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Merge branch 'master' into reformulate-co2-constraint

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Fabian Neumann 2024-01-18 19:34:27 +01:00 committed by GitHub
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13 changed files with 76 additions and 23 deletions
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2
.pre-commit-config.yaml
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@ -74,7 +74,7 @@ repos:
# Format Snakemake rule / workflow files
- repo: https://github.com/snakemake/snakefmt
rev: v0.8.5
rev: v0.9.0
hooks:
- id: snakefmt

2
config/config.default.yaml
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@ -376,7 +376,7 @@ sector:
2045: 0.8
2050: 1.0
district_heating_loss: 0.15
cluster_heat_buses: false
cluster_heat_buses: true
bev_dsm_restriction_value: 0.75
bev_dsm_restriction_time: 7
transport_heating_deadband_upper: 20.

11
doc/release_notes.rst
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@ -10,6 +10,8 @@ Release Notes
Upcoming Release
================
* Bugfix: Correct technology keys for the electricity production plotting to work out the box.
* New configuration option ``everywhere_powerplants`` to build conventional powerplants everywhere, irrespective of existing powerplants locations, in the network (https://github.com/PyPSA/pypsa-eur/pull/850).
* Remove option for wave energy as technology data is not maintained.
@ -18,6 +20,15 @@ Upcoming Release
CO2 atmosphere store. This gives a more sparse constraint that should improve
the performance of the solving process.
* Bugfix: Assure entering of code block which corrects Norwegian heat demand.
* Add warning when BEV availability weekly profile has negative values in `build_transport_demand`.
* Stacktrace of uncaught exceptions should now be correctly included inside log files (via `configure_logging(..)`).
* Cluster residential and services heat buses by default. Can be disabled with ``cluster_heat_buses: false``.
PyPSA-Eur 0.9.0 (5th January 2024)
==================================

27
scripts/_helpers.py
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@ -80,6 +80,7 @@ def configure_logging(snakemake, skip_handlers=False):
Do (not) skip the default handlers created for redirecting output to STDERR and file.
"""
import logging
import sys
kwargs = snakemake.config.get("logging", dict()).copy()
kwargs.setdefault("level", "INFO")
@ -103,6 +104,16 @@ def configure_logging(snakemake, skip_handlers=False):
)
logging.basicConfig(**kwargs)
# Setup a function to handle uncaught exceptions and include them with their stacktrace into logfiles
def handle_exception(exc_type, exc_value, exc_traceback):
# Log the exception
logger = logging.getLogger()
logger.error(
"Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback)
)
sys.excepthook = handle_exception
def update_p_nom_max(n):
# if extendable carriers (solar/onwind/...) have capacity >= 0,
@ -223,7 +234,13 @@ def progress_retrieve(url, file, disable=False):
urllib.request.urlretrieve(url, file, reporthook=update_to)
def mock_snakemake(rulename, root_dir=None, configfiles=[], **wildcards):
def mock_snakemake(
rulename,
root_dir=None,
configfiles=[],
submodule_dir="workflow/submodules/pypsa-eur",
**wildcards,
):
"""
This function is expected to be executed from the 'scripts'-directory of '
the snakemake project. It returns a snakemake.script.Snakemake object,
@ -239,6 +256,9 @@ def mock_snakemake(rulename, root_dir=None, configfiles=[], **wildcards):
path to the root directory of the snakemake project
configfiles: list, str
list of configfiles to be used to update the config
submodule_dir: str, Path
in case PyPSA-Eur is used as a submodule, submodule_dir is
the path of pypsa-eur relative to the project directory.
**wildcards:
keyword arguments fixing the wildcards. Only necessary if wildcards are
needed.
@ -257,7 +277,10 @@ def mock_snakemake(rulename, root_dir=None, configfiles=[], **wildcards):
root_dir = Path(root_dir).resolve()
user_in_script_dir = Path.cwd().resolve() == script_dir
if user_in_script_dir:
if str(submodule_dir) in __file__:
# the submodule_dir path is only need to locate the project dir
os.chdir(Path(__file__[: __file__.find(str(submodule_dir))]))
elif user_in_script_dir:
os.chdir(root_dir)
elif Path.cwd().resolve() != root_dir:
raise RuntimeError(

2
scripts/build_electricity_production.py
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@ -58,7 +58,7 @@ if __name__ == "__main__":
gen = client.query_generation(country, start=start, end=end, nett=True)
gen = gen.tz_localize(None).resample("1h").mean()
gen = gen.loc[start.tz_localize(None) : end.tz_localize(None)]
gen = gen.rename(columns=carrier_grouper).groupby(level=0, axis=1).sum()
gen = gen.rename(columns=carrier_grouper).T.groupby(level=0).sum().T
generation.append(gen)
except NoMatchingDataError:
unavailable_countries.append(country)

2
scripts/build_energy_totals.py
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@ -479,7 +479,7 @@ def build_energy_totals(countries, eurostat, swiss, idees):
# The main heating source for about 73 per cent of the households is based on electricity
# => 26% is non-electric
if "NO" in df:
if "NO" in df.index:
elec_fraction = 0.73
no_norway = df.drop("NO")

13
scripts/build_transport_demand.py
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@ -8,10 +8,14 @@ improvements due to drivetrain changes, time series for electric vehicle
availability and demand-side management constraints.
"""
import logging
import numpy as np
import pandas as pd
import xarray as xr
from _helpers import generate_periodic_profiles
from _helpers import configure_logging, generate_periodic_profiles
logger = logging.getLogger(__name__)
def build_nodal_transport_data(fn, pop_layout):
@ -130,6 +134,12 @@ def bev_availability_profile(fn, snapshots, nodes, options):
traffic.mean() - traffic.min()
)
if not avail[avail < 0].empty:
logger.warning(
"The BEV availability weekly profile has negative values which can "
"lead to infeasibility."
)
return generate_periodic_profiles(
dt_index=snapshots,
nodes=nodes,
@ -160,6 +170,7 @@ if __name__ == "__main__":
simpl="",
clusters=48,
)
configure_logging(snakemake)
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)

13
scripts/make_summary.py
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@ -521,9 +521,7 @@ def calculate_weighted_prices(n, label, weighted_prices):
if not names.empty:
load += (
n.links_t.p0[names]
.groupby(n.links.loc[names, "bus0"], axis=1)
.sum()
n.links_t.p0[names].T.groupby(n.links.loc[names, "bus0"]).sum().T
)
# Add H2 Store when charging
@ -563,11 +561,10 @@ def calculate_market_values(n, label, market_values):
dispatch = (
n.generators_t.p[gens]
.groupby(n.generators.loc[gens, "bus"], axis=1)
.T.groupby(n.generators.loc[gens, "bus"])
.sum()
.reindex(columns=buses, fill_value=0.0)
.T.reindex(columns=buses, fill_value=0.0)
)
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
@ -586,9 +583,9 @@ def calculate_market_values(n, label, market_values):
dispatch = (
n.links_t["p" + i][links]
.groupby(n.links.loc[links, "bus" + i], axis=1)
.T.groupby(n.links.loc[links, "bus" + i])
.sum()
.reindex(columns=buses, fill_value=0.0)
.T.reindex(columns=buses, fill_value=0.0)
)
revenue = dispatch * n.buses_t.marginal_price[buses]

9
scripts/plot_network.py
View File

@ -271,10 +271,11 @@ def plot_h2_map(network, regions):
assign_location(n)
h2_storage = n.stores.query("carrier == 'H2'")
regions["H2"] = h2_storage.rename(
index=h2_storage.bus.map(n.buses.location)
).e_nom_opt.div(
1e6
regions["H2"] = (
h2_storage.rename(index=h2_storage.bus.map(n.buses.location))
.e_nom_opt.groupby(level=0)
.sum()
.div(1e6)
) # TWh
regions["H2"] = regions["H2"].where(regions["H2"] > 0.1)

8
scripts/plot_summary.py
View File

@ -154,7 +154,7 @@ def plot_costs():
df = df.drop(to_drop)
logger.info(f"Total system cost of {round(df.sum()[0])} EUR billion per year")
logger.info(f"Total system cost of {round(df.sum().iloc[0])} EUR billion per year")
new_index = preferred_order.intersection(df.index).append(
df.index.difference(preferred_order)
@ -214,7 +214,7 @@ def plot_energy():
df = df.drop(to_drop)
logger.info(f"Total energy of {round(df.sum()[0])} TWh/a")
logger.info(f"Total energy of {round(df.sum().iloc[0])} TWh/a")
if df.empty:
fig, ax = plt.subplots(figsize=(12, 8))
@ -304,7 +304,9 @@ def plot_balances():
df = df.drop(to_drop)
logger.debug(f"Total energy balance for {v} of {round(df.sum()[0],2)} {units}")
logger.debug(
f"Total energy balance for {v} of {round(df.sum().iloc[0],2)} {units}"
)
if df.empty:
continue

6
scripts/plot_validation_electricity_production.py
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@ -45,6 +45,12 @@ if __name__ == "__main__":
header=[0, 1],
parse_dates=True,
)
subset_technologies = ["Geothermal", "Nuclear", "Biomass", "Lignite", "Oil", "Coal"]
lowercase_technologies = [
technology.lower() if technology in subset_technologies else technology
for technology in historic.columns.levels[1]
]
historic.columns = historic.columns.set_levels(lowercase_technologies, level=1)
colors = n.carriers.set_index("nice_name").color.where(
lambda s: s != "", "lightgrey"

2
scripts/solve_network.py
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@ -341,6 +341,8 @@ def prepare_network(
for df in (
n.generators_t.p_max_pu,
n.generators_t.p_min_pu,
n.links_t.p_max_pu,
n.links_t.p_min_pu,
n.storage_units_t.inflow,
):
df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)