Merge branch 'master' into post-merge-param

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virio-andreyana 2023-05-19 16:19:58 +02:00 committed by GitHub
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20 changed files with 78 additions and 75 deletions

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@ -67,7 +67,7 @@ repos:
# Do YAML formatting (before the linter checks it for misses)
- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
rev: v2.8.0
rev: v2.9.0
hooks:
- id: pretty-format-yaml
args: [--autofix, --indent, "2", --preserve-quotes]

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@ -623,9 +623,9 @@ clustering:
solving:
#tmpdir: "path/to/tmp"
options:
formulation: kirchhoff
clip_p_max_pu: 1.e-2
load_shedding: false
transmission_losses: 0
noisy_costs: true
skip_iterations: true
track_iterations: false

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@ -1,7 +1,7 @@
,Unit,Values,Description
options,,,
-- formulation,--,"Any of {'angles', 'kirchhoff', 'cycles', 'ptdf'}","Specifies which variant of linearized power flow formulations to use in the optimisation problem. Recommended is 'kirchhoff'. Explained in `this article <https://arxiv.org/abs/1704.01881>`_."
-- load_shedding,bool/float,"{'true','false', float}","Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh."
-- transmission_losses,int,"[0-9]","Add piecewise linear approximation of transmission losses based on n tangents. Defaults to 0, which means losses are ignored."
-- noisy_costs,bool,"{'true','false'}","Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`."
-- min_iterations,--,int,"Minimum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run."
-- max_iterations,--,int,"Maximum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run."

1 Unit Values Description
2 options
-- formulation -- Any of {'angles', 'kirchhoff', 'cycles', 'ptdf'} Specifies which variant of linearized power flow formulations to use in the optimisation problem. Recommended is 'kirchhoff'. Explained in `this article <https://arxiv.org/abs/1704.01881>`_.
3 -- load_shedding bool/float {'true','false', float} Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh.
4 -- transmission_losses int [0-9] Add piecewise linear approximation of transmission losses based on n tangents. Defaults to 0, which means losses are ignored.
5 -- noisy_costs bool {'true','false'} Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`.
6 -- min_iterations -- int Minimum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run.
7 -- max_iterations -- int Maximum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run.

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@ -23,6 +23,12 @@ Upcoming Release
hydrogen fuel cell. Add switches for both re-electrification options under
``sector: hydrogen_turbine:`` and ``sector: hydrogen_fuel_cell:``.
* Remove ``vresutils`` dependency.
* Add option to include a piecewise linear approximation of transmission losses,
e.g. by setting ``solving: options: transmission_losses: 2`` for an
approximation with two tangents.
PyPSA-Eur 0.8.0 (18th March 2023)
=================================

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@ -9,7 +9,6 @@ sphinxcontrib-bibtex
myst-parser # recommark is deprecated, https://stackoverflow.com/a/71660856/13573820
pypsa
vresutils>=0.3.1
powerplantmatching>=0.5.5
atlite>=0.2.9
dask[distributed]

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@ -133,12 +133,12 @@ The coefficient of performance (COP) of air- and ground-sourced heat pumps depen
For the sink water temperature Tsink we assume 55 °C [`Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L207>`_ file]. For the time- and location-dependent source temperatures Tsource, we rely on the `ERA5 <https://doi.org/10.1002/qj.3803>`_ reanalysis weather data. The temperature differences are converted into COP time series using results from a regression analysis performed in the study by `Stafell et al. <https://pubs.rsc.org/en/content/articlelanding/2012/EE/c2ee22653g>`_. For air-sourced heat pumps (ASHP), we use the function:
.. math::
COP (\Delta T) = 6.81 + 0.121\Delta T + 0.000630\Delta T^2
COP (\Delta T) = 6.81 - 0.121\Delta T + 0.000630\Delta T^2
for ground-sourced heat pumps (GSHP), we use the function:
.. math::
COP(\Delta T) = 8.77 + 0.150\Delta T + 0.000734\Delta T^2
COP(\Delta T) = 8.77 - 0.150\Delta T + 0.000734\Delta T^2
**Resistive heaters**

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@ -9,6 +9,6 @@ Support
* In case of code-related **questions**, please post on `stack overflow <https://stackoverflow.com/questions/tagged/pypsa>`_.
* For non-programming related and more general questions please refer to the `mailing list <https://groups.google.com/group/pypsa>`_.
* To **discuss** with other PyPSA users, organise projects, share news, and get in touch with the community you can use the [discord server](https://discord.gg/JTdvaEBb).
* To **discuss** with other PyPSA users, organise projects, share news, and get in touch with the community you can use the `discord server <https://discord.gg/AnuJBk23FU>`_.
* For **bugs and feature requests**, please use the `issue tracker <https://github.com/PyPSA/pypsa-eur/issues>`_.
* We strongly welcome anyone interested in providing **contributions** to this project. If you have any ideas, suggestions or encounter problems, feel invited to file issues or make pull requests on `Github <https://github.com/PyPSA/PyPSA>`_. For further information on how to contribute, please refer to :ref:`contributing`.

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@ -226,7 +226,7 @@ dependencies:
- nspr=4.35
- nss=3.88
- numexpr=2.8.3
- numpy=1.23.5
- numpy=1.24
- openjdk=17.0.3
- openjpeg=2.5.0
- openpyxl=3.1.0
@ -378,4 +378,3 @@ dependencies:
- highspy==1.5.0.dev0
- pybind11==2.10.3
- tsam==2.2.2
- vresutils==0.3.1

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@ -10,7 +10,7 @@ dependencies:
- python>=3.8
- pip
- pypsa>=0.21.3
- pypsa>=0.23
- atlite>=0.2.9
- dask
@ -25,7 +25,7 @@ dependencies:
- pytables
- lxml
- powerplantmatching>=0.5.5
- numpy<1.24
- numpy
- pandas>=1.4
- geopandas>=0.11.0
- xarray
@ -55,5 +55,4 @@ dependencies:
- rasterio!=1.2.10
- pip:
- vresutils>=0.3.1
- tsam>=1.1.0

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@ -16,8 +16,6 @@ rule solve_network:
),
python=LOGS
+ "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_python.log",
memory=LOGS
+ "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_memory.log",
benchmark:
BENCHMARKS + "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}"
threads: 4
@ -45,8 +43,6 @@ rule solve_operations_network:
),
python=LOGS
+ "solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_python.log",
memory=LOGS
+ "solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_memory.log",
benchmark:
(
BENCHMARKS

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@ -100,8 +100,6 @@ rule solve_sector_network_myopic:
+ "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
python=LOGS
+ "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_python.log",
memory=LOGS
+ "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_memory.log",
threads: 4
resources:
mem_mb=config["solving"]["mem"],

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@ -23,8 +23,6 @@ rule solve_sector_network:
+ "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
python=LOGS
+ "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_python.log",
memory=LOGS
+ "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_memory.log",
threads: config["solving"]["solver"].get("threads", 4)
resources:
mem_mb=config["solving"]["mem"],

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@ -85,16 +85,18 @@ It further adds extendable ``generators`` with **zero** capacity for
"""
import logging
from itertools import product
import geopandas as gpd
import numpy as np
import pandas as pd
import powerplantmatching as pm
import pypsa
import scipy.sparse as sparse
import xarray as xr
from _helpers import configure_logging, update_p_nom_max
from powerplantmatching.export import map_country_bus
from vresutils import transfer as vtransfer
from shapely.prepared import prep
idx = pd.IndexSlice
@ -216,6 +218,21 @@ def load_powerplants(ppl_fn):
)
def shapes_to_shapes(orig, dest):
"""
Adopted from vresutils.transfer.Shapes2Shapes()
"""
orig_prepped = list(map(prep, orig))
transfer = sparse.lil_matrix((len(dest), len(orig)), dtype=float)
for i, j in product(range(len(dest)), range(len(orig))):
if orig_prepped[j].intersects(dest[i]):
area = orig[j].intersection(dest[i]).area
transfer[i, j] = area / dest[i].area
return transfer
def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.0):
substation_lv_i = n.buses.index[n.buses["substation_lv"]]
regions = gpd.read_file(regions).set_index("name").reindex(substation_lv_i)
@ -232,9 +249,7 @@ def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.0):
return pd.DataFrame({group.index[0]: l})
else:
nuts3_cntry = nuts3.loc[nuts3.country == cntry]
transfer = vtransfer.Shapes2Shapes(
group, nuts3_cntry.geometry, normed=False
).T.tocsr()
transfer = shapes_to_shapes(group, nuts3_cntry.geometry).T.tocsr()
gdp_n = pd.Series(
transfer.dot(nuts3_cntry["gdp"].fillna(1.0).values), index=group.index
)
@ -403,7 +418,9 @@ def attach_conventional_generators(
if f"conventional_{carrier}_{attr}" in conventional_inputs:
# Values affecting generators of technology k country-specific
# First map generator buses to countries; then map countries to p_max_pu
values = pd.read_csv(values, index_col=0).iloc[:, 0]
values = pd.read_csv(
snakemake.input[f"conventional_{carrier}_{attr}"], index_col=0
).iloc[:, 0]
bus_values = n.buses.country.map(values)
n.generators[attr].update(
n.generators.loc[idx].bus.map(bus_values).dropna()

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@ -234,6 +234,7 @@ def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
manual = gpd.GeoDataFrame(
[["BA1", "BA", 3871.0], ["RS1", "RS", 7210.0], ["AL1", "AL", 2893.0]],
columns=["NUTS_ID", "country", "pop"],
geometry=gpd.GeoSeries(),
)
manual["geometry"] = manual["country"].map(country_shapes)
manual = manual.dropna()

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@ -22,13 +22,13 @@ from _helpers import (
override_component_attrs,
update_config_with_sector_opts,
)
from add_electricity import calculate_annuity
from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
from networkx.algorithms import complement
from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
from pypsa.geo import haversine_pts
from pypsa.io import import_components_from_dataframe
from scipy.stats import beta
from vresutils.costdata import annuity
logger = logging.getLogger(__name__)
@ -742,7 +742,7 @@ def prepare_costs(cost_file, params, nyears):
costs = costs.fillna(params["fill_values"])
def annuity_factor(v):
return annuity(v["lifetime"], v["discount rate"]) + v["FOM"] / 100
return calculate_annuity(v["lifetime"], v["discount rate"]) + v["FOM"] / 100
costs["fixed"] = [
annuity_factor(v) * v["investment"] * nyears for i, v in costs.iterrows()
@ -851,7 +851,7 @@ def add_wave(n, wave_cost_factor):
capacity = pd.Series({"Attenuator": 750, "F2HB": 1000, "MultiPA": 600})
# in EUR/MW
annuity_factor = annuity(25, 0.07) + 0.03
annuity_factor = calculate_annuity(25, 0.07) + 0.03
costs = (
1e6
* wave_cost_factor

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@ -58,9 +58,8 @@ if __name__ == "__main__":
else:
url = "https://zenodo.org/record/3517935/files/pypsa-eur-data-bundle.tar.xz"
# Save locations
tarball_fn = Path(f"{rootpath}/bundle.tar.xz")
to_fn = Path(f"{rootpath}/data")
to_fn = Path(rootpath) / Path(snakemake.output[0]).parent.parent
logger.info(f"Downloading databundle from '{url}'.")
disable_progress = snakemake.config["run"].get("disable_progressbar", False)

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@ -29,7 +29,7 @@ if __name__ == "__main__":
# Save locations
zip_fn = Path(f"{rootpath}/IGGIELGN.zip")
to_fn = Path(f"{rootpath}/data/gas_network/scigrid-gas")
to_fn = Path(rootpath) / Path(snakemake.output[0]).parent.parent
logger.info(f"Downloading databundle from '{url}'.")
disable_progress = snakemake.config["run"].get("disable_progressbar", False)

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@ -10,23 +10,25 @@ import logging
logger = logging.getLogger(__name__)
import os
import sys
import tarfile
from pathlib import Path
# Add pypsa-eur scripts to path for import of _helpers
sys.path.insert(0, os.getcwd() + "/../pypsa-eur/scripts")
from _helpers import configure_logging, progress_retrieve
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("retrieve_databundle")
rootpath = ".."
else:
rootpath = "."
configure_logging(snakemake)
url = "https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz"
tarball_fn = Path("sector-bundle.tar.gz")
to_fn = Path("data")
tarball_fn = Path(f"{rootpath}/sector-bundle.tar.gz")
to_fn = Path(rootpath) / Path(snakemake.output[0]).parent.parent
logger.info(f"Downloading databundle from '{url}'.")
disable_progress = snakemake.config["run"].get("disable_progressbar", False)

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@ -38,7 +38,6 @@ from _helpers import (
override_component_attrs,
update_config_with_sector_opts,
)
from vresutils.benchmark import memory_logger
logger = logging.getLogger(__name__)
pypsa.pf.logger.setLevel(logging.WARNING)
@ -601,6 +600,7 @@ def solve_network(n, config, opts="", **kwargs):
track_iterations = cf_solving.get("track_iterations", False)
min_iterations = cf_solving.get("min_iterations", 4)
max_iterations = cf_solving.get("max_iterations", 6)
transmission_losses = cf_solving.get("transmission_losses", 0)
# add to network for extra_functionality
n.config = config
@ -614,6 +614,7 @@ def solve_network(n, config, opts="", **kwargs):
if skip_iterations:
status, condition = n.optimize(
solver_name=solver_name,
transmission_losses=transmission_losses,
extra_functionality=extra_functionality,
**solver_options,
**kwargs,
@ -624,6 +625,7 @@ def solve_network(n, config, opts="", **kwargs):
track_iterations=track_iterations,
min_iterations=min_iterations,
max_iterations=max_iterations,
transmission_losses=transmission_losses,
extra_functionality=extra_functionality,
**solver_options,
**kwargs,
@ -667,23 +669,17 @@ if __name__ == "__main__":
np.random.seed(solve_opts.get("seed", 123))
fn = getattr(snakemake.log, "memory", None)
with memory_logger(filename=fn, interval=30.0) as mem:
if "overrides" in snakemake.input.keys():
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(
snakemake.input.network, override_component_attrs=overrides
)
else:
n = pypsa.Network(snakemake.input.network)
if "overrides" in snakemake.input.keys():
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
else:
n = pypsa.Network(snakemake.input.network)
n = prepare_network(n, solve_opts, config=snakemake.config)
n = prepare_network(n, solve_opts, config=snakemake.config)
n = solve_network(
n, config=snakemake.config, opts=opts, log_fn=snakemake.log.solver
)
n = solve_network(
n, config=snakemake.config, opts=opts, log_fn=snakemake.log.solver
)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])
logger.info("Maximum memory usage: {}".format(mem.mem_usage))
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])

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@ -17,7 +17,6 @@ from _helpers import (
update_config_with_sector_opts,
)
from solve_network import prepare_network, solve_network
from vresutils.benchmark import memory_logger
logger = logging.getLogger(__name__)
@ -46,23 +45,17 @@ if __name__ == "__main__":
np.random.seed(solve_opts.get("seed", 123))
fn = getattr(snakemake.log, "memory", None)
with memory_logger(filename=fn, interval=30.0) as mem:
if "overrides" in snakemake.input:
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(
snakemake.input.network, override_component_attrs=overrides
)
else:
n = pypsa.Network(snakemake.input.network)
if "overrides" in snakemake.input:
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
else:
n = pypsa.Network(snakemake.input.network)
n.optimize.fix_optimal_capacities()
n = prepare_network(n, solve_opts, config=snakemake.config)
n = solve_network(
n, config=snakemake.config, opts=opts, log_fn=snakemake.log.solver
)
n.optimize.fix_optimal_capacities()
n = prepare_network(n, solve_opts, config=snakemake.config)
n = solve_network(
n, config=snakemake.config, opts=opts, log_fn=snakemake.log.solver
)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])
logger.info("Maximum memory usage: {}".format(mem.mem_usage))
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])