minor fixes and address deprecation warnings

This commit is contained in:
Fabian Neumann 2023-02-16 20:13:26 +01:00
parent c2a249ec79
commit b14d657042
7 changed files with 21 additions and 13 deletions

View File

@ -1,4 +1,4 @@
version: 0.6.0
version: 0.7.0
logging_level: INFO
@ -510,6 +510,7 @@ plotting:
natural gas: '#e05b09'
CCGT: '#a85522'
CCGT marginal: '#a85522'
allam: '#B98F76'
gas for industry co2 to atmosphere: '#692e0a'
gas for industry co2 to stored: '#8a3400'
gas for industry: '#853403'

View File

@ -71,9 +71,9 @@ author = u'2019-2021 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), L
# built documents.
#
# The short X.Y version.
version = u'0.6'
version = u'0.7'
# The full version, including alpha/beta/rc tags.
release = u'0.6.0'
release = u'0.7.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.

View File

@ -8,6 +8,11 @@ Future release
.. note::
This unreleased version currently may require the master branches of PyPSA, PyPSA-Eur, and the technology-data repository.
* new feature
PyPSA-Eur-Sec 0.7.0 (16th February 2023)
========================================
This release includes the addition of the European gas transmission network and
incorporates retrofitting options to hydrogen.

View File

@ -178,7 +178,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
inv_busmap = {}
for k, v in busmap.iteritems():
for k, v in busmap.items():
inv_busmap[v] = inv_busmap.get(v, []) + [k]
clustermaps = busmap_s.map(busmap)

View File

@ -40,7 +40,7 @@ if __name__ == '__main__':
reference = ["RS", "BA"]
average = urban_fraction[reference].mean()
fill_values = pd.Series({ct: average for ct in missing})
urban_fraction = urban_fraction.append(fill_values)
urban_fraction = pd.concat([urban_fraction, fill_values])
# population in each grid cell
pop_cells = pd.Series(I.dot(nuts3['pop']))

View File

@ -28,8 +28,8 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
## Get overall demand curve for all vehicles
traffic = pd.read_csv(
traffic_fn, skiprows=2, usecols=["count"], squeeze=True
)
traffic_fn, skiprows=2, usecols=["count"]
).squeeze("columns")
transport_shape = generate_periodic_profiles(
dt_index=snapshots,
@ -118,7 +118,7 @@ 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=True)
traffic = pd.read_csv(fn, skiprows=2, usecols=["count"]).squeeze("columns")
avail_max = options["bev_avail_max"]
avail_mean = options["bev_avail_mean"]

View File

@ -527,6 +527,8 @@ def add_co2_tracking(n, options):
e_nom_max = pd.read_csv(snakemake.input.sequestration_potential, index_col=0).squeeze()
e_nom_max = e_nom_max.reindex(spatial.co2.locations).fillna(0.).clip(upper=upper_limit).mul(1e6) / annualiser # t
e_nom_max = e_nom_max.rename(index=lambda x: x + " co2 stored")
else:
e_nom_max = np.inf
n.madd("Store",
spatial.co2.nodes,
@ -759,8 +761,8 @@ def add_ammonia(n, costs):
carrier="Haber-Bosch",
efficiency=1 / (cf_industry["MWh_elec_per_tNH3_electrolysis"] / cf_industry["MWh_NH3_per_tNH3"]), # output: MW_NH3 per MW_elec
efficiency2=-cf_industry["MWh_H2_per_tNH3_electrolysis"] / cf_industry["MWh_elec_per_tNH3_electrolysis"], # input: MW_H2 per MW_elec
capital_cost=costs.at["Haber-Bosch synthesis", "fixed"],
lifetime=costs.at["Haber-Bosch synthesis", 'lifetime']
capital_cost=costs.at["Haber-Bosch", "fixed"],
lifetime=costs.at["Haber-Bosch", 'lifetime']
)
n.madd("Link",
@ -2905,6 +2907,9 @@ if __name__ == "__main__":
if "B" in opts:
add_biomass(n, costs)
if options['ammonia']:
add_ammonia(n, costs)
if "I" in opts:
add_industry(n, costs)
@ -2917,9 +2922,6 @@ if __name__ == "__main__":
if options['dac']:
add_dac(n, costs)
if options['ammonia']:
add_ammonia(n, costs)
if "decentral" in opts:
decentral(n)