Merge branch 'master' into configs-in-one-folder

.pre-commit-config.yaml

@@ -39,7 +39,7 @@ repos:
 
   # Make docstrings PEP 257 compliant
 - repo: https://github.com/PyCQA/docformatter
-  rev: v1.5.1
+  rev: v1.6.0
   hooks:
   - id: docformatter
     args: ["--in-place", "--make-summary-multi-line", "--pre-summary-newline"]
@@ -74,7 +74,7 @@ repos:
 
   # Format Snakemake rule / workflow files
 - repo: https://github.com/snakemake/snakefmt
-  rev: v0.8.3
+  rev: v0.8.4
   hooks:
   - id: snakefmt
 

scripts/_helpers.py

@@ -400,7 +400,6 @@ def override_component_attrs(directory):
     -------
     Dictionary of overridden component attributes.
     """
-
     attrs = Dict({k: v.copy() for k, v in component_attrs.items()})
 
     for component, list_name in components.list_name.items():
@@ -418,7 +417,6 @@ def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
     country for the period dt_index, taking account of time zones and summer
     time.
     """
-
     weekly_profile = pd.Series(weekly_profile, range(24 * 7))
 
     week_df = pd.DataFrame(index=dt_index, columns=nodes)

scripts/add_existing_baseyear.py

@@ -38,7 +38,6 @@ def add_build_year_to_new_assets(n, baseyear):
     baseyear : int
         year in which optimized assets are built
     """
-
     # Give assets with lifetimes and no build year the build year baseyear
     for c in n.iterate_components(["Link", "Generator", "Store"]):
         assets = c.df.index[(c.df.lifetime != np.inf) & (c.df.build_year == 0)]
@@ -62,7 +61,6 @@ def add_existing_renewables(df_agg):
     Append existing renewables to the df_agg pd.DataFrame with the conventional
     power plants.
     """
-
     carriers = {"solar": "solar", "onwind": "onwind", "offwind": "offwind-ac"}
 
     for tech in ["solar", "onwind", "offwind"]:

scripts/build_biomass_potentials.py

@@ -68,7 +68,6 @@ def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
         Biomass potentials for given year and scenario
         in TWh/a by commodity and NUTS2 region.
     """
-
     glossary = pd.read_excel(
         str(snakemake.input.enspreso_biomass),
         sheet_name="Glossary",
@@ -124,7 +123,6 @@ def disaggregate_nuts0(bio):
     -------
     pd.DataFrame
     """
-
     pop = build_nuts_population_data()
 
     # get population in nuts2
@@ -149,7 +147,6 @@ def build_nuts2_shapes():
     - add RS, AL, BA country shapes (not covered in NUTS 2013)
     - consistently name ME, MK
     """
-
     nuts2 = gpd.GeoDataFrame(
         gpd.read_file(snakemake.input.nuts2).set_index("id").geometry
     )
@@ -186,7 +183,6 @@ def convert_nuts2_to_regions(bio_nuts2, regions):
     -------
     gpd.GeoDataFrame
     """
-
     # calculate area of nuts2 regions
     bio_nuts2["area_nuts2"] = area(bio_nuts2)
 

scripts/build_cop_profiles.py

@@ -7,10 +7,11 @@
 Build coefficient of performance (COP) time series for air- or ground-sourced
 heat pumps.
 
-The COP is a function of the temperature difference between
+The COP is a function of the temperature difference between source and
-source and sink.
+sink.
 
 The quadratic regression used is based on Staffell et al. (2012)
+
 https://doi.org/10.1039/C2EE22653G.
 """
 

scripts/build_energy_totals.py

@@ -124,7 +124,6 @@ def build_eurostat(input_eurostat, countries, report_year, year):
     """
     Return multi-index for all countries' energy data in TWh/a.
     """
-
     filenames = {
         2016: f"/{year}-Energy-Balances-June2016edition.xlsx",
         2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx",
@@ -163,7 +162,6 @@ def build_swiss(year):
     """
     Return a pd.Series of Swiss energy data in TWh/a.
     """
-
     fn = snakemake.input.swiss
 
     df = pd.read_csv(fn, index_col=[0, 1]).loc["CH", str(year)]

scripts/build_gas_network.py

@@ -22,14 +22,15 @@ def diameter_to_capacity(pipe_diameter_mm):
     """
     Calculate pipe capacity in MW based on diameter in mm.
 
-    20 inch (500 mm)  50 bar -> 1.5   GW CH4 pipe capacity (LHV)
+    20 inch (500 mm)  50 bar -> 1.5   GW CH4 pipe capacity (LHV) 24 inch
-    24 inch (600 mm)  50 bar -> 5     GW CH4 pipe capacity (LHV)
+    (600 mm)  50 bar -> 5     GW CH4 pipe capacity (LHV) 36 inch (900
-    36 inch (900 mm)  50 bar -> 11.25 GW CH4 pipe capacity (LHV)
+    mm)  50 bar -> 11.25 GW CH4 pipe capacity (LHV) 48 inch (1200 mm) 80
-    48 inch (1200 mm) 80 bar -> 21.7  GW CH4 pipe capacity (LHV)
+    bar -> 21.7  GW CH4 pipe capacity (LHV)
 
-    Based on p.15 of https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
+    Based on p.15 of
+
+    https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
     """
-
     # slopes definitions
     m0 = (1500 - 0) / (500 - 0)
     m1 = (5000 - 1500) / (600 - 500)

scripts/build_industrial_distribution_key.py

@@ -27,7 +27,6 @@ def locate_missing_industrial_sites(df):
     Should only be used if the model's spatial resolution is coarser
     than individual cities.
     """
-
     try:
         from geopy.extra.rate_limiter import RateLimiter
         from geopy.geocoders import Nominatim
@@ -71,7 +70,6 @@ def prepare_hotmaps_database(regions):
     """
     Load hotmaps database of industrial sites and map onto bus regions.
     """
-
     df = pd.read_csv(snakemake.input.hotmaps_industrial_database, sep=";", index_col=0)
 
     df[["srid", "coordinates"]] = df.geom.str.split(";", expand=True)
@@ -103,7 +101,6 @@ def build_nodal_distribution_key(hotmaps, regions, countries):
     """
     Build nodal distribution keys for each sector.
     """
-
     sectors = hotmaps.Subsector.unique()
 
     keys = pd.DataFrame(index=regions.index, columns=sectors, dtype=float)

scripts/build_industrial_production_per_country.py

@@ -247,7 +247,6 @@ def separate_basic_chemicals(demand, year):
     """
     Separate basic chemicals into ammonia, chlorine, methanol and HVC.
     """
-
     ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
 
     there = ammonia.index.intersection(demand.index)

scripts/build_retro_cost.py

@@ -150,7 +150,6 @@ def prepare_building_stock_data():
                        type and period
 
     """
-
     building_data = pd.read_csv(snakemake.input.building_stock, usecols=list(range(13)))
 
     # standardize data
@@ -318,7 +317,6 @@ def prepare_building_topology(u_values, same_building_topology=True):
     Reads in typical building topologies (e.g. average surface of building
     elements) and typical losses through thermal bridging and air ventilation.
     """
-
     data_tabula = pd.read_csv(
         snakemake.input.data_tabula,
         skiprows=lambda x: x in range(1, 11),

scripts/build_salt_cavern_potentials.py

@@ -6,8 +6,8 @@
 """
 Build salt cavern potentials for hydrogen storage.
 
-Technical Potential of Salt Caverns for Hydrogen Storage in Europe
+Technical Potential of Salt Caverns for Hydrogen Storage in Europe CC-BY
-CC-BY 4.0
+4.0
 https://doi.org/10.20944/preprints201910.0187.v1
 https://doi.org/10.1016/j.ijhydene.2019.12.161
 
@@ -39,7 +39,6 @@ def load_bus_regions(onshore_path, offshore_path):
     """
     Load pypsa-eur on- and offshore regions and concat.
     """
-
     bus_regions_offshore = gpd.read_file(offshore_path)
     bus_regions_onshore = gpd.read_file(onshore_path)
     bus_regions = concat_gdf([bus_regions_offshore, bus_regions_onshore])

scripts/build_transport_demand.py

@@ -124,7 +124,6 @@ def bev_availability_profile(fn, snapshots, nodes, options):
     """
     Derive plugged-in availability for passenger electric vehicles.
     """
-
     traffic = pd.read_csv(fn, skiprows=2, usecols=["count"]).squeeze("columns")
 
     avail_max = options["bev_avail_max"]

scripts/cluster_gas_network.py

@@ -30,7 +30,6 @@ def load_bus_regions(onshore_path, offshore_path):
     """
     Load pypsa-eur on- and offshore regions and concat.
     """
-
     bus_regions_offshore = gpd.read_file(offshore_path)
     bus_regions_onshore = gpd.read_file(onshore_path)
     bus_regions = concat_gdf([bus_regions_offshore, bus_regions_onshore])

scripts/make_summary.py

@@ -320,7 +320,6 @@ def calculate_supply(n, label, supply):
     Calculate the max dispatch of each component at the buses aggregated by
     carrier.
     """
-
     bus_carriers = n.buses.carrier.unique()
 
     for i in bus_carriers:
@@ -372,7 +371,6 @@ def calculate_supply_energy(n, label, supply_energy):
     Calculate the total energy supply/consuption of each component at the buses
     aggregated by carrier.
     """
-
     bus_carriers = n.buses.carrier.unique()
 
     for i in bus_carriers:

scripts/plot_summary.py

@@ -441,7 +441,6 @@ def plot_carbon_budget_distribution(input_eurostat):
     """
     Plot historical carbon emissions in the EU and decarbonization path.
     """
-
     import seaborn as sns
 
     sns.set()

scripts/prepare_sector_network.py

@@ -51,7 +51,6 @@ def define_spatial(nodes, options):
     ----------
     nodes : list-like
     """
-
     global spatial
 
     spatial.nodes = nodes
@@ -362,7 +361,6 @@ def update_wind_solar_costs(n, costs):
     Update costs for wind and solar generators added with pypsa-eur to those
     cost in the planning year.
     """
-
     # NB: solar costs are also manipulated for rooftop
     # when distribution grid is inserted
     n.generators.loc[n.generators.carrier == "solar", "capital_cost"] = costs.at[
@@ -440,7 +438,6 @@ def add_carrier_buses(n, carrier, nodes=None):
     """
     Add buses to connect e.g. coal, nuclear and oil plants.
     """
-
     if nodes is None:
         nodes = vars(spatial)[carrier].nodes
     location = vars(spatial)[carrier].locations
@@ -487,7 +484,6 @@ def remove_elec_base_techs(n):
     batteries and H2) from base electricity-only network, since they're added
     here differently using links.
     """
-
     for c in n.iterate_components(snakemake.config["pypsa_eur"]):
         to_keep = snakemake.config["pypsa_eur"][c.name]
         to_remove = pd.Index(c.df.carrier.unique()).symmetric_difference(to_keep)

scripts/simplify_network.py

@@ -112,15 +112,12 @@ def simplify_network_to_380(n):
     Fix all lines to a voltage level of 380 kV and remove all transformers.
 
     The function preserves the transmission capacity for each line while
-    updating
+    updating its voltage level, line type and number of parallel bundles
-    its voltage level, line type and number of parallel bundles
     (num_parallel).
 
     Transformers are removed and connected components are moved from
-    their
+    their starting bus to their ending bus. The corresponding starting
-    starting bus to their ending bus. The corresponding starting buses
+    buses are removed as well.
-    are
-    removed as well.
     """
     logger.info("Mapping all network lines onto a single 380kV layer")