Initial commit of public branch of the SecMOD framework

sampledata/00-EXTERNAL/external_data_packages/README.md

+
+EXTERNAL DATA PACKAGES
+===========================================================================
+SecMOD can include external data from the OPSD-database. The folder "scripts" contains the code we used to include data about existing conventional and renewable plants. Please note that existing infrastructure is not included automatically in the current settings.
+
+
+We follow the Data Package standard by the [Frictionless Data project](http://frictionlessdata.io/),
+a part of the Open Knowledge Foundation.
+
+Field documentation
+===========================================================================
+
+external_data_packages.csv
+---------------------------------------------------------------------------
+
+* internal_name
+  * Type: string
+  * Description: unique and fixed name for the internal use of the external data package
+* source_url
+  * Type: string
+  * Description: unique and fixed name for the internal use of the external data package

sampledata/00-EXTERNAL/external_data_packages/datapackage.json

+{
+    "profile": "tabular-data-package",
+    "resources": [
+        {
+            "path": "external_data_packages.csv",
+            "profile": "tabular-data-resource",
+            "name": "external_data_packages",
+            "format": "csv",
+            "mediatype": "text/csv",
+            "encoding": "utf-8",
+            "schema": {
+                "fields": [
+                    {
+                        "name": "internal_name",
+                        "type": "string",
+                        "format": "default"
+                    },
+                    {
+                        "name": "source_url",
+                        "type": "string",
+                        "format": "default"
+                    }
+                ],
+                "missingValues": [
+                    ""
+                ]
+            }
+        }
+    ]
+}
\ No newline at end of file

sampledata/00-EXTERNAL/external_data_packages/external_data_packages.csv

+internal_name,source_url
+renewable_power_plants,https://data.open-power-system-data.org/renewable_power_plants/2016-10-21
+conventional_power_plants,https://data.open-power-system-data.org/conventional_power_plants/2016-10-27

sampledata/00-EXTERNAL/external_data_packages/scripts/conventional_power_plants.py

+# Important columns of the file renewable_power_plants_DE.csv:
+# - commissioned_original
+# - shutdown
+# - fuel
+# - capacity_net_bnetza
+# - lat
+# - lon
+
+import pandas as pd
+import numpy as np
+from geopy import distance
+from pathlib import Path
+from tqdm import tqdm
+import secmod.helpers as helpers
+
+def setup(working_directory: Path, data_package_name: str, grid: str = "DE", unit: str = "MW", years: list =[1900,1905,1910,1915,1920,1925,1930,1935,1940,1945,1950,1955,1960,1965,1970,1975,1980,1985,1990,1995,2000,2005,2010,2016,2020,2025,2030,2035,2040,2045,2050], debug=False):
+    """This method is called in order to import the data from this data package to the input data."""
+
+    tqdm.pandas()
+
+    used_processes = {"Natural gas", "Hard coal", "Hydro", "Waste", "Biomass and biogas", "Lignite", "Nuclear"}
+    process_to_limit_potential = {"Hydro", "Waste", "Biomass and biogas"}
+    internal_process_name = {"Natural gas": "natural_gas_turbine", "Hard coal": "hard_coal", "Hydro": "pumped_storage_hydro", "Waste": "waste_incineration", "Biomass and biogas": "biogas-to-power", "Lignite": "lignite", "Nuclear": "nuclear"}
+    process_classes = {"Natural gas": "production", "Hard coal": "production", "Hydro": "storage", "Waste": "production", "Biomass and biogas": "production", "Lignite": "production", "Nuclear": "production"}
+
+    fuel = "fuel"
+    columns = ["commissioned_original", "shutdown", fuel, "technology", "capacity_net_bnetza", "lat", "lon"]
+    print("Load data")
+    plants = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "00-EXTERNAL" / data_package_name / "conventional_power_plants_{0}.csv".format(grid), float_precision="high", usecols=(lambda column: column in columns), parse_dates=["commissioned_original", "shutdown"])
+    # check if all columns are present and replace columns that at some point in time change their names
+    if fuel not in plants.columns:  # check if fuels were loaded, i.e., if the fuel column is missing
+        fuel = 'energy_source'  # staring in 2020, 'fuel' has been renamed to 'energy_source'
+        plants = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "00-EXTERNAL" / data_package_name / "conventional_power_plants_{0}.csv".format(grid), float_precision="high", usecols=(lambda column: column in columns), parse_dates=["commissioned_original", "shutdown"])
+    assert (plants.columns.isin(["commissioned_original", "shutdown", fuel, "technology", "capacity_net_bnetza", "lat", "lon"]).all()), 'At least one of the columns could not be loaded. Check if any of the column names has been updated.'
+    
+    if debug:
+        plants = plants.sample(500)
+    nodes = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "01-GRID" / grid / "nodes.csv", index_col="node", usecols=lambda x: "Unnamed" not in x, float_precision="high")
+    print("Select used technologies")
+    plants = plants[plants[fuel].isin(used_processes)]
+    print("Determine commissioning year")
+    plants["commissioning_year"]    = plants["commissioned_original"].progress_apply(lambda date: parse_year(date))
+    print("Determine decommissioning year")
+    plants["decommissioning_year"]  = plants["shutdown"].progress_apply(lambda date: parse_year(date))
+    plants = plants[[fuel, "capacity_net_bnetza", "lat", "lon", "commissioning_year", "decommissioning_year", "technology"]]
+    print("Determine actual construction year")
+    plants["construction_year"]     = plants.progress_apply(lambda plant: get_construction_year(plant, working_directory, internal_process_name[plant.fuel], process_classes[plant.fuel]), axis=1)
+    print("Get used construction year")
+    plants["construction_year"] = plants["construction_year"].progress_apply(lambda year: get_closest_year(years, year))
+    plants = plants[[fuel, "capacity_net_bnetza", "lat", "lon", "construction_year", "technology"]]
+    plants = plants.dropna()
+    print("Determine closest nodes")
+    plants["node"] = plants[["lat", "lon"]].progress_apply(lambda plant: find_closest_node(nodes, plant.lon, plant.lat), axis=1)
+    plants = plants.loc[plants["technology"] != "Run-of-river"]
+    plants = plants[[fuel, "capacity_net_bnetza", "node", "construction_year"]]
+    plants.set_index([fuel, "node", "construction_year"], inplace=True)
+    plants.sort_index(inplace=True)
+    print("Aggregate capacities by fuel, nodes and year of construction")
+    plants = plants["capacity_net_bnetza"].groupby(level=[0,1,2]).sum()
+
+    print("Write existing capacity for {0}".format(list(plants.index.get_level_values(fuel).unique())))
+    for process in tqdm(list(plants.index.get_level_values(fuel).unique())):
+        capacity = plants.loc[process].unstack("construction_year").reindex(index=nodes.index).fillna(0)
+        capacity_columns = list(capacity.columns)
+        capacity_columns.sort()
+        if process != "Hydro":
+            capacity["unit"] = unit
+        else:
+            capacity = capacity / 0.164332428589893
+            capacity["unit"] = unit + "h"
+        capacity["comments"] = ""
+        capacity.reset_index(inplace=True)
+        capacity = capacity[["node", "unit"] + capacity_columns + ["comments"]]
+        export_existing_capacity(capacity, working_directory, internal_process_name[process], grid, process_classes[process])
+        if process in process_to_limit_potential:
+            potential = get_nodal_capacity_by_latest_year(capacity, working_directory, internal_process_name[process], process_classes[process], years)
+            export_potential_capacity(potential, working_directory, internal_process_name[process], grid, process_classes[process])
+    print("Done!")
+    
+def parse_year(date):
+    try:
+        return int(pd.to_datetime(date, format="%d.%m.%Y").year)
+    except:
+        try:
+            return int(pd.to_datetime(date, format="%Y").year)
+        except:
+            return np.nan
+
+def get_nodal_capacity_by_latest_year(capacity: pd.DataFrame, working_directory: Path, process: str, process_class: str, years: list):
+    relevant_years = [int(column) for column in capacity.columns if helpers.isInteger(column)]
+    reference_year = max(relevant_years)
+    lifetime_duration = get_lifetime_duration(working_directory, reference_year, process, process_class)
+    relevant_years = [year for year in relevant_years if (reference_year - year) <= lifetime_duration]
+    pd.options.mode.chained_assignment = None  # default='warn'
+    potential = capacity[relevant_years]
+    potential["total"] = potential.sum(axis=1)
+    potential[["node", "unit"]] = capacity[["node", "unit"]]
+    potential["comments"] = "Based on the total existing capacity in {0}".format(reference_year)
+    for year in years:
+        potential[year] = potential["total"]
+    potential = potential[["node", "unit"] + years + ["comments"]]
+    pd.options.mode.chained_assignment = "warn"  # default='warn'
+    return potential
+
+def get_construction_year(plant, working_directory: Path, process: str, process_class: str = "production"):
+    """Returns the construction year, to be used"""
+    if np.isnan(plant.decommissioning_year):
+        return plant.commissioning_year
+    elif not np.isnan(plant.commissioning_year):
+        return (plant.decommissioning_year - get_lifetime_duration(working_directory, plant.commissioning_year, process, process_class))
+    else:
+        return np.nan
+
+def find_closest_node(nodes: pd.DataFrame, longitude, latitude):
+    """This method return the ID of the node, which is closest to the plant."""
+    nodes["distance"] = nodes.apply(lambda node: distance.great_circle((node.latitude, node.longitude), (latitude, longitude)).km, axis=1)
+    return nodes["distance"].idxmin()
+
+def get_lifetime_duration(working_directory: Path, construction_year: int, process: str, process_class: str = "production"):
+    """Return the lifetime duration of a process from lifetime_duration.csv"""
+    process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+    target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / "lifetime_duration.csv"
+    lifetime_duration = pd.read_csv(target_directory, float_precision="high", usecols=(lambda column: (column != "comments") and (column != "unit")))
+    years_of_data = [int(column) for column in lifetime_duration.columns]
+    lifetime_duration.columns = years_of_data
+    closest_year = get_closest_year(years_of_data, construction_year)
+    return lifetime_duration.at[0,get_closest_year(years_of_data, construction_year)]
+
+def get_closest_year(years: list, year_of_construction: int):
+    """Return the closest year"""
+    difference = [abs(year - year_of_construction) for year in years]
+    return years[difference.index(min(difference))]
+
+def export_existing_capacity(capacity: pd.DataFrame, working_directory: Path, process: str, grid: str, process_class: str = "production"):
+    """This method saves a dataframe to the correct position in a process and grid-subfolder."""
+    process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+    target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / grid / "existing_capacity.csv"
+    capacity.to_csv(path_or_buf=target_directory, index=False)
+
+def export_potential_capacity(potential: pd.DataFrame, working_directory: Path, process: str, grid: str, process_class: str):
+    """This method saves a dataframe to the correct position in a process and grid-subfolder."""
+    process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+    target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / grid / "potential_capacity.csv"
+    potential.to_csv(path_or_buf=target_directory)
+
+if __name__ == "__main__":
+    setup(Path().cwd(), "conventional_power_plants", debug=False)
\ No newline at end of file

sampledata/00-EXTERNAL/external_data_packages/scripts/renewable_power_plants.py

+# Important columns of the file renewable_power_plants_DE.csv:
+# - commissioning_date
+# - decommissioning_date
+# - technology
+# - electrical_capacity
+# - lat
+# - lon
+
+import pandas as pd
+import numpy as np
+from geopy import distance
+from pathlib import Path
+from tqdm import tqdm
+import secmod.helpers as helpers
+
+def setup(working_directory: Path, data_package_name: str, grid: str = "DE", unit: str = "MW", years: list =[1900,1905,1910,1915,1920,1925,1930,1935,1940,1945,1950,1955,1960,1965,1970,1975,1980,1985,1990,1995,2000,2005,2010,2016,2020,2025,2030,2035,2040,2045,2050], debug=False):
+    """This method is called in order to import the data from this data package to the input data."""
+
+    tqdm.pandas()
+    used_processes = {"Run-of-river", "Onshore", "Photovoltaics", "Geothermal", "Offshore"}
+    process_to_limit_potential = {"Run-of-river", "Geothermal"}
+    internal_process_name = {"Run-of-river": "run-of-river_hydro", "Onshore": "wind_onshore", "Photovoltaics": "photovoltaics", "Geothermal": "geothermal", "Offshore": "wind_offshore"}
+    process_classes = {"Run-of-river": "production", "Biomass and biogas": "production", "Sewage and landfill gas": "production", "Onshore": "production", "Photovoltaics": "production", "Other fossil fuels": "production", "Geothermal": "production", "Offshore": "production", "Photovoltaics ground": "production", "Storage": "storage"}
+
+    columns = ["commissioning_date", "decommissioning_date", "technology", "electrical_capacity", "lat", "lon"]
+    print("Load data")
+    plants = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "00-EXTERNAL" / data_package_name / "renewable_power_plants_{0}.csv".format(grid), float_precision="high", usecols=(lambda column: column in columns), parse_dates=["commissioning_date", "decommissioning_date"])
+    if debug:
+        plants = plants.sample(500)
+    nodes = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "01-GRID" / grid / "nodes.csv", index_col="node", usecols=lambda x: "Unnamed" not in x, float_precision="high")
+    print("Select used technologies")
+    plants = plants[plants["technology"].isin(used_processes)]
+    print("Determine commissioning year")
+    plants["commissioning_year"]    = plants["commissioning_date"].progress_apply(lambda date: date.year)
+    print("Determine decommissioning year")
+    plants["decommissioning_year"]  = plants["decommissioning_date"].progress_apply(lambda date: date.year)
+    plants = plants[["technology", "electrical_capacity", "lat", "lon", "commissioning_year", "decommissioning_year"]]
+    print("Determine actual construction year")
+    plants["construction_year"]     = plants.progress_apply(lambda plant: get_construction_year(plant, working_directory, internal_process_name[plant.technology], process_classes[plant.technology]), axis=1)
+    print("Get used construction year")
+    plants["construction_year"] = plants["construction_year"].progress_apply(lambda year: get_closest_year(years, year))
+    plants = plants[["technology", "electrical_capacity", "lat", "lon", "construction_year"]]
+    plants = plants.dropna()
+    print("Determine closest nodes")
+    plants["node"] = plants[["lat", "lon"]].progress_apply(lambda plant: find_closest_node(nodes, plant.lon, plant.lat), axis=1)
+    plants = plants[["technology", "electrical_capacity", "node", "construction_year"]]
+    plants.set_index(["technology", "node", "construction_year"], inplace=True)
+    plants.sort_index(inplace=True)
+    print("Aggregate capacities by technology, nodes and year of construction")
+    plants = plants["electrical_capacity"].groupby(level=[0,1,2]).sum()
+
+    print("Write existing capacity for {0}".format(list(plants.index.get_level_values("technology").unique())))
+    for process in tqdm(list(plants.index.get_level_values("technology").unique())):
+        capacity = plants.loc[process].unstack("construction_year").reindex(index=nodes.index).fillna(0)
+        capacity_columns = list(capacity.columns)
+        capacity_columns.sort()
+        capacity["unit"] = unit
+        capacity["comments"] = ""
+        capacity.reset_index(inplace=True)
+        capacity = capacity[["node", "unit"] + capacity_columns + ["comments"]]
+        export_existing_capacity(capacity, working_directory, internal_process_name[process], grid, process_classes[process])
+        if process in process_to_limit_potential:
+            potential = get_nodal_capacity_by_latest_year(capacity, working_directory, internal_process_name[process], process_classes[process], years)
+            export_potential_capacity(potential, working_directory, internal_process_name[process], grid, process_classes[process])
+    print("Done!")
+    
+def get_nodal_capacity_by_latest_year(capacity: pd.DataFrame, working_directory: Path, process: str, process_class: str, years: list):
+    relevant_years = [int(column) for column in capacity.columns if helpers.isInteger(column)]
+    reference_year = max(relevant_years)
+    lifetime_duration = get_lifetime_duration(working_directory, reference_year, process, process_class)
+    relevant_years = [year for year in relevant_years if (reference_year - year) <= lifetime_duration]
+    pd.options.mode.chained_assignment = None  # default='warn'
+    potential = capacity[relevant_years]
+    potential["total"] = potential.sum(axis=1)
+    potential[["node", "unit"]] = capacity[["node", "unit"]]
+    potential["comments"] = "Based on the total existing capacity in {0}".format(reference_year)
+    for year in years:
+        potential[year] = potential["total"]
+    potential = potential[["node", "unit"] + years + ["comments"]]
+    pd.options.mode.chained_assignment = "warn"  # default='warn'
+    return potential
+
+def get_construction_year(plant, working_directory: Path, process: str, process_class: str = "production"):
+    """Returns the construction year, to be used"""
+    if np.isnan(plant.decommissioning_year):
+        return plant.commissioning_year
+    elif not np.isnan(plant.commissioning_year):
+        return (plant.decommissioning_year - get_lifetime_duration(working_directory, plant.commissioning_year, process, process_class))
+    else:
+        return np.nan
+
+def find_closest_node(nodes: pd.DataFrame, longitude, latitude):
+    """This method return the ID of the node, which is closest to the plant."""
+    nodes["distance"] = nodes.apply(lambda node: distance.great_circle((node.latitude, node.longitude), (latitude, longitude)).km, axis=1)
+    return nodes["distance"].idxmin()
+
+def get_lifetime_duration(working_directory: Path, construction_year: int, process: str, process_class: str = "production"):
+    """Return the lifetime duration of a process from lifetime_duration.csv"""
+    process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+    target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / "lifetime_duration.csv"
+    lifetime_duration = pd.read_csv(target_directory, float_precision="high", usecols=(lambda column: (column != "comments") and (column != "unit")))
+    years_of_data = [int(column) for column in lifetime_duration.columns]
+    lifetime_duration.columns = years_of_data
+    closest_year = get_closest_year(years_of_data, construction_year)
+    return lifetime_duration.at[0,get_closest_year(years_of_data, construction_year)]
+
+def get_closest_year(years: list, year_of_construction: int):
+    """Return the closest year"""
+    difference = [abs(year - year_of_construction) for year in years]
+    return years[difference.index(min(difference))]
+
+def export_existing_capacity(capacity: pd.DataFrame, working_directory: Path, process: str, grid: str, process_class: str = "production"):
+    """This method saves a dataframe to the correct position in a process and grid-subfolder."""
+    process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+    target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / grid / "existing_capacity.csv"
+    capacity.to_csv(path_or_buf=target_directory, index=False)
+
+def export_potential_capacity(potential: pd.DataFrame, working_directory: Path, process: str, grid: str, process_class: str):
+    """This method saves a dataframe to the correct position in a process and grid-subfolder."""
+    process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+    target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / grid / "potential_capacity.csv"
+    potential.to_csv(path_or_buf=target_directory)
+
+if __name__ == "__main__":
+    setup(Path().cwd(), "renewable_power_plants", debug=False)
\ No newline at end of file

sampledata/00-UNITS-DEFINITIONS.txt

+m3 = m ** 3 = cubic_meter
+m2 = m ** 2 = square_meter
+mass_CO2_equivalent = [Global_Warming_Potential_100a] = CO2_eq = CO2_Eq = CO2_EQ
+megajoule_equivalent = [cumulative_energy_demand] = MJ_Eq = MJ_EQ = MJ_eq
+mole_h_plus_equivalent = [acidification] = H_plus_eq = H_plus_Eq = H_plus_EQ
+comparative_toxic_unit_for_humans = [ecotoxicity] = CTUh
+phosphor_equivalent = [freshwater_eutrophication] = P_Eq = P_EQ = P_eq
+nitrogen_equivalent = [marine_eutrophication] = N_Eq = N_EQ = N_eq
+mass_uranium_235_equivalent = [ionising_radiation] = U235_Eq = U235_EQ = U235_eq
+mass_CFC_11_equivalent = [ozone_layer_depletion] = CFC_11_Eq = CFC_11_EQ = CFC_11_eq
+mass_ethylen_equivalent = [photochemical_ozone_creation] = ethylen_Eq = ethylen_EQ = ethylen_eq = C2H4_Eq = C2H4_EQ = C2H4_eq
+mass_particulate_matter_2_5_equivalent = [respiratory_effects] = PM2_5_Eq = PM2_5_EQ = PM2_5_eq = PM2_5
+mass_deficit_of_soil_organic_carbon = [land_use] = soil_organic_carbon = SOC
+mass_antimony_equivalent = [mineral] = Sb_Eq = Sb_EQ = Sb_eq
+oil_equivalent = [FDP] = oil_Eq = oil_EQ = oil_eq
+mass_1_4_DCB_equivalent = [FETPinf] = DCB_Eq = DCB_EQ = DCB_eq
+iron_equivalent = [MDP] = Fe_Eq = Fe_EQ = Fe_eq
+particulate_matter_10_equivalent = [PMFP] = PM10_Eq = PM10_EQ = PM10_eq = PM10
+NMVOC_equivalent = [POFP] = NMVOC_Eq = NMVOC_EQ = NMVOC_eq =  NMVOC
+mass_sulfur_dioxide = [TAP100] = SO2_Eq = SO2_EQ = SO2_eq
+
+Euro = [money] = EURO
+car = [car]
+meter_vehicle = [mobility] = meter * car
+circuit = [power_circuit]
\ No newline at end of file

sampledata/01-GRID/DE/README.md

+# README - GRID DE
+
+This data package contains all the data about the German Electricity
+grid. It consists of nodes and connections. Nodes have an integer
+ID and geo coordinates. Connections have an integer ID as well
+and are defined by the two nodes they connect.

sampledata/01-GRID/DE/connections.csv

+connection,node1,node2,manual_distance,manual_distance_unit
+1,1,16,48.9379,km
+2,1,16,52.1117,km
+3,1,2,57.3021,km
+4,1,16,49.3466,km
+5,1,16,52.0148,km
+6,1,16,57.3962,km
+7,1,16,62.8176,km
+8,2,3,131.201,km
+9,2,9,46.991,km
+10,2,9,47.2844,km
+11,2,16,28.7351,km
+12,2,3,34.6007,km
+13,2,3,104.834,km
+14,2,9,105.559,km
+15,2,16,24.004,km
+16,3,11,27.5512,km
+17,3,11,34.2476,km
+18,3,11,45.6801,km
+19,3,4,15.4317,km
+20,3,4,29.0022,km
+21,3,4,56.892,km
+22,3,9,79.2796,km
+23,3,11,32.0209,km
+24,3,11,60.8896,km
+25,3,15,48.9509,km
+26,3,16,28.5293,km
+27,4,5,29.9327,km
+28,4,5,116.183,km
+29,4,11,86.3902,km
+30,4,12,37.9364,km
+31,4,17,58.4932,km
+32,4,17,130.04,km
+33,4,5,29.5558,km
+34,4,11,33.4769,km
+35,4,11,54.2218,km
+36,4,12,54.4385,km
+37,4,13,20.7582,km
+38,4,13,23.5401,km
+39,5,6,73.9456,km
+40,5,6,95,km
+41,5,6,89.7931,km
+42,5,6,93.1178,km
+43,5,6,100.562,km
+44,5,6,112.844,km
+45,5,7,92.4486,km
+46,5,7,101.55,km
+47,5,17,56.3032,km
+48,6,14,50.1904,km
+49,7,8,13.3963,km
+50,7,8,20.279,km
+51,7,8,104.812,km
+52,7,13,21.0193,km
+53,7,13,26.7976,km
+54,7,8,14.1789,km
+55,7,8,23.5505,km
+56,7,8,45.7653,km
+57,7,8,55.481,km
+58,7,8,120.971,km
+59,7,8,204.077,km
+60,7,13,47.3946,km
+61,7,13,61.5578,km
+62,7,13,74.1112,km
+63,7,13,152.519,km
+64,8,13,111.813,km
+65,8,13,86.4628,km
+66,8,13,138.549,km
+67,8,14,16.9606,km
+68,8,14,19.9738,km
+69,8,14,26.6516,km
+70,8,14,40.8788,km
+71,8,14,41.1006,km
+72,8,14,46.9505,km
+73,9,10,64.6059,km
+74,9,10,64.6962,km
+75,9,10,110.981,km
+76,9,11,42.3182,km
+77,9,11,69.992,km
+78,9,11,115.669,km
+79,9,11,59.2155,km
+80,10,11,10.0014,km
+81,10,11,21.97,km
+82,10,11,22.4985,km
+83,10,11,24.2453,km
+84,10,11,26.707,km
+85,10,11,28.8168,km
+86,10,11,77.1936,km
+87,10,12,3.98041,km
+88,10,12,5.3362,km
+89,10,12,11.3042,km
+90,10,12,18.9789,km
+91,10,12,18.9865,km
+92,10,12,22.6509,km
+93,10,12,22.764,km
+94,10,12,24.5185,km
+95,10,12,27.1188,km
+96,10,12,31.2371,km
+97,10,12,31.3148,km
+98,10,12,43.979,km
+99,10,11,25.743,km
+100,10,11,34.8071,km
+101,10,11,37.6526,km
+102,10,11,57.8004,km
+103,10,12,10.1819,km
+104,10,12,30.9199,km
+105,10,12,74.0115,km
+106,10,12,83.9702,km
+107,11,12,51.9124,km
+108,12,13,56.9823,km
+109,12,13,58.7159,km
+110,12,13,73.8307,km
+111,12,13,95.3573,km
+112,12,13,118.637,km
+113,12,13,124.115,km
+114,15,16,87.3598,km
+115,15,17,119.15,km
+116,15,18,62.4058,km
+117,15,18,94.9249,km
+118,15,18,125.677,km
+119,15,18,130.612,km
+120,15,18,208.282,km
+121,17,18,61.8101,km
+122,17,18,98.654,km
+123,17,18,109.47,km
+124,17,18,131.693,km
+125,17,18,157.592,km
+126,17,18,169.377,km
+127,17,18,67.4574,km
+128,17,18,103.507,km
+129,17,18,104.592,km

sampledata/01-GRID/DE/datapackage.json

+{
+    "profile": "tabular-data-package",
+    "resources": [
+        {
+            "path": "connections.csv",
+            "profile": "tabular-data-resource",
+            "name": "connections",
+            "format": "csv",
+            "mediatype": "text/csv",
+            "encoding": "utf-8",
+            "schema": {
+                "fields": [
+                    {
+                        "name": "connection",
+                        "type": "integer",
+                        "format": "default"
+                    },
+                    {
+                        "name": "node1",
+                        "type": "integer",
+                        "format": "default"
+                    },
+                    {
+                        "name": "node2",
+                        "type": "integer",
+                        "format": "default"
+                    }
+                ],
+                "missingValues": [
+                    ""
+                ]
+            }
+        },
+        {
+            "path": "nodes.csv",
+            "profile": "tabular-data-resource",
+            "name": "nodes",
+            "format": "csv",
+            "mediatype": "text/csv",
+            "encoding": "utf-8",
+            "schema": {
+                "fields": [
+                    {
+                        "name": "node",
+                        "type": "integer",
+                        "format": "default"
+                    },
+                    {
+                        "name": "country",
+                        "type": "string",
+                        "format": "default"
+                    },
+                    {
+                        "name": "state",
+                        "type": "string",
+                        "format": "default"
+                    },
+                    {
+                        "name": "dena_region",
+                        "type": "string",
+                        "format": "default"
+                    },
+                    {
+                        "name": "z6_region",
+                        "type": "string",
+                        "format": "default"
+                    },
+                    {
+                        "name": "latitude",
+                        "type": "number",
+                        "format": "default"
+                    },
+                    {
+                        "name": "longitude",
+                        "type": "number",
+                        "format": "default"
+                    }
+                ],
+                "missingValues": [
+                    ""
+                ]
+            }
+        }
+    ]
+}
\ No newline at end of file

sampledata/01-GRID/DE/nodes.csv

+node,latitude,longitude,country,state,dena_region,z6_region
+1,54.29,9.725,DE,DE_SH,dena21,DE_NW
+2,53.235,8.406,DE,DE_NI,dena22,DE_NW
+3,52.278,9.917,DE,DE_NI,dena23,DE_NW
+4,51.059,9.258,DE,DE_HE,dena24,DE_WE
+5,49.929,10.836,DE,DE_BY,dena25,DE_SE
+6,48.207,11.868,DE,DE_BY,dena26,DE_SE
+7,49.29,8.674,DE,DE_BW,dena41,DE_SW
+8,48.31,8.873,DE,DE_BW,dena42,DE_SW
+9,52.288,7.748,DE,DE_NRW,dena71,DE_NW
+10,51.445,6.948,DE,DE_NRW,dena72,DE_WE
+11,51.684,7.992,DE,DE_NRW,dena73,DE_WE
+12,50.795,6.973,DE,DE_NRW,dena74,DE_WE
+13,49.483,7.729,DE,DE_RP,dena75,DE_SW
+14,48.027,10.642,DE,DE_BY,dena76,DE_SE
+15,52.562,12.941,DE,DE_BB,dena81,DE_NE
+16,53.555,10.158,DE,DE_HH,dena82,DE_NW
+17,50.93,11.471,DE,DE_TH,dena83,DE_EA
+18,51.356,13.382,DE,DE_SN,dena84,DE_EA

sampledata/01-GRID/README.md

+# README - 01-GRID
+
+This directory contains all data packages which define
+a grid model. A grid definition contains the information
+about the nodes of the grid, their geo coordinates
+and connections between the nodes. A connection is only
+defined by the two nodes it connects.
+
+SecMOD can handle multiple grid folders at once. The grid which should be used in the optimization must be indicated in the config.py.

sampledata/01-UNITS-TARGET-OPTIMIZATION.json

+[
+    "megameter",
+    "kilocar",
+    "MW",
+    "MWh",
+    "circuit",
+    "hour",
+    "cubic_meter",
+    "",
+    "gigagram",
+    "mass_CO2_equivalent",
+    "gigaEuro",
+    "ohm",
+    "kV"
+    ]
\ No newline at end of file

sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/README.md

+# README - 01-ECOINVENT-BASED
+
+This directory contains all data packages which define
+production processes using ecoinvent.

sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/README.md

+# README - NATURAL GAS COMBINED CYCLE - DE
+
+This data package contains all necessary information
+about the production process **natural gas combined cycle** in the grid: **DE**

sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/existing_capacity.csv

+node,unit,2016,comments
+1,MW ,,
+2,MW ,,
+3,MW ,,
+4,MW ,,
+5,MW ,,
+6,MW ,,
+7,MW ,,
+8,MW ,,
+9,MW ,,
+10,MW ,,
+11,MW ,,
+12,MW ,,
+13,MW ,,
+14,MW ,,
+15,MW ,,
+16,MW ,,
+17,MW ,,
+18,MW ,,

sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/maximum_production_share.csv

+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,1,,,,,,,,,
+2,1,,,,,,,,,
+3,1,,,,,,,,,
+4,1,,,,,,,,,
+5,1,,,,,,,,,
+6,1,,,,,,,,,
+7,1,,,,,,,,,
+8,1,,,,,,,,,
+9,1,,,,,,,,,
+10,1,,,,,,,,,
+11,1,,,,,,,,,
+12,1,,,,,,,,,
+13,1,,,,,,,,,
+14,1,,,,,,,,,
+15,1,,,,,,,,,
+16,1,,,,,,,,,
+17,1,,,,,,,,,
+18,1,,,,,,,,,

sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/potential_capacity.csv

+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,MW ,,,,,,,,,
+2,MW ,,,,,,,,,
+3,MW ,,,,,,,,,
+4,MW ,,,,,,,,,
+5,MW ,,,,,,,,,
+6,MW ,,,,,,,,,
+7,MW ,,,,,,,,,
+8,MW ,,,,,,,,,
+9,MW ,,,,,,,,,
+10,MW ,,,,,,,,,
+11,MW ,,,,,,,,,
+12,MW ,,,,,,,,,
+13,MW ,,,,,,,,,
+14,MW ,,,,,,,,,
+15,MW ,,,,,,,,,
+16,MW ,,,,,,,,,
+17,MW ,,,,,,,,,
+18,MW ,,,,,,,,,

sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/README.md

+# README - NATURAL GAS COMBINED CYCLE
+
+This data package contains all necessary information
+about the production process **natural gas combined cycle**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.

sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/costs.csv

+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,kiloEuro * MW ** (-1),700,700,700,700,700,700,700,700,
+operation,kiloEuro * MWh ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * MW ** (-1),24,24,24,24,24,24,24,24,
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,