timesteps_after_aggregation( + hours_per_period=aggregation.hoursPerPeriod, + hours_of_input_time_series=aggregation.timeSeries.__len__(), + periods_total_occurrence=aggregation.clusterPeriodNoOccur + ) + aggregated_time_series.extend(timeseries) + tsam_weighting.extend(weighting) + tsa_parameters.append( + { + "timesteps_per_period": aggregation.hoursPerPeriod, + "order": aggregation.clusterOrder, + "occurrences": aggregation.clusterPeriodNoOccur, + "timeindex": aggregation.timeIndex, + }) + if periods is not None: + periods.append(timeseries) + #todo does the two periods need to be floating? + timeindex = pd.DatetimeIndex(aggregated_time_series) + + timeindex = timeindex.append(pd.to_datetime([current_timestamp])) + del aggregated_time_series, weighting + self.tsam_weighting = tsam_weighting if not ( isinstance(timeindex, pd.DatetimeIndex) or isinstance(timeindex, type(None)) @@ -273,6 +316,54 @@ def get_period_duration(self, period): + 1 ) + def _set_aggregated_timeseries_and_weighting(self, + segmentation, + periods_total_occurrence, + aggregated_period_dict, + first_time_stamp): + previous_period = 0 + weighting = [] + aggregated_time_series = [] + current_timestamp = first_time_stamp + if segmentation: + for period, timestep, segmented_timestep in aggregated_period_dict.index: + if previous_period == period: + aggregated_time_series.append(current_timestamp) + else: + aggregated_time_series.append(current_timestamp) + previous_period = period + weighting.append(periods_total_occurrence[period] * segmented_timestep) + current_timestamp += pd.Timedelta(minutes=60 * segmented_timestep) + else: + for period, timestep in aggregated_period_dict.index: + if previous_period == period: + aggregated_time_series.append(current_timestamp) + else: + aggregated_time_series.append(current_timestamp) + previous_period = period + weighting.append(periods_total_occurrence[period]) + current_timestamp += pd.Timedelta(minutes=60) + aggregated_time_series = pd.DatetimeIndex(aggregated_time_series) + return aggregated_time_series, weighting, current_timestamp + + def _check_equal_timesteps_after_aggregation(self, + hours_per_period, + hours_of_input_time_series, + periods_total_occurrence): + if not sum(periods_total_occurrence.values()) * hours_per_period == hours_of_input_time_series: + # todo: prints can be deleted in future + print("aggregated timeseries has: " + str( + int( sum(periods_total_occurrence.values()) * hours_per_period)) + " timesteps") + print("unaggregated timeseries has: " + str(hours_of_input_time_series) + " timesteps") + print("therefore the occurrence of the typical periods for the objective weighting will be customized") + + customize_factor = hours_of_input_time_series / int(sum(periods_total_occurrence.values()) * hours_per_period) + print("customize factor is: "+(str(customize_factor))) + result_list = [float(occurrence) * customize_factor for occurrence in periods_total_occurrence] + periods_total_occurrence = result_list + return periods_total_occurrence + else: + return periods_total_occurrence def create_time_index( year: int = None, From 5ab4d58a9508fdffa00af935b29456201c0e0ba3 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 30 Oct 2023 09:17:06 +0100 Subject: [PATCH 17/37] tsam weighting added to models --- src/oemof/solph/_models.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/oemof/solph/_models.py b/src/oemof/solph/_models.py index affc049e6..9365aaee9 100644 --- a/src/oemof/solph/_models.py +++ b/src/oemof/solph/_models.py @@ -17,6 +17,7 @@ import itertools import logging import warnings +import pandas as pd from logging import getLogger from oemof.tools import debugging @@ -126,8 +127,12 @@ def __init__(self, energysystem, **kwargs): self.timeincrement = kwargs.get("timeincrement", self.es.timeincrement) self.objective_weighting = kwargs.get( - "objective_weighting", self.timeincrement + "objective_weighting", pd.Series(1.0, index=self.timeincrement.index) ) + if self.es.tsam_weighting is None: + self.tsam_weighting = pd.Series(1.0, index=self.timeincrement.index) + else: + self.tsam_weighting = self.es.tsam_weighting self._constraint_groups = type(self).CONSTRAINT_GROUPS + kwargs.get( "constraint_groups", [] From 8f8739c4cfb27f9a62b65a3112808abfc3c6b090 Mon Sep 17 00:00:00 2001 From: "Maximilian.Hillen@dlr.de" Date: Mon, 30 Oct 2023 09:17:31 +0100 Subject: [PATCH 18/37] tsam_weighting_objective_weighting and time_increment added to cost function --- src/oemof/solph/flows/_simple_flow_block.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/oemof/solph/flows/_simple_flow_block.py b/src/oemof/solph/flows/_simple_flow_block.py index eb4d55c26..7cebbc1b1 100644 --- a/src/oemof/solph/flows/_simple_flow_block.py +++ b/src/oemof/solph/flows/_simple_flow_block.py @@ -465,7 +465,9 @@ def _objective_expression(self): for p, t in m.TIMEINDEX: variable_costs += ( m.flow[i, o, p, t] + * m.timeincrement[t] * m.objective_weighting[t] + * m.tsam_weighting[t] * m.flows[i, o].variable_costs[t] ) @@ -475,7 +477,9 @@ def _objective_expression(self): for p, t in m.TIMEINDEX: variable_costs += ( m.flow[i, o, p, t] + * m.timeincrement[t] * m.objective_weighting[t] + * m.tsam_weighting[t] * m.flows[i, o].variable_costs[t] * ((1 + m.discount_rate) ** -m.es.periods_years[p]) ) From d22d030573270a28233c88f4ca33a38b1815cf3a Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Tue, 31 Oct 2023 15:50:56 +0100 Subject: [PATCH 19/37] Fix default init of objective and tsam weighting --- src/oemof/solph/_models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/oemof/solph/_models.py b/src/oemof/solph/_models.py index 9365aaee9..2f06076d2 100644 --- a/src/oemof/solph/_models.py +++ b/src/oemof/solph/_models.py @@ -127,10 +127,10 @@ def __init__(self, energysystem, **kwargs): self.timeincrement = kwargs.get("timeincrement", self.es.timeincrement) self.objective_weighting = kwargs.get( - "objective_weighting", pd.Series(1.0, index=self.timeincrement.index) + "objective_weighting", [1] * len(self.timeincrement) ) if self.es.tsam_weighting is None: - self.tsam_weighting = pd.Series(1.0, index=self.timeincrement.index) + self.tsam_weighting = [1] * len(self.timeincrement) else: self.tsam_weighting = self.es.tsam_weighting From f73c79214ede8eba5ea3f1fc2ca90d0bc54e7683 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Tue, 31 Oct 2023 16:36:41 +0100 Subject: [PATCH 20/37] Remove internal tsam aggregation --- src/oemof/solph/_energy_system.py | 91 ------------------------------- 1 file changed, 91 deletions(-) diff --git a/src/oemof/solph/_energy_system.py b/src/oemof/solph/_energy_system.py index e134826b6..5f6ccc2bb 100644 --- a/src/oemof/solph/_energy_system.py +++ b/src/oemof/solph/_energy_system.py @@ -90,8 +90,6 @@ def __init__( infer_last_interval=None, periods=None, tsa_parameters=None, - tsam_aggregations=None, - tsam_weighting=None, use_remaining_value=False, **kwargs, ): @@ -102,47 +100,6 @@ def __init__( kwargs["groupings"] = GROUPINGS + kwargs.get("groupings", []) - if tsam_aggregations is not None: - aggregated_time_series, tsam_weighting, tsa_parameters = [], [], [] - first_time_stamp = None - if len(tsam_aggregations) > 1: - periods = [] - for aggregation in tsam_aggregations: - if aggregation.segmentation: - print("segmentation hasn't been added so far") - aggregation.clusterPeriodNoOccur # without, no aggregation.timeIndex exists - if first_time_stamp is None: - first_time_stamp = pd.to_datetime(aggregation.timeIndex[0]) - timeseries, weighting, current_timestamp = self._set_aggregated_timeseries_and_weighting( - segmentation=aggregation.segmentation, - periods_total_occurrence=aggregation.clusterPeriodNoOccur, - aggregated_period_dict=pd.DataFrame.from_dict(aggregation.clusterPeriodDict), - first_time_stamp=first_time_stamp - ) - first_time_stamp = current_timestamp - #todo: this function can be deleted in future. It just serves as a control mechanism. - self._check_equal_timesteps_after_aggregation( - hours_per_period=aggregation.hoursPerPeriod, - hours_of_input_time_series=aggregation.timeSeries.__len__(), - periods_total_occurrence=aggregation.clusterPeriodNoOccur - ) - aggregated_time_series.extend(timeseries) - tsam_weighting.extend(weighting) - tsa_parameters.append( - { - "timesteps_per_period": aggregation.hoursPerPeriod, - "order": aggregation.clusterOrder, - "occurrences": aggregation.clusterPeriodNoOccur, - "timeindex": aggregation.timeIndex, - }) - if periods is not None: - periods.append(timeseries) - #todo does the two periods need to be floating? - timeindex = pd.DatetimeIndex(aggregated_time_series) - - timeindex = timeindex.append(pd.to_datetime([current_timestamp])) - del aggregated_time_series, weighting - self.tsam_weighting = tsam_weighting if not ( isinstance(timeindex, pd.DatetimeIndex) or isinstance(timeindex, type(None)) @@ -316,54 +273,6 @@ def get_period_duration(self, period): + 1 ) - def _set_aggregated_timeseries_and_weighting(self, - segmentation, - periods_total_occurrence, - aggregated_period_dict, - first_time_stamp): - previous_period = 0 - weighting = [] - aggregated_time_series = [] - current_timestamp = first_time_stamp - if segmentation: - for period, timestep, segmented_timestep in aggregated_period_dict.index: - if previous_period == period: - aggregated_time_series.append(current_timestamp) - else: - aggregated_time_series.append(current_timestamp) - previous_period = period - weighting.append(periods_total_occurrence[period] * segmented_timestep) - current_timestamp += pd.Timedelta(minutes=60 * segmented_timestep) - else: - for period, timestep in aggregated_period_dict.index: - if previous_period == period: - aggregated_time_series.append(current_timestamp) - else: - aggregated_time_series.append(current_timestamp) - previous_period = period - weighting.append(periods_total_occurrence[period]) - current_timestamp += pd.Timedelta(minutes=60) - aggregated_time_series = pd.DatetimeIndex(aggregated_time_series) - return aggregated_time_series, weighting, current_timestamp - - def _check_equal_timesteps_after_aggregation(self, - hours_per_period, - hours_of_input_time_series, - periods_total_occurrence): - if not sum(periods_total_occurrence.values()) * hours_per_period == hours_of_input_time_series: - # todo: prints can be deleted in future - print("aggregated timeseries has: " + str( - int( sum(periods_total_occurrence.values()) * hours_per_period)) + " timesteps") - print("unaggregated timeseries has: " + str(hours_of_input_time_series) + " timesteps") - print("therefore the occurrence of the typical periods for the objective weighting will be customized") - - customize_factor = hours_of_input_time_series / int(sum(periods_total_occurrence.values()) * hours_per_period) - print("customize factor is: "+(str(customize_factor))) - result_list = [float(occurrence) * customize_factor for occurrence in periods_total_occurrence] - periods_total_occurrence = result_list - return periods_total_occurrence - else: - return periods_total_occurrence def create_time_index( year: int = None, From 42c49ec06e6a45ab34656c0682a4e2f6b5ca49f2 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Tue, 31 Oct 2023 16:36:58 +0100 Subject: [PATCH 21/37] Move tsam weighting init into TSAM block --- src/oemof/solph/_models.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/src/oemof/solph/_models.py b/src/oemof/solph/_models.py index 2f06076d2..fb852970e 100644 --- a/src/oemof/solph/_models.py +++ b/src/oemof/solph/_models.py @@ -17,7 +17,6 @@ import itertools import logging import warnings -import pandas as pd from logging import getLogger from oemof.tools import debugging @@ -129,10 +128,6 @@ def __init__(self, energysystem, **kwargs): self.objective_weighting = kwargs.get( "objective_weighting", [1] * len(self.timeincrement) ) - if self.es.tsam_weighting is None: - self.tsam_weighting = [1] * len(self.timeincrement) - else: - self.tsam_weighting = self.es.tsam_weighting self._constraint_groups = type(self).CONSTRAINT_GROUPS + kwargs.get( "constraint_groups", [] @@ -469,8 +464,19 @@ def _add_parent_block_sets(self): # Set up disaggregated timesteps from original timeseries self.TSAM_MODE = False - if self.es.tsa_parameters is not None: + if self.es.tsa_parameters is None: + self.tsam_weighting = [1] * len(self.timeincrement) + else: self.TSAM_MODE = True + # Construct weighting from occurrences and order + self.tsam_weighting = list( + self.es.tsa_parameters[p]["occurrences"][k] + for p in self.PERIODS + for k in self.es.tsa_parameters[p]["order"] + for _ in range( + self.es.tsa_parameters[p]["timesteps_per_period"] + ) + ) self.CLUSTERS = po.Set( initialize=list( range( From d13b2310cd5af84615e4d5fe7416b46fd9343272 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Tue, 31 Oct 2023 16:41:16 +0100 Subject: [PATCH 22/37] Remove inter storage set to zero at start of each period --- src/oemof/solph/components/_generic_storage.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/src/oemof/solph/components/_generic_storage.py b/src/oemof/solph/components/_generic_storage.py index 4fe6e655c..8ff93fdc6 100644 --- a/src/oemof/solph/components/_generic_storage.py +++ b/src/oemof/solph/components/_generic_storage.py @@ -528,10 +528,6 @@ def _storage_content_bound_rule(block, n, t): # set the initial intra storage content # first timestep in intra storage is always zero for n in group: - # todo: Discussion, should the storage level of every period be zero - self.storage_content_inter[n, 0] = 0 - self.storage_content_inter[n, 0].fix() - for p, k in m.TYPICAL_CLUSTERS: self.storage_content_intra[n, p, k, 0] = 0 self.storage_content_intra[n, p, k, 0].fix() From 3f96b713610a23b80fa73d57cfc2deb4ddb47196 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 09:31:49 +0100 Subject: [PATCH 23/37] Apply tsam weighting to flow equations --- src/oemof/solph/flows/_non_convex_flow_block.py | 9 +++++++++ src/oemof/solph/flows/_simple_flow_block.py | 8 ++++++-- 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/src/oemof/solph/flows/_non_convex_flow_block.py b/src/oemof/solph/flows/_non_convex_flow_block.py index ea5cec56c..4d30e1301 100644 --- a/src/oemof/solph/flows/_non_convex_flow_block.py +++ b/src/oemof/solph/flows/_non_convex_flow_block.py @@ -355,6 +355,8 @@ def _shutdown_costs(self): if m.flows[i, o].nonconvex.shutdown_costs[0] is not None: shutdown_costs += sum( self.shutdown[i, o, t] + * m.objective_weighting[t] + * m.tsam_weighting[t] * m.flows[i, o].nonconvex.shutdown_costs[t] for t in m.TIMESTEPS ) @@ -365,6 +367,7 @@ def _shutdown_costs(self): self.shutdown[i, o, t] * m.flows[i, o].nonconvex.shutdown_costs[t] * m.objective_weighting[t] + * m.tsam_weighting[t] * ((1 + m.discount_rate) ** -m.es.periods_years[p]) for p, t in m.TIMEINDEX ) @@ -390,6 +393,8 @@ def _activity_costs(self): activity_costs += sum( self.status[i, o, t] * m.flows[i, o].nonconvex.activity_costs[t] + * m.objective_weighting[t] + * m.tsam_weighting[t] for t in m.TIMESTEPS ) else: @@ -399,6 +404,7 @@ def _activity_costs(self): self.status[i, o, t] * m.flows[i, o].nonconvex.activity_costs[t] * m.objective_weighting[t] + * m.tsam_weighting[t] * ((1 + m.discount_rate) ** -m.es.periods_years[p]) for p, t in m.TIMEINDEX ) @@ -423,6 +429,8 @@ def _inactivity_costs(self): if m.flows[i, o].nonconvex.inactivity_costs[0] is not None: inactivity_costs += sum( (1 - self.status[i, o, t]) + * m.objective_weighting[t] + * m.tsam_weighting[t] * m.flows[i, o].nonconvex.inactivity_costs[t] for t in m.TIMESTEPS ) @@ -433,6 +441,7 @@ def _inactivity_costs(self): (1 - self.status[i, o, t]) * m.flows[i, o].nonconvex.inactivity_costs[t] * m.objective_weighting[t] + * m.tsam_weighting[t] * ((1 + m.discount_rate) ** -m.es.periods_years[p]) for p, t in m.TIMEINDEX ) diff --git a/src/oemof/solph/flows/_simple_flow_block.py b/src/oemof/solph/flows/_simple_flow_block.py index 7cebbc1b1..009e65eb1 100644 --- a/src/oemof/solph/flows/_simple_flow_block.py +++ b/src/oemof/solph/flows/_simple_flow_block.py @@ -215,7 +215,9 @@ def _flow_full_load_time_max_rule(model): """Rule definition for build action of max. sum flow constraint.""" for inp, out in self.FULL_LOAD_TIME_MAX_FLOWS: lhs = sum( - m.flow[inp, out, p, ts] * m.timeincrement[ts] + m.flow[inp, out, p, ts] + * m.timeincrement[ts] + * m.tsam_weighting[ts] for p, ts in m.TIMEINDEX ) rhs = ( @@ -235,7 +237,9 @@ def _flow_full_load_time_min_rule(model): """Rule definition for build action of min. sum flow constraint.""" for inp, out in self.FULL_LOAD_TIME_MIN_FLOWS: lhs = sum( - m.flow[inp, out, p, ts] * m.timeincrement[ts] + m.flow[inp, out, p, ts] + * m.timeincrement[ts] + * m.tsam_weighting[ts] for p, ts in m.TIMEINDEX ) rhs = ( From c898de03e1159b4b76a081d0dcf5d2d64dc68bba Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 09:47:16 +0100 Subject: [PATCH 24/37] Add whatsnew file for TSAM feature --- docs/whatsnew/v0-6-0.rst | 41 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 docs/whatsnew/v0-6-0.rst diff --git a/docs/whatsnew/v0-6-0.rst b/docs/whatsnew/v0-6-0.rst new file mode 100644 index 000000000..dfd183705 --- /dev/null +++ b/docs/whatsnew/v0-6-0.rst @@ -0,0 +1,41 @@ +v0.6.0 (????) +------------- + +API changes +########### + +* New attribute `tsa_paramters` of `oemof.solph.EnergySystem` + +New features +############ + +* Allow usage of aggregated timeseries by + [TSAM](https://github.com/FZJ-IEK3-VSA/tsam) module. Original timeseries is + reflected by weighting typical periods (output from TSAM) accordingly. + Thus, variable costs, full load hours, startup/shutdown costs are weighted + accordingly to occurrences of typical period in original timeseries. + Additionally, storage equations are adapted in order to respect + long-term/saisonal energy storage. + +Documentation +############# + +Bug fixes +######### + +* Fix objective weighting for startup and shutdown costs in case of no + multi-period approach. + +Testing +####### + +* Added test for comparison of TSAM mode versus original mode. + Including testing of storage behaviour, variable and investment costs. + +Contributors +############ + +* Hendrik Huyskens +* Maximilian Hillen +* Patrik Schönfeldt +* Johannes Kochems From c4061614d1f4af4ff14230277ebcc13a6c8b2920 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 10:18:50 +0100 Subject: [PATCH 25/37] Fix TSAM weighting factors --- src/oemof/solph/_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/oemof/solph/_models.py b/src/oemof/solph/_models.py index fb852970e..a39f44fdc 100644 --- a/src/oemof/solph/_models.py +++ b/src/oemof/solph/_models.py @@ -472,7 +472,7 @@ def _add_parent_block_sets(self): self.tsam_weighting = list( self.es.tsa_parameters[p]["occurrences"][k] for p in self.PERIODS - for k in self.es.tsa_parameters[p]["order"] + for k in range(len(self.es.tsa_parameters[p]["occurrences"])) for _ in range( self.es.tsa_parameters[p]["timesteps_per_period"] ) From e4434e904daacf70af5732951e2b09579c6a3bc4 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 10:22:16 +0100 Subject: [PATCH 26/37] Add test for full load hours and variable costs in TSAM mode --- ...test_full_load_hours_and_variable_costs.py | 132 ++++++++++++++++++ 1 file changed, 132 insertions(+) create mode 100644 tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py diff --git a/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py b/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py new file mode 100644 index 000000000..ce1b7b893 --- /dev/null +++ b/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py @@ -0,0 +1,132 @@ +# -*- coding: utf-8 -*- + +""" +General description: +--------------------- +This tests shall prove, that TSAM weighting is reflected in full load hours and variable costs. + +The example models the following energy system: + + input/output bel + | | + Source |--------->| + | | + Sink |<---------| + +whereby source is defined with full load hours. + +This file is part of project oemof (github.com/oemof/oemof). It's copyrighted +by the contributors recorded in the version control history of the file, +available from its original location oemof/tests/test_scripts/test_solph/ +test_storage_investment/test_storage_investment.py + +SPDX-License-Identifier: MIT +""" + +import logging +import pandas as pd +import pytest + +from oemof.tools import logger +from oemof import solph + +########################################################################## +# Initialize the energy system and read/calculate necessary parameters +########################################################################## + +logger.define_logging() +logging.info("Initialize the energy system") + +tindex_original = pd.date_range("2022-01-01", periods=8, freq="H") +tindex = pd.date_range("2022-01-01", periods=4, freq="H") + +energysystem = solph.EnergySystem( + timeindex=tindex, + timeincrement=[1] * len(tindex), + periods=[tindex], + tsa_parameters=[ + { + "timesteps_per_period": 2, + "order": [1, 1, 1, 0], + "occurrences": {0: 1, 1: 3}, + "timeindex": tindex_original, + }, + ], + infer_last_interval=False, +) + +########################################################################## +# Create oemof objects +########################################################################## + +logging.info("Create oemof objects") + +# create electricity bus +bel = solph.Bus(label="electricity") +energysystem.add(bel) + +# create fixed source object representing wind power plants +source = solph.components.Source( + label="source", + outputs={ + bel: solph.Flow( + full_load_time_min=0.8, + full_load_time_max=0.8, + nominal_value=100, + variable_costs=[0.1, 0.2, 0.3, 0.4] + ) + }, +) + +# create simple sink object representing the electrical demand +excess = solph.components.Sink( + label="excess", + inputs={bel: solph.Flow()}, +) + +energysystem.add(source, excess) + +########################################################################## +# Optimise the energy system +########################################################################## + +logging.info("Optimise the energy system") + +# initialise the operational model +om = solph.Model(energysystem) + +# if tee_switch is true solver messages will be displayed +logging.info("Solve the optimization problem") +om.solve(solver="cbc", solve_kwargs={"tee": True}) + +########################################################################## +# Check and plot the results +########################################################################## + +# check if the new result object is working for custom components +results = solph.processing.results(om) +meta_results = solph.processing.meta_results(om) + +# Concatenate flows: +flows = pd.concat([flow["sequences"] for flow in results.values()], axis=1) +flows.columns = [ + f"{oemof_tuple[0]}-{oemof_tuple[1]}" for oemof_tuple in results.keys() +] + + +def test_weighted_full_load_hours(): + """Tests if full load hours are weighted accordingly to TSAM occurrences""" + assert flows["source-electricity"].sum() == pytest.approx(80) + + +def test_weighted_variable_costs(): + """Tests if variable costs are weighted accordingly to TSAM occurrences""" + assert meta_results["objective"] == ( + flows["source-electricity"][6] * 0.1 + + flows["source-electricity"][7] * 0.2 + + ( + flows["source-electricity"][0] * 0.3 + + flows["source-electricity"][1] * 0.4 + ) + * 3 + ) From 7df596e69f49423b51e5fb5c3060a442579353b8 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 11:03:52 +0100 Subject: [PATCH 27/37] Remove testing examples --- ...mize_all_technologies_using_mp_and_tsam.py | 450 ------------------ ...vest_tsam_integrated_into_energy_system.py | 356 -------------- 2 files changed, 806 deletions(-) delete mode 100644 examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py delete mode 100644 examples/storage_investment/v7_no_invest_tsam_integrated_into_energy_system.py diff --git a/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py b/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py deleted file mode 100644 index 8b6ae57fd..000000000 --- a/examples/storage_investment/v6_no_invest_optimize_all_technologies_using_mp_and_tsam.py +++ /dev/null @@ -1,450 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -General description -------------------- -This example shows how to perform a capacity optimization for -an energy system with storage. The following energy system is modeled: - -.. code-block:: text - - input/output bgas bel - | | | - | | | - wind(FixedSource) |------------------>| - | | | - pv(FixedSource) |------------------>| - | | | - gas_resource |--------->| | - (Commodity) | | | - | | | - demand(Sink) |<------------------| - | | | - | | | - pp_gas(Converter) |<---------| | - |------------------>| - | | | - storage(Storage) |<------------------| - |------------------>| - -The example exists in four variations. The following parameters describe -the main setting for the optimization variation 1: - -- optimize wind, pv, gas_resource and storage -- set investment cost for wind, pv and storage -- set gas price for kWh - -Results show an installation of wind and the use of the gas resource. -A renewable energy share of 51% is achieved. - -.. tip:: - - Have a look at different parameter settings. There are four variations - of this example in the same folder. - -Code ----- -Download source code: :download:`v1_invest_optimize_all_technologies.py ` - -.. dropdown:: Click to display code - - .. literalinclude:: /../examples/storage_investment/v1_invest_optimize_all_technologies.py - :language: python - :lines: 80- - -Data ----- -Download data: :download:`storage_investment.csv ` - - -Installation requirements -------------------------- - -This example requires oemof.solph (v0.5.x), install by: - -.. code:: bash - - pip install oemof.solph[examples] - - -License -------- -`MIT license `_ - -""" - -############################################################################### -# Imports -############################################################################### - -import logging -import os -import pprint as pp -import warnings -import tsam.timeseriesaggregation as tsam -import matplotlib.pyplot as plt - -import pandas as pd -from oemof.tools import economics -from oemof.tools import logger -from oemof.solph import views - -from oemof import solph -def check_equal_timesteps_after_aggregation(hours_per_period : int, - hours_of_input_time_series: int, - periods_total_occurrence: list): - - if not sum(periods_total_occurrence) * hours_per_period == 8760: - #todo: prints can be deleted in future - print("aggregated timeseries has: " + str(int(sum(periods_total_occurrence) * hours_per_period)) + " timesteps") - print("unaggregated timeseries has: " + str(hours_of_input_time_series) + " timesteps") - print("therefore the occurrence of the typical periods for the objective weighting will be customized") - customize_factor = hours_of_input_time_series / int(sum(periods_total_occurrence) * hours_per_period) - result_list = [float(occurrence) * customize_factor for occurrence in periods_total_occurrence] - periods_total_occurrence = result_list - return periods_total_occurrence - else: - return periods_total_occurrence - -def set_aggregated_timeseries_and_objective_weighting(segmentation, - periods_total_occurrence, - aggregated_period_dict, - first_time_stamp): - previous_period = 0 - objective_weighting = [] - aggregated_time_series = [] - current_timestamp=first_time_stamp - if segmentation: - for period, timestep, segmented_timestep in aggregated_period_dict.index: - if previous_period == period: - aggregated_time_series.append(current_timestamp) - else: - aggregated_time_series.append(current_timestamp) - previous_period = period - objective_weighting.append(periods_total_occurrence[period] * segmented_timestep) - current_timestamp += pd.Timedelta(minutes=60 * segmented_timestep) - else: - for period, timestep in aggregated_period_dict.index: - if previous_period == period: - aggregated_time_series.append(current_timestamp) - else: - aggregated_time_series.append(current_timestamp) - previous_period = period - objective_weighting.append(periods_total_occurrence[period]) - current_timestamp += pd.Timedelta(minutes=60) - #time series have to be extended by one, to fit into form of energysystem iput - aggregated_time_series.append(current_timestamp) - aggregated_time_series = pd.DatetimeIndex(aggregated_time_series) - return aggregated_time_series, objective_weighting - -def main(): - # Read data file - filename = os.path.join(os.getcwd(), "storage_investment.csv") - try: - data = pd.read_csv(filename) - except FileNotFoundError: - msg = "Data file not found: {0}. Only one value used!" - warnings.warn(msg.format(filename), UserWarning) - data = pd.DataFrame( - {"pv": [0.3, 0.5], "wind": [0.6, 0.8], "demand_el": [500, 600]} - ) - - data = pd.concat([data, data], ignore_index=True) - data["wind"].iloc[8760 - 24 : 8760] = 0 - data["wind"].iloc[8760 * 2 - 24 : 8760] = 0 - data["pv"].iloc[8760 - 24 : 8760] = 0 - data["pv"].iloc[8760 * 2 - 24 : 8760] = 0 - - # add a season without electricity production to simulate the possible advantage using a seasonal storages - data["wind"].iloc[2920 * 4:5 * 2920 + 1] = 0 - data["wind"].iloc[2920: 2 * 2920 + 1] = 0 - data["pv"].iloc[2920:2 * 2920 + 1] = 0 - data["pv"].iloc[2920 * 4:5 * 2920 + 1] = 0 - - ########################################################################## - # Initialize the energy system and read/calculate necessary parameters - ########################################################################## - - logger.define_logging() - logging.info("Initialize the energy system") - - t1 = pd.date_range("2022-01-01", periods=8760, freq="H") - t2 = pd.date_range("2023-01-01", periods=8760, freq="H") - tindex = t1.append(t2) - - data.index = tindex - del data["timestep"] - - typical_periods = 60 - hours_per_period = 24 - segmentation = False - if segmentation: - print("segmentation hasn't been added so far") - - - else: - aggregation1 = tsam.TimeSeriesAggregation( - timeSeries=data.iloc[:8760], - noTypicalPeriods=typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod="hierarchical", - sortValues=True, - rescaleClusterPeriods=False, - extremePeriodMethod="replace_cluster_center", - addPeakMin=["wind", "pv"], - representationMethod="durationRepresentation", - ) - - aggregation2 = tsam.TimeSeriesAggregation( - timeSeries=data.iloc[8760:], - noTypicalPeriods=typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod="hierarchical", - sortValues=True, - rescaleClusterPeriods=False, - extremePeriodMethod="replace_cluster_center", - addPeakMin=["wind", "pv"], - representationMethod="durationRepresentation", - ) - - - aggregation1.createTypicalPeriods() - aggregation2.createTypicalPeriods() - if False: - periods_total_occurrence1 = [ - (aggregation1.clusterOrder == typical_period_name).sum() for typical_period_name in - aggregation1.clusterPeriodIdx] - periods_total_occurrence2 = [ - (aggregation2.clusterOrder == typical_period_name).sum() for typical_period_name in - aggregation2.clusterPeriodIdx] - else: - periods_total_occurrence1 = aggregation1.clusterPeriodNoOccur - periods_total_occurrence2 = aggregation1.clusterPeriodNoOccur - periods_total_occurrence1 = check_equal_timesteps_after_aggregation(hours_per_period=hours_per_period, - hours_of_input_time_series=t1.__len__(), - periods_total_occurrence=periods_total_occurrence1 - ) - periods_total_occurrence2 = check_equal_timesteps_after_aggregation(hours_per_period = hours_per_period, - hours_of_input_time_series = t2.__len__(), - periods_total_occurrence=periods_total_occurrence2 - ) - #before timeseries generation was based on freq="H" (hourly), now you have to set the number of minutes of one timestep - t1_agg, objective_weighting1 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, - periods_total_occurrence = periods_total_occurrence1, - aggregated_period_dict=pd.DataFrame.from_dict(aggregation1.clusterPeriodDict), - first_time_stamp=pd.to_datetime(t1[0]) - ) - t2_agg, objective_weighting2 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, - periods_total_occurrence = periods_total_occurrence2, - aggregated_period_dict=pd.DataFrame.from_dict(aggregation2.clusterPeriodDict), - first_time_stamp=pd.to_datetime(t1[0]) - ) - #objective_weighting = objective_weighting1 + objective_weighting2 - objective_weighting = objective_weighting1 - - #tindex_agg = t1_agg.append(t2_agg) - tindex_agg = t1_agg - - #todo aggregation1.clusterPeriodNoOccur besser zum objective weighting nutzen - energysystem = solph.EnergySystem( - timeindex=tindex_agg, - #timeincrement=[1] * len(tindex_agg), - periods=[t1_agg, - #t2_agg - ], - tsa_parameters=[ - { - "timesteps_per_period": aggregation1.hoursPerPeriod, - "order": aggregation1.clusterOrder, - "occurrences": aggregation1.clusterPeriodNoOccur, - "timeindex": aggregation1.timeIndex, - } - ], - infer_last_interval=False, - ) - - electricity_price = 100 - - ########################################################################## - # Create oemof objects - ########################################################################## - - logging.info("Create oemof objects") - - - # create electricity bus - bel = solph.Bus(label="electricity") - - energysystem.add( bel) - - # create excess component for the electricity bus to allow overproduction - excess = solph.components.Sink( - label="excess_bel", inputs={bel: solph.Flow()} - ) - - # create source object representing the gas commodity (annual limit) - elect_resource = solph.components.Source( - label="electricity_source", outputs={bel: solph.Flow(variable_costs=electricity_price)} - ) - - wind_profile = pd.concat( - [ - aggregation1.typicalPeriods["wind"], - #aggregation2.typicalPeriods["wind"], - ], - ignore_index=True, - ) - wind_profile.iloc[-24:] = 0 - - # create fixed source object representing wind power plants - wind = solph.components.Source( - label="wind", - outputs={ - bel: solph.Flow( - fix=wind_profile, - nominal_value=1500000 - ) - }, - ) - - pv_profile = pd.concat( - [aggregation1.typicalPeriods["pv"], - #aggregation2.typicalPeriods["pv"] - ], - ignore_index=True, - ) - pv_profile.iloc[-24:] = 0 - - # create fixed source object representing pv power plants - pv = solph.components.Source( - label="pv", - outputs={ - bel: solph.Flow( - fix=pd.concat( - [ - aggregation1.typicalPeriods["pv"], - #aggregation2.typicalPeriods["pv"], - ], - ignore_index=True, - ), - nominal_value=900000 - ) - }, - ) - - # create simple sink object representing the electrical demand - demand = solph.components.Sink( - label="demand", - inputs={ - bel: solph.Flow( - fix=pd.concat( - [ - aggregation1.typicalPeriods["demand_el"], - #aggregation2.typicalPeriods["demand_el"], - ], - ignore_index=True, - ), - nominal_value=0.05, - ) - }, - ) - - # create storage object representing a battery - storage = solph.components.GenericStorage( - label="storage", - nominal_storage_capacity=3000000, - initial_storage_level=0, - inputs={bel: solph.Flow(variable_costs=0.0, nominal_value=2000)}, - outputs={bel: solph.Flow(nominal_value=2000)}, - loss_rate=0.000, - inflow_conversion_factor=1, - outflow_conversion_factor=1, - ) - - energysystem.add(excess, elect_resource, wind, pv, demand, storage) - - ########################################################################## - # Optimise the energy system - ########################################################################## - - logging.info("Optimise the energy system") - - # initialise the operational model - om = solph.Model(energysystem - ) - - # if tee_switch is true solver messages will be displayed - logging.info("Solve the optimization problem") - om.solve(solver="cbc", solve_kwargs={"tee": True}) - - ########################################################################## - # Check and plot the results - ########################################################################## - - # check if the new result object is working for custom components - results = solph.processing.results(om) - print(results) - - # Concatenate flows: - flows = pd.concat([flow["sequences"] for flow in results.values()], axis=1) - flows.columns = [ - f"{oemof_tuple[0]}-{oemof_tuple[1]}" for oemof_tuple in results.keys() - ] - print(flows) - - electricity_bus = solph.views.node(results, "electricity") - - meta_results = solph.processing.meta_results(om) - pp.pprint(meta_results) - - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(storage, None)]["sequences"] / storage.nominal_storage_capacity - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - plt.show() - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(wind, bel)]["sequences"] - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - plt.show() - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(pv, bel)]["sequences"] - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - plt.show() - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(bel, demand)]["sequences"] - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - plt.show() - my_results = electricity_bus["period_scalars"] - - # installed capacity of storage in GWh - my_results["storage_invest_GWh"] = ( - results[(storage, None)]["period_scalars"]["invest"] / 1e6 - ) - - # installed capacity of wind power plant in MW - my_results["wind_invest_MW"] = ( - results[(wind, bel)]["period_scalars"]["invest"] / 1e3 - ) - - # resulting renewable energy share - print( - "res_share:", - ( - 1 - - results[(pp_gas, bel)]["sequences"].sum() - / results[(bel, demand)]["sequences"].sum() - ), - ) - - pp.pprint(my_results) - - -if __name__ == "__main__": - main() diff --git a/examples/storage_investment/v7_no_invest_tsam_integrated_into_energy_system.py b/examples/storage_investment/v7_no_invest_tsam_integrated_into_energy_system.py deleted file mode 100644 index 66b2c66ec..000000000 --- a/examples/storage_investment/v7_no_invest_tsam_integrated_into_energy_system.py +++ /dev/null @@ -1,356 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -General description -------------------- -This example shows how to perform a capacity optimization for -an energy system with storage. The following energy system is modeled: - -.. code-block:: text - - input/output bgas bel - | | | - | | | - wind(FixedSource) |------------------>| - | | | - pv(FixedSource) |------------------>| - | | | - gas_resource |--------->| | - (Commodity) | | | - | | | - demand(Sink) |<------------------| - | | | - | | | - pp_gas(Converter) |<---------| | - |------------------>| - | | | - storage(Storage) |<------------------| - |------------------>| - -The example exists in four variations. The following parameters describe -the main setting for the optimization variation 1: - -- optimize wind, pv, gas_resource and storage -- set investment cost for wind, pv and storage -- set gas price for kWh - -Results show an installation of wind and the use of the gas resource. -A renewable energy share of 51% is achieved. - -.. tip:: - - Have a look at different parameter settings. There are four variations - of this example in the same folder. - -Code ----- -Download source code: :download:`v1_invest_optimize_all_technologies.py ` - -.. dropdown:: Click to display code - - .. literalinclude:: /../examples/storage_investment/v1_invest_optimize_all_technologies.py - :language: python - :lines: 80- - -Data ----- -Download data: :download:`storage_investment.csv ` - - -Installation requirements -------------------------- - -This example requires oemof.solph (v0.5.x), install by: - -.. code:: bash - - pip install oemof.solph[examples] - - -License -------- -`MIT license `_ - -""" - -############################################################################### -# Imports -############################################################################### - -import logging -import os -import pprint as pp -import warnings -import tsam.timeseriesaggregation as tsam -import matplotlib.pyplot as plt - -import pandas as pd -from oemof.tools import economics -from oemof.tools import logger -from oemof.solph import views - -from oemof import solph - - -def main(): - # Read data file - filename = os.path.join(os.getcwd(), "storage_investment.csv") - try: - data = pd.read_csv(filename) - except FileNotFoundError: - msg = "Data file not found: {0}. Only one value used!" - warnings.warn(msg.format(filename), UserWarning) - data = pd.DataFrame( - {"pv": [0.3, 0.5], "wind": [0.6, 0.8], "demand_el": [500, 600]} - ) - - data = pd.concat([data, data], ignore_index=True) - data["wind"].iloc[8760 - 24 : 8760] = 0 - data["wind"].iloc[8760 * 2 - 24 : 8760] = 0 - data["pv"].iloc[8760 - 24 : 8760] = 0 - data["pv"].iloc[8760 * 2 - 24 : 8760] = 0 - - # add a season without electricity production to simulate the possible advantage using a seasonal storages - # for the first perido - data["wind"].iloc[2920: 2 * 2920 + 1] = 0 - data["pv"].iloc[2920:2 * 2920 + 1] = 0 - - ########################################################################## - # Initialize the energy system and read/calculate necessary parameters - ########################################################################## - - logger.define_logging() - logging.info("Initialize the energy system") - #todo: right now, tsam only determines the timeincrement right, when you pick the - #first periods last timestamp next to the second periods first timestep - #2022-31-12-23:00 --> 2023-01-01-00:00 , than timeincrement in between is equal to 1 - #todo add initial storage level in new periods is equal to zero? - t1 = pd.date_range("2022-01-01", periods=8760, freq="H") - t2 = pd.date_range("2023-01-01", periods=8760, freq="H") - tindex = t1.append(t2) - - data.index = tindex - del data["timestep"] - - typical_periods = 10 - hours_per_period = 24 - segmentation = False - if segmentation: - print("segmentation hasn't been added so far") - - - else: - aggregation1 = tsam.TimeSeriesAggregation( - timeSeries=data.iloc[:8760], - noTypicalPeriods=typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod="hierarchical", - sortValues=True, - segmentation=False, - rescaleClusterPeriods=False, - extremePeriodMethod="replace_cluster_center", - addPeakMin=["wind", "pv"], - representationMethod="durationRepresentation", - ) - - aggregation2 = tsam.TimeSeriesAggregation( - timeSeries=data.iloc[8760:], - noTypicalPeriods=typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod="hierarchical", - sortValues=True, - segmentation=False, - rescaleClusterPeriods=False, - extremePeriodMethod="replace_cluster_center", - addPeakMin=["wind", "pv"], - representationMethod="durationRepresentation", - ) - - energysystem = solph.EnergySystem( - tsam_aggregations=[aggregation1, aggregation2], - infer_last_interval=False, - ) - - electricity_price = 100 - - ########################################################################## - # Create oemof objects - ########################################################################## - - logging.info("Create oemof objects") - - - # create electricity bus - bel = solph.Bus(label="electricity") - - energysystem.add( bel) - - # create excess component for the electricity bus to allow overproduction - excess = solph.components.Sink( - label="excess_bel", inputs={bel: solph.Flow()} - ) - - # create source object representing the gas commodity (annual limit) - elect_resource = solph.components.Source( - label="electricity_source", outputs={bel: solph.Flow(variable_costs=electricity_price)} - ) - - wind_profile = pd.concat( - [ - aggregation1.typicalPeriods["wind"], - aggregation2.typicalPeriods["wind"], - ], - ignore_index=True, - ) - wind_profile.iloc[-24:] = 0 - - # create fixed source object representing wind power plants - wind = solph.components.Source( - label="wind", - outputs={ - bel: solph.Flow( - fix=wind_profile, - nominal_value=1500000 - ) - }, - ) - - pv_profile = pd.concat( - [aggregation1.typicalPeriods["pv"], - aggregation2.typicalPeriods["pv"] - ], - ignore_index=True, - ) - pv_profile.iloc[-24:] = 0 - - # create fixed source object representing pv power plants - if False: - pv = solph.components.Source( - label="pv", - outputs={ - bel: solph.Flow( - fix=pd.concat( - [ - aggregation1.typicalPeriods["pv"], - aggregation2.typicalPeriods["pv"], - ], - ignore_index=True, - ), - nominal_value=900000 - ) - }, - ) - - # create simple sink object representing the electrical demand - demand = solph.components.Sink( - label="demand", - inputs={ - bel: solph.Flow( - fix=pd.concat( - [ - aggregation1.typicalPeriods["demand_el"], - aggregation2.typicalPeriods["demand_el"], - ], - ignore_index=True, - ), - nominal_value=0.05, - ) - }, - ) - - # create storage object representing a battery - storage = solph.components.GenericStorage( - label="storage", - nominal_storage_capacity=3000000, - initial_storage_level=0, - inputs={bel: solph.Flow(variable_costs=0.0, nominal_value=2000)}, - outputs={bel: solph.Flow(nominal_value=2000)}, - loss_rate=0.001, - inflow_conversion_factor=1, - outflow_conversion_factor=1, - ) - if False: - energysystem.add(excess, elect_resource, wind, pv, demand, storage) - else: - energysystem.add(excess, elect_resource, wind, demand, storage) - - ########################################################################## - # Optimise the energy system - ########################################################################## - - logging.info("Optimise the energy system") - - # initialise the operational model - om = solph.Model(energysystem - ) - - # if tee_switch is true solver messages will be displayed - logging.info("Solve the optimization problem") - om.solve(solver="cbc", solve_kwargs={"tee": True}) - - ########################################################################## - # Check and plot the results - ########################################################################## - - # check if the new result object is working for custom components - results = solph.processing.results(om) - print(results) - - # Concatenate flows: - flows = pd.concat([flow["sequences"] for flow in results.values()], axis=1) - flows.columns = [ - f"{oemof_tuple[0]}-{oemof_tuple[1]}" for oemof_tuple in results.keys() - ] - print(flows) - - electricity_bus = solph.views.node(results, "electricity") - - meta_results = solph.processing.meta_results(om) - pp.pprint(meta_results) - - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(storage, None)]["sequences"] / storage.nominal_storage_capacity - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - plt.show() - - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(wind, bel)]["sequences"] - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - ax.set_title("Elect. from Wind") - plt.show() - if False: - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(pv, bel)]["sequences"] - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - ax.set_title("Elect. from PV") - plt.show() - - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(bel, demand)]["sequences"] - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - ax.set_title("Demand") - plt.show() - - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(elect_resource, bel)]["sequences"] - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - ax.set_title("Elect. from Grid") - plt.show() - my_results = electricity_bus["period_scalars"] - - - pp.pprint(my_results) - - -if __name__ == "__main__": - main() From 689af8fbf3c4ad53dc419c3e2c5fe64c27aabc66 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 11:14:18 +0100 Subject: [PATCH 28/37] remove basic TSAm example; move TSAM invest example into TSAM test folder --- ...hnologies_but_storage_using_mp_and_tsam.py | 439 - examples/tsam/basic_example.csv | 8761 ----------------- examples/tsam/basic_tsam_example.py | 271 - ...ize_all_technologies_using_mp_and_tsam.py} | 2 +- 4 files changed, 1 insertion(+), 9472 deletions(-) delete mode 100644 examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py delete mode 100644 examples/tsam/basic_example.csv delete mode 100644 examples/tsam/basic_tsam_example.py rename examples/{storage_investment/v6_invest_optimize_all_technologies_using_mp_and_tsam.py => tsam/invest_optimize_all_technologies_using_mp_and_tsam.py} (99%) diff --git a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py b/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py deleted file mode 100644 index 33a197437..000000000 --- a/examples/storage_investment/v5_invest_optimize_all_technologies_but_storage_using_mp_and_tsam.py +++ /dev/null @@ -1,439 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -General description -------------------- -This example shows how to perform a capacity optimization for -an energy system with storage. The following energy system is modeled: - -.. code-block:: text - - input/output bgas bel - | | | - | | | - wind(FixedSource) |------------------>| - | | | - pv(FixedSource) |------------------>| - | | | - gas_resource |--------->| | - (Commodity) | | | - | | | - demand(Sink) |<------------------| - | | | - | | | - pp_gas(Converter) |<---------| | - |------------------>| - | | | - storage(Storage) |<------------------| - |------------------>| - -The example exists in four variations. The following parameters describe -the main setting for the optimization variation 1: - -- optimize wind, pv, gas_resource and storage -- set investment cost for wind, pv and storage -- set gas price for kWh - -Results show an installation of wind and the use of the gas resource. -A renewable energy share of 51% is achieved. - -.. tip:: - - Have a look at different parameter settings. There are four variations - of this example in the same folder. - -Code ----- -Download source code: :download:`v1_invest_optimize_all_technologies.py ` - -.. dropdown:: Click to display code - - .. literalinclude:: /../examples/storage_investment/v1_invest_optimize_all_technologies.py - :language: python - :lines: 80- - -Data ----- -Download data: :download:`storage_investment.csv ` - - -Installation requirements -------------------------- - -This example requires oemof.solph (v0.5.x), install by: - -.. code:: bash - - pip install oemof.solph[examples] - - -License -------- -`MIT license `_ - -""" - -############################################################################### -# Imports -############################################################################### - -import logging -import os -import pprint as pp -import warnings -import tsam.timeseriesaggregation as tsam -import matplotlib.pyplot as plt - -import pandas as pd -from oemof.tools import economics -from oemof.tools import logger -from oemof.solph import views - -from oemof import solph -def check_equal_timesteps_after_aggregation(hours_per_period : int, - hours_of_input_time_series: int, - periods_total_occurrence: list): - - if not sum(periods_total_occurrence) * hours_per_period == 8760: - #todo: prints can be deleted in future - print("aggregated timeseries has: " + str(int(sum(periods_total_occurrence) * hours_per_period)) + " timesteps") - print("unaggregated timeseries has: " + str(hours_of_input_time_series) + " timesteps") - print("therefore the occurrence of the typical periods for the objective weighting will be customized") - customize_factor = hours_of_input_time_series / int(sum(periods_total_occurrence) * hours_per_period) - result_list = [float(occurrence) * customize_factor for occurrence in periods_total_occurrence] - periods_total_occurrence = result_list - return periods_total_occurrence - else: - return periods_total_occurrence - -def set_aggregated_timeseries_and_objective_weighting(segmentation, - periods_total_occurrence, - aggregated_period_dict, - first_time_stamp): - previous_period = 0 - objective_weighting = [] - aggregated_time_series = [] - current_timestamp=first_time_stamp - if segmentation: - for period, timestep, segmented_timestep in aggregated_period_dict.index: - if previous_period == period: - aggregated_time_series.append(current_timestamp) - else: - aggregated_time_series.append(current_timestamp) - previous_period = period - objective_weighting.append(periods_total_occurrence[period] * segmented_timestep) - current_timestamp += pd.Timedelta(minutes=60 * segmented_timestep) - else: - for period, timestep in aggregated_period_dict.index: - if previous_period == period: - aggregated_time_series.append(current_timestamp) - else: - aggregated_time_series.append(current_timestamp) - previous_period = period - objective_weighting.append(periods_total_occurrence[period]) - current_timestamp += pd.Timedelta(minutes=60) - aggregated_time_series = pd.DatetimeIndex(aggregated_time_series) - return aggregated_time_series, objective_weighting - -def main(): - # Read data file - filename = os.path.join(os.getcwd(), "storage_investment.csv") - try: - data = pd.read_csv(filename) - except FileNotFoundError: - msg = "Data file not found: {0}. Only one value used!" - warnings.warn(msg.format(filename), UserWarning) - data = pd.DataFrame( - {"pv": [0.3, 0.5], "wind": [0.6, 0.8], "demand_el": [500, 600]} - ) - - data = pd.concat([data, data], ignore_index=True) - data["wind"].iloc[8760 - 24 : 8760] = 0 - data["wind"].iloc[8760 * 2 - 24 : 8760] = 0 - data["pv"].iloc[8760 - 24 : 8760] = 0 - data["pv"].iloc[8760 * 2 - 24 : 8760] = 0 - - ########################################################################## - # Initialize the energy system and read/calculate necessary parameters - ########################################################################## - - logger.define_logging() - logging.info("Initialize the energy system") - - t1 = pd.date_range("2022-01-01", periods=8760, freq="H") - t2 = pd.date_range("2033-01-01", periods=8760, freq="H") - tindex = t1.append(t2) - - data.index = tindex - del data["timestep"] - - typical_periods = 40 - hours_per_period = 24 - segmentation = False - if segmentation: - print("segmentation hasn't been added so far") - - - else: - aggregation1 = tsam.TimeSeriesAggregation( - timeSeries=data.iloc[:8760], - noTypicalPeriods=typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod="k_means", - sortValues=False, - rescaleClusterPeriods=False, - extremePeriodMethod="replace_cluster_center", - addPeakMin=["wind", "pv"], - representationMethod="durationRepresentation", - ) - - aggregation2 = tsam.TimeSeriesAggregation( - timeSeries=data.iloc[8760:], - noTypicalPeriods=typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod="hierarchical", - sortValues=False, - rescaleClusterPeriods=False, - extremePeriodMethod="replace_cluster_center", - addPeakMin=["wind", "pv"], - representationMethod="durationRepresentation", - ) - - aggregation1.createTypicalPeriods() - aggregation2.createTypicalPeriods() - - periods_total_occurrence1 = [ - (aggregation1.clusterOrder == typical_period_name).sum() for typical_period_name in - aggregation1.clusterPeriodIdx] - periods_total_occurrence2 = [ - (aggregation2.clusterOrder == typical_period_name).sum() for typical_period_name in - aggregation2.clusterPeriodIdx] - periods_total_occurrence1 = check_equal_timesteps_after_aggregation(hours_per_period=hours_per_period, - hours_of_input_time_series=t1.__len__(), - periods_total_occurrence=periods_total_occurrence1 - ) - periods_total_occurrence2 = check_equal_timesteps_after_aggregation(hours_per_period = hours_per_period, - hours_of_input_time_series = t2.__len__(), - periods_total_occurrence=periods_total_occurrence2 - ) - #before timeseries generation was based on freq="H" (hourly), now you have to set the number of minutes of one timestep - t1_agg, objective_weighting1 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, - periods_total_occurrence = periods_total_occurrence1, - aggregated_period_dict=pd.DataFrame.from_dict(aggregation1.clusterPeriodDict), - first_time_stamp=pd.to_datetime(t1[0]) - ) - t2_agg, objective_weighting2 = set_aggregated_timeseries_and_objective_weighting(segmentation=segmentation, - periods_total_occurrence = periods_total_occurrence2, - aggregated_period_dict=pd.DataFrame.from_dict(aggregation2.clusterPeriodDict), - first_time_stamp=pd.to_datetime(t2[0]) - ) - objective_weighting = objective_weighting1 + objective_weighting2 - - t2_agg = t2_agg.append(pd.DatetimeIndex([t2_agg[-1] + pd.DateOffset(hours=1)])) - tindex_agg = t1_agg.append(t2_agg) - - energysystem = solph.EnergySystem( - timeindex=tindex_agg, - periods=[t1_agg, t2_agg], - tsa_parameters=[ - { - "timesteps_per_period": aggregation1.hoursPerPeriod, - "order": aggregation1.clusterOrder, - "occurrences": aggregation1.clusterPeriodNoOccur, - "timeindex": aggregation1.timeIndex, - }, - { - "timesteps_per_period": aggregation2.hoursPerPeriod, - "order": aggregation2.clusterOrder, - "occurrences": aggregation2.clusterPeriodNoOccur, - "timeindex": aggregation2.timeIndex, - }, - ], - infer_last_interval=False, - ) - - price_gas = 5 - - # If the period is one year the equivalent periodical costs (epc) of an - # investment are equal to the annuity. Use oemof's economic tools. - epc_wind = economics.annuity(capex=1000, n=20, wacc=0.05) - epc_pv = economics.annuity(capex=1000, n=20, wacc=0.05) - epc_storage = economics.annuity(capex=1000, n=20, wacc=0.05) - - ########################################################################## - # Create oemof objects - ########################################################################## - - logging.info("Create oemof objects") - # create natural gas bus - bgas = solph.Bus(label="natural_gas") - - # create electricity bus - bel = solph.Bus(label="electricity") - - energysystem.add(bgas, bel) - - # create excess component for the electricity bus to allow overproduction - excess = solph.components.Sink( - label="excess_bel", inputs={bel: solph.Flow()} - ) - - # create source object representing the gas commodity (annual limit) - gas_resource = solph.components.Source( - label="rgas", outputs={bgas: solph.Flow(variable_costs=price_gas)} - ) - - wind_profile = pd.concat( - [ - aggregation1.typicalPeriods["wind"], - aggregation2.typicalPeriods["wind"], - ], - ignore_index=True, - ) - wind_profile.iloc[-24:] = 0 - - # create fixed source object representing wind power plants - wind = solph.components.Source( - label="wind", - outputs={ - bel: solph.Flow( - fix=wind_profile, - investment=solph.Investment(ep_costs=epc_wind, lifetime=10), - ) - }, - ) - - pv_profile = pd.concat( - [aggregation1.typicalPeriods["pv"], aggregation2.typicalPeriods["pv"]], - ignore_index=True, - ) - pv_profile.iloc[-24:] = 0 - - # create fixed source object representing pv power plants - pv = solph.components.Source( - label="pv", - outputs={ - bel: solph.Flow( - fix=pd.concat( - [ - aggregation1.typicalPeriods["pv"], - aggregation2.typicalPeriods["pv"], - ], - ignore_index=True, - ), - investment=solph.Investment(ep_costs=epc_pv, lifetime=10), - ) - }, - ) - - # create simple sink object representing the electrical demand - demand = solph.components.Sink( - label="demand", - inputs={ - bel: solph.Flow( - fix=pd.concat( - [ - aggregation1.typicalPeriods["demand_el"], - aggregation2.typicalPeriods["demand_el"], - ], - ignore_index=True, - ), - nominal_value=1, - ) - }, - ) - - # create simple Converter object representing a gas power plant - pp_gas = solph.components.Converter( - label="pp_gas", - inputs={bgas: solph.Flow()}, - outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=0)}, - conversion_factors={bel: 0.58}, - ) - - # create storage object representing a battery - storage = solph.components.GenericStorage( - label="storage", - nominal_storage_capacity=5000, - inputs={bel: solph.Flow(variable_costs=0.0001)}, - outputs={bel: solph.Flow()}, - loss_rate=0.01, - lifetime_inflow=10, - lifetime_outflow=10, - invest_relation_input_capacity=1 / 6, - invest_relation_output_capacity=1 / 6, - inflow_conversion_factor=1, - outflow_conversion_factor=0.8, - ) - - energysystem.add(excess, gas_resource, wind, pv, demand, pp_gas, storage) - - ########################################################################## - # Optimise the energy system - ########################################################################## - - logging.info("Optimise the energy system") - - # initialise the operational model - om = solph.Model(energysystem, - objective_weighting= objective_weighting - ) - - # if tee_switch is true solver messages will be displayed - logging.info("Solve the optimization problem") - om.solve(solver="cbc", solve_kwargs={"tee": True}) - - ########################################################################## - # Check and plot the results - ########################################################################## - - # check if the new result object is working for custom components - results = solph.processing.results(om) - print(results) - - # Concatenate flows: - flows = pd.concat([flow["sequences"] for flow in results.values()], axis=1) - flows.columns = [ - f"{oemof_tuple[0]}-{oemof_tuple[1]}" for oemof_tuple in results.keys() - ] - print(flows) - - electricity_bus = solph.views.node(results, "electricity") - - meta_results = solph.processing.meta_results(om) - pp.pprint(meta_results) - - fig, ax = plt.subplots(figsize=(10, 5)) - storage_results = results[(storage, None)]["sequences"] / storage.nominal_storage_capacity - storage_results .plot( - ax=ax, kind="line", drawstyle="steps-post" - ) - plt.show() - - my_results = electricity_bus["period_scalars"] - - # installed capacity of storage in GWh - my_results["storage_invest_GWh"] = ( - results[(storage, None)]["period_scalars"]["invest"] / 1e6 - ) - - # installed capacity of wind power plant in MW - my_results["wind_invest_MW"] = ( - results[(wind, bel)]["period_scalars"]["invest"] / 1e3 - ) - - # resulting renewable energy share - print( - "res_share:", - ( - 1 - - results[(pp_gas, bel)]["sequences"].sum() - / results[(bel, demand)]["sequences"].sum() - ), - ) - - pp.pprint(my_results) - - -if __name__ == "__main__": - main() diff --git a/examples/tsam/basic_example.csv b/examples/tsam/basic_example.csv deleted file mode 100644 index 20afccc6c..000000000 --- a/examples/tsam/basic_example.csv +++ /dev/null @@ -1,8761 +0,0 @@ -timestep,demand_el,pv,wind -1,209643.3415769,0,0.10702 -2,207497.2007709,0,0.074873 -3,200108.0148989,0,0.055654 -4,191892.6802004,0,0.065059 -5,185717.3331069,0,0.085573 -6,180672.7483735,0,0.096229 -7,172683.5661471,0,0.11794 -8,170048.1975444,0,0.15953 -9,171132.8063389,0,0.23717 -10,179532.7553002,0.062188,0.28515 -11,189155.7737532,0.15426,0.35724 -12,201026.4708567,0.16016,0.46354 -13,208466.425651,0.1036,0.56932 -14,207718.7378864,0.084862,0.68999 -15,205443.3670963,0.052101,0.79517 -16,206255.669853,0.014673,0.86893 -17,217240.2184947,0,0.92278 -18,232798.5854994,0,0.95895 -19,237321.6349401,0,0.97963 -20,232387.8187645,0,0.99052 -21,224306.3294067,0,0.99626 -22,219280.2060996,0,0.99859 -23,223701.7176957,0,0.99933 -24,213926.3924759,0,0.99923 -25,201834.1582569,0,0.99874 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-8757,228880.1477697,0,0.7667 -8758,226554.0080573,0,0.7523 -8759,236343.1793469,0,0.73739 -8760,235840.1054805,0,0.70744 diff --git a/examples/tsam/basic_tsam_example.py b/examples/tsam/basic_tsam_example.py deleted file mode 100644 index 11effbd8e..000000000 --- a/examples/tsam/basic_tsam_example.py +++ /dev/null @@ -1,271 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -General description -------------------- - -A basic example to show how to model a simple energy system with oemof.solph. - -The following energy system is modeled: - -.. code-block:: text - - input/output bgas bel - | | | - | | | - wind(FixedSource) |------------------>| - | | | - pv(FixedSource) |------------------>| - | | | - rgas(Commodity) |--------->| | - | | | - demand(Sink) |<------------------| - | | | - | | | - pp_gas(Transformer) |<---------| | - |------------------>| - | | | - storage(Storage) |<------------------| - |------------------>| - - -Data ----- -basic_example.csv - - -Installation requirements -------------------------- -This example requires oemof.solph (v0.5.x), install by: - - pip install oemof.solph[examples] - -License -------- -`MIT license `_ -""" -########################################################################### -# imports -########################################################################### - -import logging -import os -import pprint as pp -import warnings - -import pandas as pd -from oemof.tools import logger - -from oemof.solph import EnergySystem -from oemof.solph import Model -from oemof.solph import buses -from oemof.solph import components as cmp -from oemof.solph import flows -from oemof.solph import helpers -from oemof.solph import processing -from oemof.solph import views -from oemof.solph._helpers import aggregate_time_series - -def main(): - # ************************************************************************* - # ********** PART 1 - Define and optimise the energy system *************** - # ************************************************************************* - - # Read data file - - filename = os.path.join(os.getcwd(), "basic_example.csv") - try: - data = pd.read_csv(filename) - except FileNotFoundError: - msg = "Data file not found: {0}. Only one value used!" - warnings.warn(msg.format(filename), UserWarning) - data = pd.DataFrame({"pv": [0.3], "wind": [0.6], "demand_el": [500]}) - - # First there need to be a pre-processing, because our aggregate_time_series function only - # works with timeseries. - df = pd.DataFrame(data) - start_time = pd.Timestamp.now().replace(year=2023, month=1, day=1,hour=0, minute=0, second=0, microsecond=0) - df["datetime"] = start_time + pd.to_timedelta(df["timestep"] - 1, unit="H") - df.set_index("datetime", inplace=True) - - data_dict_to_aggregate = {"wind" : {"timeseries" : df["wind"], "weighted_factor" : 1}, - "pv" : {"timeseries" : df["pv"], "weighted_factor" : 1}, - "demand_el" : {"timeseries" : df["demand_el"], "weighted_factor" : 1}, - } - - number_of_time_steps_per_periods =24 - number_of_segments_per_period = 24*4 - number_of_typical_periods = 12 - segmentation = False - if False: - determine_aggregation_parameters(data_dict_to_aggregate = data_dict_to_aggregate, - number_of_time_steps_per_periods = number_of_time_steps_per_periods, - segmentation = False) - if segmentation: - data_dict_aggregated, objective_weighting, clusterClass = aggregate_time_series(data_dict_to_aggregate = data_dict_to_aggregate, - number_of_typical_periods = number_of_typical_periods, - number_of_time_steps_per_periods = number_of_time_steps_per_periods, - number_of_segments_per_period= number_of_segments_per_period, - segmentation = segmentation - ) - else: - data_dict_aggregated, objective_weighting, clusterClass = aggregate_time_series(data_dict_to_aggregate = data_dict_to_aggregate, - number_of_typical_periods = number_of_typical_periods, - number_of_time_steps_per_periods = number_of_time_steps_per_periods, - segmentation = segmentation - ) - - solver = "cbc" # 'glpk', 'gurobi',.... - debug = False # Set number_of_timesteps to 3 to get a readable lp-file. - solver_verbose = False # show/hide solver output - - # initiate the logger (see the API docs for more information) - logger.define_logging( - logfile="oemof_example.log", - screen_level=logging.INFO, - file_level=logging.INFO, - ) - - logging.info("Initialize the energy system") - date_time_index = data_dict_aggregated["demand_el"]["timeseries"].index - - energysystem = EnergySystem( - timeindex=date_time_index, - infer_last_interval=False, - - ) - - ########################################################################## - # Create oemof object - ########################################################################## - - logging.info("Create oemof objects") - - # The bus objects were assigned to variables which makes it easier to - # connect components to these buses (see below). - - # create natural gas bus - bgas = buses.Bus(label="natural_gas") - - # create electricity bus - bel = buses.Bus(label="electricity") - - # adding the buses to the energy system - energysystem.add(bgas, bel) - - # create excess component for the electricity bus to allow overproduction - energysystem.add(cmp.Sink(label="excess_bel", inputs={bel: flows.Flow()})) - - # create source object representing the gas commodity (annual limit) - energysystem.add( - cmp.Source( - label="rgas", - outputs={bgas: flows.Flow()}, - ) - ) - - # create fixed source object representing wind power plants - energysystem.add( - cmp.Source( - label="wind", - outputs={bel: flows.Flow(fix=data_dict_aggregated["wind"]["timeseries"], - - nominal_value=1000000)}, - ) - ) - - # create fixed source object representing pv power plants - energysystem.add( - cmp.Source( - label="pv", - outputs={bel: flows.Flow(fix=data_dict_aggregated["pv"]["timeseries"], nominal_value=582000)}, - ) - ) - - # create simple sink object representing the electrical demand - energysystem.add( - cmp.Sink( - label="demand", - inputs={bel: flows.Flow(fix=data_dict_aggregated["demand_el"]["timeseries"], nominal_value=1)}, - ) - ) - - # create simple transformer object representing a gas power plant - energysystem.add( - cmp.Transformer( - label="pp_gas", - inputs={bgas: flows.Flow()}, - outputs={bel: flows.Flow(nominal_value=10e10, variable_costs=50)}, - conversion_factors={bel: 0.58}, - ) - ) - - ########################################################################## - # Optimise the energy system and plot the results - ########################################################################## - - logging.info("Optimise the energy system") - - # initialise the operational model - model = Model(energysystem, objective_weighting= objective_weighting) - - # This is for debugging only. It is not(!) necessary to solve the problem - # and should be set to False to save time and disc space in normal use. For - # debugging the timesteps should be set to 3, to increase the readability - # of the lp-file. - if debug: - filename = os.path.join( - helpers.extend_basic_path("lp_files"), "basic_example.lp" - ) - logging.info("Store lp-file in {0}.".format(filename)) - model.write(filename, io_options={"symbolic_solver_labels": True}) - - # if tee_switch is true solver messages will be displayed - logging.info("Solve the optimization problem") - model.solve(solver=solver, solve_kwargs={"tee": solver_verbose}) - - logging.info("Store the energy system with the results.") - - # The processing module of the outputlib can be used to extract the results - # from the model transfer them into a homogeneous structured dictionary. - - # add results to the energy system to make it possible to store them. - energysystem.results["main"] = processing.results(model) - energysystem.results["meta"] = processing.meta_results(model) - - # The default path is the '.oemof' folder in your $HOME directory. - # The default filename is 'es_dump.oemof'. - # You can omit the attributes (as None is the default value) for testing - # cases. You should use unique names/folders for valuable results to avoid - # overwriting. - - # store energy system with results - energysystem.dump(dpath=None, filename=None) - - # ************************************************************************* - # ********** PART 2 - Processing the results ****************************** - # ************************************************************************* - - logging.info("**** The script can be divided into two parts here.") - logging.info("Restore the energy system and the results.") - energysystem = EnergySystem() - energysystem.restore(dpath=None, filename=None) - - # define an alias for shorter calls below (optional) - results = energysystem.results["main"] - - electricity_bus = views.node(results, "electricity") - - # print the solver results - print("********* Meta results *********") - pp.pprint(energysystem.results["meta"]) - print("") - - # print the sums of the flows around the electricity bus - print("********* Main results *********") - print(electricity_bus["sequences"].sum(axis=0)) - -if __name__ == "__main__": - main() - - diff --git a/examples/storage_investment/v6_invest_optimize_all_technologies_using_mp_and_tsam.py b/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py similarity index 99% rename from examples/storage_investment/v6_invest_optimize_all_technologies_using_mp_and_tsam.py rename to examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py index 89be65446..7e023b20a 100644 --- a/examples/storage_investment/v6_invest_optimize_all_technologies_using_mp_and_tsam.py +++ b/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py @@ -92,7 +92,7 @@ def main(): # Read data file - filename = os.path.join(os.getcwd(), "storage_investment.csv") + filename = os.path.join(os.getcwd(), "../storage_investment/storage_investment.csv") try: data = pd.read_csv(filename) except FileNotFoundError: From ee89e6985b436ca59a15dee6886ab7cd78106913 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 11:23:51 +0100 Subject: [PATCH 29/37] Remove TSAM function from helpers --- src/oemof/solph/_helpers.py | 153 ------------------------------------ 1 file changed, 153 deletions(-) diff --git a/src/oemof/solph/_helpers.py b/src/oemof/solph/_helpers.py index ac9324d14..fc245aca2 100644 --- a/src/oemof/solph/_helpers.py +++ b/src/oemof/solph/_helpers.py @@ -19,12 +19,7 @@ from oemof.tools import debugging -from tsam.timeseriesaggregation import TimeSeriesAggregation -from tsam.hyperparametertuning import HyperTunedAggregations -from typing import Dict, List -import copy -import pandas as pd def check_node_object_for_missing_attribute(obj, attribute): """Raises a predefined warning if object does not have attribute. @@ -50,151 +45,3 @@ def warn_if_missing_attribute(obj, attribute): msg.format(attribute, obj.label, type(obj)), debugging.SuspiciousUsageWarning, ) - -def aggregate_time_series( - data_dict_to_aggregate: Dict[str, Dict[str, any]], - number_of_typical_periods: int = 7, - number_of_time_steps_per_periods : int = 24, - number_of_segments_per_period :int = 16, - segmentation : bool = False, - cluster_method : str = 'hierarchical', - sort_values : bool = True, - minutes_per_time_step : int = 60, - ): - """Aggregate timeseries with the tsam-package. - Firstly controls format of input data. - Secondly does time series aggregation with tsam. - Thirdly changes datetime index of dataframe of results. - - Arguments - --------- - #todo: Few explanations are copied 1to1 from tsam. Here has to be correctly linked to source! - data_dict_to_aggregate : Dictionary of a dictionary with a time series and a weighted factor. The "timeseries" has - to have the datetime as index and the timeseries parameters as columns. Format: - data_dict_to_aggregate = {"name" : {"timeseries" : pd.Series, "weighted_factor" : float} - number_of_typical_periods : (int) Number of typical Periods - equivalent to the number of clusters. - number_of_time_steps_per_periods: (int) Value which defines the length of a cluster period. - number_of_segments_per_period: (int) Number of segments in which the typical periods should be subdivided - - equivalent to the number of inner-period clusters. - segmentation: (bool) Decision variable if typical periods should be subdivided - cluster_method: (str) Chosen clustering method - Options are: - * 'averaging' - * 'k_means' - * 'k_medoids' - * 'k_maxoids' - * 'hierarchical' - * 'adjacent_periods' - sort_values : (bool) #todo understand what this variable does - """ - entry_format_timeseries = None - for entry_name, entry_data in data_dict_to_aggregate.items(): - if not isinstance(entry_data, dict): - raise ValueError(f"Entry '{entry_name}' should have a dictionary as its value.") - - required_keys = ["timeseries", "weighted_factor"] - missing_keys = [key for key in required_keys if key not in entry_data] - - if entry_format_timeseries is None: - entry_format_timeseries = entry_data["timeseries"].index - else: - if not entry_format_timeseries.equals(entry_data["timeseries"].index): - raise ValueError(f"TimeSeries Format of at least'{entry_name}' is unequal to: {previous_entry_name}") - previous_entry_name = entry_name - if missing_keys: - raise ValueError(f"Entry '{entry_name}' is missing the following keys: {', '.join(missing_keys)}") - - if not isinstance(entry_data["timeseries"], pd.Series): - raise ValueError(f"Timeseries for entry '{entry_name}' should be a pd.Series.") - - if not all(isinstance(timestamp, (int, float)) for timestamp in entry_data["timeseries"]): - raise ValueError(f"Timeseries for entry '{entry_name}' should contain only numeric timestamps.") - - if not isinstance(entry_data["weighted_factor"], (float, int)): - raise ValueError(f"Weighted factor for entry '{entry_name}' should be a float.") - - if segmentation: - if number_of_segments_per_period > number_of_time_steps_per_periods: - ValueError(f"Number of segments per period equal to'{number_of_segments_per_period}' has to be smaller than number of time steps per periods equal to {number_of_time_steps_per_periods}") - - hours_per_period = number_of_time_steps_per_periods * minutes_per_time_step / 60 - time_series_data = pd.DataFrame() - weighted_factors_dict = {} - for key, value in data_dict_to_aggregate.items(): - if 'timeseries' in value: - time_series_data[key] = value['timeseries'] - if 'weighted_factor' in value: - weighted_factors_dict[key] = value['weighted_factor'] - if segmentation: - clusterClass = TimeSeriesAggregation( - timeSeries=time_series_data, - noTypicalPeriods=number_of_typical_periods, - segmentation=segmentation, - noSegments=number_of_segments_per_period, - hoursPerPeriod=hours_per_period, - clusterMethod=cluster_method, - sortValues=sort_values, - weightDict=weighted_factors_dict - ) - data = pd.DataFrame.from_dict(clusterClass.clusterPeriodDict) - else: - clusterClass = TimeSeriesAggregation( - timeSeries=time_series_data, - noTypicalPeriods=number_of_typical_periods, - hoursPerPeriod=hours_per_period, - clusterMethod=cluster_method, - sortValues=sort_values, - weightDict=weighted_factors_dict, - ) - data = pd.DataFrame.from_dict(clusterClass.clusterPeriodDict) - - hours_of_time_series = entry_format_timeseries.__len__() - periods_name = clusterClass.clusterPeriodIdx - periods_order = clusterClass.clusterOrder - periods_total_occurrence = [ - (periods_order == typical_period_name).sum() for typical_period_name in periods_name - ] - start_date = entry_format_timeseries[0] - if not sum(periods_total_occurrence) * hours_per_period == 8760: - - print("aggregated timeseries has: " +str(int(sum(periods_total_occurrence) * hours_per_period))+ " timesteps") - print("unaggregated timeseries has: " +str(hours_of_time_series)+ " timesteps") - print("therefore the occurrence of the typical periods for the objective weighting will be customized") - customize_factor = hours_of_time_series / int(sum(periods_total_occurrence) * hours_per_period) - result_list = [float(occurrence) * customize_factor for occurrence in periods_total_occurrence] - periods_total_occurrence = result_list - - - current_timestamp = pd.to_datetime(start_date) - previous_period = 0 - objective_weighting = [] - extended_time_series = [] - minute_resolution_of_one_hour = 60 - if segmentation: - for period, timestep, segmented_timestep in data.index: - if previous_period == period: - extended_time_series.append(current_timestamp) - else: - extended_time_series.append(current_timestamp) - previous_period = period - objective_weighting.append(periods_total_occurrence[period] * segmented_timestep) - current_timestamp += pd.Timedelta(minutes=minute_resolution_of_one_hour * segmented_timestep) - else: - for period, timestep in data.index: - if previous_period == period: - extended_time_series.append(current_timestamp) - else: - extended_time_series.append(current_timestamp) - previous_period = period - objective_weighting.append(periods_total_occurrence[period]) - current_timestamp += pd.Timedelta(minutes=minute_resolution_of_one_hour) - - data.index = extended_time_series - data_dict_aggregated = {} - for name in data: - if len(data[name]) == len(objective_weighting): - data_dict_aggregated[name] = { "timeseries" : data[name]} - else: - raise ValueError(f"Aggregated timeseries for: '{data[name]}' has a different length as " - f"objective weighting list") - return data_dict_aggregated, objective_weighting, clusterClass From fa59306708eaec19853cfcd6fc15a141c3ecb24b Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 11:30:41 +0100 Subject: [PATCH 30/37] Fix linting errors --- src/oemof/solph/components/_generic_storage.py | 5 ++--- .../test_full_load_hours_and_variable_costs.py | 3 ++- .../test_storage_invest_tsam_integration.py | 14 ++++++++++---- .../test_tsam/test_storage_tsam_integration.py | 10 +++++++--- 4 files changed, 21 insertions(+), 11 deletions(-) diff --git a/src/oemof/solph/components/_generic_storage.py b/src/oemof/solph/components/_generic_storage.py index 8ff93fdc6..ed87c8871 100644 --- a/src/oemof/solph/components/_generic_storage.py +++ b/src/oemof/solph/components/_generic_storage.py @@ -1458,8 +1458,6 @@ def _storage_investvar_bound_rule(block, n, p): i = {n: [i for i in n.inputs][0] for n in group} o = {n: [o for o in n.outputs][0] for n in group} - reduced_periods_timesteps = [(p, t) for (p, t) in m.TIMEINDEX if t > 0] - # Handle unit lifetimes def _total_storage_capacity_rule(block): """Rule definition for determining total installed @@ -1990,7 +1988,8 @@ def _storage_inter_minimum_level_rule(block): rule=_storage_inter_minimum_level_rule ) else: - # Set the lower bound of the storage content if the attribute exists + # Set the lower bound of the storage content if the + # attribute exists self.min_storage_content = Constraint( self.MIN_INVESTSTORAGES, m.TIMEINDEX, diff --git a/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py b/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py index ce1b7b893..b769c5f59 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py +++ b/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py @@ -3,7 +3,8 @@ """ General description: --------------------- -This tests shall prove, that TSAM weighting is reflected in full load hours and variable costs. +This tests shall prove, that TSAM weighting is reflected in full load hours and +variable costs. The example models the following energy system: diff --git a/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py b/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py index e793fbc64..1025f6d74 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py +++ b/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py @@ -17,10 +17,12 @@ -An initial SOC of zero leads to infeasible solution, as last inter SOC has to match first inter SOC. +An initial SOC of zero leads to infeasible solution, as last inter SOC has to +match first inter SOC. Following equations have to be fulfilled: F_{el,st}[0] = F_{el,st}[6] -SOC_{init} * discharge + F_{el,st}[0] = \sum_{i=1}^{n=5}F_{st,el}[i]/eff_{out}/(1 - discharge)^i +SOC_{init} * discharge + F_{el,st}[0] = +\sum_{i=1}^{n=5}F_{st,el}[i]/eff_{out}/(1 - discharge)^i F_{el,st}[6] = (SOC_{init} + F_{el,st}[5]/eff_{out}) / (1 - discharge) This file is part of project oemof (github.com/oemof/oemof). It's copyrighted @@ -150,11 +152,15 @@ def test_storage_investment(): """Make sure that max SOC investment equals max load""" - assert results[storage, None]["period_scalars"]["invest"].iloc[0] == pytest.approx(first_input) + assert results[storage, None]["period_scalars"]["invest"].iloc[ + 0 + ] == pytest.approx(first_input) def test_storage_input(): - assert flows["electricity-storage"][0] == pytest.approx((first_input - 0.99 * init_soc) / 0.9) + assert flows["electricity-storage"][0] == pytest.approx( + (first_input - 0.99 * init_soc) / 0.9 + ) assert flows["electricity-storage"][1] == 0 assert flows["electricity-storage"][2] == 0 assert flows["electricity-storage"][3] == 0 diff --git a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py index ec2e87aa9..c808473fa 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py +++ b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py @@ -17,10 +17,12 @@ -An initial SOC of zero leads to infeasible solution, as last inter SOC has to match first inter SOC. +An initial SOC of zero leads to infeasible solution, as last inter SOC has to +match first inter SOC. Following equations have to be fulfilled: F_{el,st}[0] = F_{el,st}[6] -SOC_{init} * discharge + F_{el,st}[0] = \sum_{i=1}^{n=5}F_{st,el}[i]/eff_{out}/(1 - discharge)^i +SOC_{init} * discharge + F_{el,st}[0] = +\sum_{i=1}^{n=5}F_{st,el}[i]/eff_{out}/(1 - discharge)^i F_{el,st}[6] = (SOC_{init} + F_{el,st}[5]/eff_{out}) / (1 - discharge) This file is part of project oemof (github.com/oemof/oemof). It's copyrighted @@ -147,7 +149,9 @@ def test_storage_input(): - assert flows["electricity-storage"][0] == pytest.approx((first_input - 0.99 * init_soc) / 0.9) + assert flows["electricity-storage"][0] == pytest.approx( + (first_input - 0.99 * init_soc) / 0.9 + ) assert flows["electricity-storage"][1] == 0 assert flows["electricity-storage"][2] == 0 assert flows["electricity-storage"][3] == 0 From d80fe6a3a54a290c7840d1da447d22ecc9dcd7e2 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 11:33:37 +0100 Subject: [PATCH 31/37] Fix black errors --- .../invest_optimize_all_technologies_using_mp_and_tsam.py | 6 ++++-- .../test_tsam/test_full_load_hours_and_variable_costs.py | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py b/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py index 7e023b20a..4b0d7cb51 100644 --- a/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py +++ b/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py @@ -92,7 +92,9 @@ def main(): # Read data file - filename = os.path.join(os.getcwd(), "../storage_investment/storage_investment.csv") + filename = os.path.join( + os.getcwd(), "../storage_investment/storage_investment.csv" + ) try: data = pd.read_csv(filename) except FileNotFoundError: @@ -306,7 +308,7 @@ def main(): # if tee_switch is true solver messages will be displayed logging.info("Solve the optimization problem") - om.write('my_model.lp', io_options={'symbolic_solver_labels': True}) + om.write("my_model.lp", io_options={"symbolic_solver_labels": True}) om.solve(solver="cbc", solve_kwargs={"tee": True}) ########################################################################## diff --git a/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py b/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py index b769c5f59..c44eeae24 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py +++ b/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py @@ -74,7 +74,7 @@ full_load_time_min=0.8, full_load_time_max=0.8, nominal_value=100, - variable_costs=[0.1, 0.2, 0.3, 0.4] + variable_costs=[0.1, 0.2, 0.3, 0.4], ) }, ) From 251542bb171320378f8f50c4902ce6fd2acc96b1 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 12:56:46 +0100 Subject: [PATCH 32/37] Fix non-TSAM storage balance equation --- .../solph/components/_generic_storage.py | 22 ++++++++++--------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/src/oemof/solph/components/_generic_storage.py b/src/oemof/solph/components/_generic_storage.py index ed87c8871..93c3c0dc3 100644 --- a/src/oemof/solph/components/_generic_storage.py +++ b/src/oemof/solph/components/_generic_storage.py @@ -1458,6 +1458,8 @@ def _storage_investvar_bound_rule(block, n, p): i = {n: [i for i in n.inputs][0] for n in group} o = {n: [o for o in n.outputs][0] for n in group} + reduced_periods_timesteps = [(p, t) for (p, t) in m.TIMEINDEX if t > 0] + # Handle unit lifetimes def _total_storage_capacity_rule(block): """Rule definition for determining total installed @@ -1720,27 +1722,27 @@ def _storage_balance_first_rule(block, n): def _storage_balance_rule(block, n, p, t): """ - Rule definition for the storage balance of every storage n and - every timestep. + Rule definition for the storage balance of every storage n + for every time step but the first. """ expr = 0 - expr += block.storage_content[n, t + 1] + expr += block.storage_content[n, t] expr += ( - -block.storage_content[n, t] + -block.storage_content[n, t - 1] * (1 - n.loss_rate[t]) ** m.timeincrement[t] ) expr += ( n.fixed_losses_relative[t] - * n.nominal_storage_capacity + * self.total[n, p] * m.timeincrement[t] ) expr += n.fixed_losses_absolute[t] * m.timeincrement[t] expr += ( - -m.flow[i[n], n, p, t] * n.inflow_conversion_factor[t] - ) * m.timeincrement[t] + -m.flow[i[n], n, p, t] * n.inflow_conversion_factor[t] + ) * m.timeincrement[t] expr += ( - m.flow[n, o[n], p, t] / n.outflow_conversion_factor[t] - ) * m.timeincrement[t] + m.flow[n, o[n], p, t] / n.outflow_conversion_factor[t] + ) * m.timeincrement[t] return expr == 0 def _intra_storage_balance_rule(block, n, p, k, g): @@ -1771,7 +1773,7 @@ def _intra_storage_balance_rule(block, n, p, k, g): if not m.TSAM_MODE: self.balance = Constraint( - self.INVESTSTORAGES, m.TIMEINDEX, rule=_storage_balance_rule + self.INVESTSTORAGES, reduced_periods_timesteps, rule=_storage_balance_rule ) else: self.intra_balance = Constraint( From ef75a5ae97ac391ac18299e3f3c046ce52014ba0 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 12:57:03 +0100 Subject: [PATCH 33/37] Fix obejective weighting in case no timeincrement is set --- src/oemof/solph/_models.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/oemof/solph/_models.py b/src/oemof/solph/_models.py index a39f44fdc..7284575d6 100644 --- a/src/oemof/solph/_models.py +++ b/src/oemof/solph/_models.py @@ -126,7 +126,10 @@ def __init__(self, energysystem, **kwargs): self.timeincrement = kwargs.get("timeincrement", self.es.timeincrement) self.objective_weighting = kwargs.get( - "objective_weighting", [1] * len(self.timeincrement) + "objective_weighting", + [1] * len(self.timeincrement) + if self.timeincrement is not None + else [1], ) self._constraint_groups = type(self).CONSTRAINT_GROUPS + kwargs.get( From 565540b235015974cbf2e92978d138616612d7fe Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 13:08:56 +0100 Subject: [PATCH 34/37] Fix linting error --- src/oemof/solph/components/_generic_storage.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/oemof/solph/components/_generic_storage.py b/src/oemof/solph/components/_generic_storage.py index 93c3c0dc3..3c87909c5 100644 --- a/src/oemof/solph/components/_generic_storage.py +++ b/src/oemof/solph/components/_generic_storage.py @@ -1738,11 +1738,11 @@ def _storage_balance_rule(block, n, p, t): ) expr += n.fixed_losses_absolute[t] * m.timeincrement[t] expr += ( - -m.flow[i[n], n, p, t] * n.inflow_conversion_factor[t] - ) * m.timeincrement[t] + -m.flow[i[n], n, p, t] * n.inflow_conversion_factor[t] + ) * m.timeincrement[t] expr += ( - m.flow[n, o[n], p, t] / n.outflow_conversion_factor[t] - ) * m.timeincrement[t] + m.flow[n, o[n], p, t] / n.outflow_conversion_factor[t] + ) * m.timeincrement[t] return expr == 0 def _intra_storage_balance_rule(block, n, p, k, g): @@ -1773,7 +1773,9 @@ def _intra_storage_balance_rule(block, n, p, k, g): if not m.TSAM_MODE: self.balance = Constraint( - self.INVESTSTORAGES, reduced_periods_timesteps, rule=_storage_balance_rule + self.INVESTSTORAGES, + reduced_periods_timesteps, + rule=_storage_balance_rule, ) else: self.intra_balance = Constraint( From aabc649280ae4aaddca3c4e96e761f10a3fe19e0 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 13:09:06 +0100 Subject: [PATCH 35/37] Fix latex in docstring --- .../test_solph/test_tsam/test_storage_tsam_integration.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py index c808473fa..8adfe4b38 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py +++ b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py @@ -20,10 +20,10 @@ An initial SOC of zero leads to infeasible solution, as last inter SOC has to match first inter SOC. Following equations have to be fulfilled: -F_{el,st}[0] = F_{el,st}[6] -SOC_{init} * discharge + F_{el,st}[0] = -\sum_{i=1}^{n=5}F_{st,el}[i]/eff_{out}/(1 - discharge)^i -F_{el,st}[6] = (SOC_{init} + F_{el,st}[5]/eff_{out}) / (1 - discharge) +:math:`F_{el,st}[0] = F_{el,st}[6]` +:math:`SOC_{init} * discharge + F_{el,st}[0] =` +:math:`\sum_{i=1}^{n=5}F_{st,el}[i]/eff_{out}/(1 - discharge)^i` +:math:`F_{el,st}[6] = (SOC_{init} + F_{el,st}[5]/eff_{out}) / (1 - discharge)` This file is part of project oemof (github.com/oemof/oemof). It's copyrighted by the contributors recorded in the version control history of the file, From 752312398b60eae98dc040da3746944dfa225571 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 13:22:15 +0100 Subject: [PATCH 36/37] Fix latex in docstring --- .../test_tsam/test_storage_invest_tsam_integration.py | 10 +++++----- .../test_tsam/test_storage_tsam_integration.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py b/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py index 1025f6d74..05211acc9 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py +++ b/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -""" +r""" General description: --------------------- @@ -20,10 +20,10 @@ An initial SOC of zero leads to infeasible solution, as last inter SOC has to match first inter SOC. Following equations have to be fulfilled: -F_{el,st}[0] = F_{el,st}[6] -SOC_{init} * discharge + F_{el,st}[0] = -\sum_{i=1}^{n=5}F_{st,el}[i]/eff_{out}/(1 - discharge)^i -F_{el,st}[6] = (SOC_{init} + F_{el,st}[5]/eff_{out}) / (1 - discharge) +:math:`F_{el,st}[0] = F_{el,st}[6]` +:math:`SOC_{init} * discharge + F_{el,st}[0] =` +:math:`\sum_{i=1}^{n=5}F_{st,el}[i]/eff_{out}/(1 - discharge)^i` +:math:`F_{el,st}[6] = (SOC_{init} + F_{el,st}[5]/eff_{out}) / (1 - discharge)` This file is part of project oemof (github.com/oemof/oemof). It's copyrighted by the contributors recorded in the version control history of the file, diff --git a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py index 8adfe4b38..7a9132187 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py +++ b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -""" +r""" General description: --------------------- From 995cdb2e03d00ad9c563f3cf5fc3327bc2831fb8 Mon Sep 17 00:00:00 2001 From: Hendrik Huyskens Date: Wed, 1 Nov 2023 13:28:38 +0100 Subject: [PATCH 37/37] Fix import order --- .../invest_optimize_all_technologies_using_mp_and_tsam.py | 2 +- .../test_tsam/test_full_load_hours_and_variable_costs.py | 3 ++- .../test_tsam/test_storage_invest_tsam_integration.py | 5 +++-- .../test_solph/test_tsam/test_storage_tsam_integration.py | 3 ++- .../test_solph/test_tsam/test_storage_tsam_vs_original.py | 3 ++- 5 files changed, 10 insertions(+), 6 deletions(-) diff --git a/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py b/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py index 4b0d7cb51..f859c799c 100644 --- a/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py +++ b/examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py @@ -81,9 +81,9 @@ import os import pprint as pp import warnings -import tsam.timeseriesaggregation as tsam import pandas as pd +import tsam.timeseriesaggregation as tsam from oemof.tools import economics from oemof.tools import logger diff --git a/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py b/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py index c44eeae24..3fafe3f26 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py +++ b/tests/test_scripts/test_solph/test_tsam/test_full_load_hours_and_variable_costs.py @@ -25,10 +25,11 @@ """ import logging + import pandas as pd import pytest - from oemof.tools import logger + from oemof import solph ########################################################################## diff --git a/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py b/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py index 05211acc9..1e1fa5b20 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py +++ b/tests/test_scripts/test_solph/test_tsam/test_storage_invest_tsam_integration.py @@ -34,11 +34,12 @@ """ import logging + import pandas as pd import pytest - -from oemof.tools import logger from oemof.tools import economics +from oemof.tools import logger + from oemof import solph ########################################################################## diff --git a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py index 7a9132187..bf0aae66c 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py +++ b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_integration.py @@ -34,10 +34,11 @@ """ import logging + import pandas as pd import pytest - from oemof.tools import logger + from oemof import solph ########################################################################## diff --git a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_vs_original.py b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_vs_original.py index 10cafdb08..dfc0f727c 100644 --- a/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_vs_original.py +++ b/tests/test_scripts/test_solph/test_tsam/test_storage_tsam_vs_original.py @@ -17,10 +17,11 @@ """ import logging + import pandas as pd import pytest - from oemof.tools import logger + from oemof import solph ##########################################################################