348 lines
11 KiB
Python
348 lines
11 KiB
Python
# -*- coding: utf-8 -*-
|
|
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
|
|
#
|
|
# SPDX-License-Identifier: MIT
|
|
|
|
from pathlib import Path
|
|
|
|
import pandas as pd
|
|
|
|
REGION_COLS = ["geometry", "name", "x", "y", "country"]
|
|
|
|
|
|
def configure_logging(snakemake, skip_handlers=False):
|
|
"""
|
|
Configure the basic behaviour for the logging module.
|
|
|
|
Note: Must only be called once from the __main__ section of a script.
|
|
|
|
The setup includes printing log messages to STDERR and to a log file defined
|
|
by either (in priority order): snakemake.log.python, snakemake.log[0] or "logs/{rulename}.log".
|
|
Additional keywords from logging.basicConfig are accepted via the snakemake configuration
|
|
file under snakemake.config.logging.
|
|
|
|
Parameters
|
|
----------
|
|
snakemake : snakemake object
|
|
Your snakemake object containing a snakemake.config and snakemake.log.
|
|
skip_handlers : True | False (default)
|
|
Do (not) skip the default handlers created for redirecting output to STDERR and file.
|
|
"""
|
|
import logging
|
|
|
|
kwargs = snakemake.config.get("logging", dict()).copy()
|
|
kwargs.setdefault("level", "INFO")
|
|
|
|
if skip_handlers is False:
|
|
fallback_path = Path(__file__).parent.joinpath(
|
|
"..", "logs", f"{snakemake.rule}.log"
|
|
)
|
|
logfile = snakemake.log.get(
|
|
"python", snakemake.log[0] if snakemake.log else fallback_path
|
|
)
|
|
kwargs.update(
|
|
{
|
|
"handlers": [
|
|
# Prefer the 'python' log, otherwise take the first log for each
|
|
# Snakemake rule
|
|
logging.FileHandler(logfile),
|
|
logging.StreamHandler(),
|
|
]
|
|
}
|
|
)
|
|
logging.basicConfig(**kwargs)
|
|
|
|
|
|
def load_network(import_name=None, custom_components=None):
|
|
"""
|
|
Helper for importing a pypsa.Network with additional custom components.
|
|
|
|
Parameters
|
|
----------
|
|
import_name : str
|
|
As in pypsa.Network(import_name)
|
|
custom_components : dict
|
|
Dictionary listing custom components.
|
|
For using ``snakemake.config['override_components']``
|
|
in ``config.yaml`` define:
|
|
|
|
.. code:: yaml
|
|
|
|
override_components:
|
|
ShadowPrice:
|
|
component: ["shadow_prices","Shadow price for a global constraint.",np.nan]
|
|
attributes:
|
|
name: ["string","n/a","n/a","Unique name","Input (required)"]
|
|
value: ["float","n/a",0.,"shadow value","Output"]
|
|
|
|
Returns
|
|
-------
|
|
pypsa.Network
|
|
"""
|
|
import pypsa
|
|
from pypsa.descriptors import Dict
|
|
|
|
override_components = None
|
|
override_component_attrs = None
|
|
|
|
if custom_components is not None:
|
|
override_components = pypsa.components.components.copy()
|
|
override_component_attrs = Dict(
|
|
{k: v.copy() for k, v in pypsa.components.component_attrs.items()}
|
|
)
|
|
for k, v in custom_components.items():
|
|
override_components.loc[k] = v["component"]
|
|
override_component_attrs[k] = pd.DataFrame(
|
|
columns=["type", "unit", "default", "description", "status"]
|
|
)
|
|
for attr, val in v["attributes"].items():
|
|
override_component_attrs[k].loc[attr] = val
|
|
|
|
return pypsa.Network(
|
|
import_name=import_name,
|
|
override_components=override_components,
|
|
override_component_attrs=override_component_attrs,
|
|
)
|
|
|
|
|
|
def pdbcast(v, h):
|
|
return pd.DataFrame(
|
|
v.values.reshape((-1, 1)) * h.values, index=v.index, columns=h.index
|
|
)
|
|
|
|
|
|
def load_network_for_plots(fn, tech_costs, config, combine_hydro_ps=True):
|
|
import pypsa
|
|
from add_electricity import load_costs, update_transmission_costs
|
|
|
|
n = pypsa.Network(fn)
|
|
|
|
n.loads["carrier"] = n.loads.bus.map(n.buses.carrier) + " load"
|
|
n.stores["carrier"] = n.stores.bus.map(n.buses.carrier)
|
|
|
|
n.links["carrier"] = (
|
|
n.links.bus0.map(n.buses.carrier) + "-" + n.links.bus1.map(n.buses.carrier)
|
|
)
|
|
n.lines["carrier"] = "AC line"
|
|
n.transformers["carrier"] = "AC transformer"
|
|
|
|
n.lines["s_nom"] = n.lines["s_nom_min"]
|
|
n.links["p_nom"] = n.links["p_nom_min"]
|
|
|
|
if combine_hydro_ps:
|
|
n.storage_units.loc[
|
|
n.storage_units.carrier.isin({"PHS", "hydro"}), "carrier"
|
|
] = "hydro+PHS"
|
|
|
|
# if the carrier was not set on the heat storage units
|
|
# bus_carrier = n.storage_units.bus.map(n.buses.carrier)
|
|
# n.storage_units.loc[bus_carrier == "heat","carrier"] = "water tanks"
|
|
|
|
Nyears = n.snapshot_weightings.objective.sum() / 8760.0
|
|
costs = load_costs(tech_costs, config["costs"], config["electricity"], Nyears)
|
|
update_transmission_costs(n, costs)
|
|
|
|
return n
|
|
|
|
|
|
def update_p_nom_max(n):
|
|
# if extendable carriers (solar/onwind/...) have capacity >= 0,
|
|
# e.g. existing assets from the OPSD project are included to the network,
|
|
# the installed capacity might exceed the expansion limit.
|
|
# Hence, we update the assumptions.
|
|
|
|
n.generators.p_nom_max = n.generators[["p_nom_min", "p_nom_max"]].max(1)
|
|
|
|
|
|
def aggregate_p_nom(n):
|
|
return pd.concat(
|
|
[
|
|
n.generators.groupby("carrier").p_nom_opt.sum(),
|
|
n.storage_units.groupby("carrier").p_nom_opt.sum(),
|
|
n.links.groupby("carrier").p_nom_opt.sum(),
|
|
n.loads_t.p.groupby(n.loads.carrier, axis=1).sum().mean(),
|
|
]
|
|
)
|
|
|
|
|
|
def aggregate_p(n):
|
|
return pd.concat(
|
|
[
|
|
n.generators_t.p.sum().groupby(n.generators.carrier).sum(),
|
|
n.storage_units_t.p.sum().groupby(n.storage_units.carrier).sum(),
|
|
n.stores_t.p.sum().groupby(n.stores.carrier).sum(),
|
|
-n.loads_t.p.sum().groupby(n.loads.carrier).sum(),
|
|
]
|
|
)
|
|
|
|
|
|
def aggregate_e_nom(n):
|
|
return pd.concat(
|
|
[
|
|
(n.storage_units["p_nom_opt"] * n.storage_units["max_hours"])
|
|
.groupby(n.storage_units["carrier"])
|
|
.sum(),
|
|
n.stores["e_nom_opt"].groupby(n.stores.carrier).sum(),
|
|
]
|
|
)
|
|
|
|
|
|
def aggregate_p_curtailed(n):
|
|
return pd.concat(
|
|
[
|
|
(
|
|
(
|
|
n.generators_t.p_max_pu.sum().multiply(n.generators.p_nom_opt)
|
|
- n.generators_t.p.sum()
|
|
)
|
|
.groupby(n.generators.carrier)
|
|
.sum()
|
|
),
|
|
(
|
|
(n.storage_units_t.inflow.sum() - n.storage_units_t.p.sum())
|
|
.groupby(n.storage_units.carrier)
|
|
.sum()
|
|
),
|
|
]
|
|
)
|
|
|
|
|
|
def aggregate_costs(n, flatten=False, opts=None, existing_only=False):
|
|
components = dict(
|
|
Link=("p_nom", "p0"),
|
|
Generator=("p_nom", "p"),
|
|
StorageUnit=("p_nom", "p"),
|
|
Store=("e_nom", "p"),
|
|
Line=("s_nom", None),
|
|
Transformer=("s_nom", None),
|
|
)
|
|
|
|
costs = {}
|
|
for c, (p_nom, p_attr) in zip(
|
|
n.iterate_components(components.keys(), skip_empty=False), components.values()
|
|
):
|
|
if c.df.empty:
|
|
continue
|
|
if not existing_only:
|
|
p_nom += "_opt"
|
|
costs[(c.list_name, "capital")] = (
|
|
(c.df[p_nom] * c.df.capital_cost).groupby(c.df.carrier).sum()
|
|
)
|
|
if p_attr is not None:
|
|
p = c.pnl[p_attr].sum()
|
|
if c.name == "StorageUnit":
|
|
p = p.loc[p > 0]
|
|
costs[(c.list_name, "marginal")] = (
|
|
(p * c.df.marginal_cost).groupby(c.df.carrier).sum()
|
|
)
|
|
costs = pd.concat(costs)
|
|
|
|
if flatten:
|
|
assert opts is not None
|
|
conv_techs = opts["conv_techs"]
|
|
|
|
costs = costs.reset_index(level=0, drop=True)
|
|
costs = costs["capital"].add(
|
|
costs["marginal"].rename({t: t + " marginal" for t in conv_techs}),
|
|
fill_value=0.0,
|
|
)
|
|
|
|
return costs
|
|
|
|
|
|
def progress_retrieve(url, file):
|
|
import urllib
|
|
|
|
from progressbar import ProgressBar
|
|
|
|
pbar = ProgressBar(0, 100)
|
|
|
|
def dlProgress(count, blockSize, totalSize):
|
|
pbar.update(int(count * blockSize * 100 / totalSize))
|
|
|
|
urllib.request.urlretrieve(url, file, reporthook=dlProgress)
|
|
|
|
|
|
def get_aggregation_strategies(aggregation_strategies):
|
|
# default aggregation strategies that cannot be defined in .yaml format must be specified within
|
|
# the function, otherwise (when defaults are passed in the function's definition) they get lost
|
|
# when custom values are specified in the config.
|
|
|
|
import numpy as np
|
|
from pypsa.networkclustering import _make_consense
|
|
|
|
bus_strategies = dict(country=_make_consense("Bus", "country"))
|
|
bus_strategies.update(aggregation_strategies.get("buses", {}))
|
|
|
|
generator_strategies = {"build_year": lambda x: 0, "lifetime": lambda x: np.inf}
|
|
generator_strategies.update(aggregation_strategies.get("generators", {}))
|
|
|
|
return bus_strategies, generator_strategies
|
|
|
|
|
|
def mock_snakemake(rulename, **wildcards):
|
|
"""
|
|
This function is expected to be executed from the 'scripts'-directory of '
|
|
the snakemake project. It returns a snakemake.script.Snakemake object,
|
|
based on the Snakefile.
|
|
|
|
If a rule has wildcards, you have to specify them in **wildcards.
|
|
|
|
Parameters
|
|
----------
|
|
rulename: str
|
|
name of the rule for which the snakemake object should be generated
|
|
**wildcards:
|
|
keyword arguments fixing the wildcards. Only necessary if wildcards are
|
|
needed.
|
|
"""
|
|
import os
|
|
|
|
import snakemake as sm
|
|
from packaging.version import Version, parse
|
|
from pypsa.descriptors import Dict
|
|
from snakemake.script import Snakemake
|
|
|
|
script_dir = Path(__file__).parent.resolve()
|
|
assert (
|
|
Path.cwd().resolve() == script_dir
|
|
), f"mock_snakemake has to be run from the repository scripts directory {script_dir}"
|
|
os.chdir(script_dir.parent)
|
|
for p in sm.SNAKEFILE_CHOICES:
|
|
if os.path.exists(p):
|
|
snakefile = p
|
|
break
|
|
kwargs = dict(rerun_triggers=[]) if parse(sm.__version__) > Version("7.7.0") else {}
|
|
workflow = sm.Workflow(snakefile, overwrite_configfiles=[], **kwargs)
|
|
workflow.include(snakefile)
|
|
workflow.global_resources = {}
|
|
rule = workflow.get_rule(rulename)
|
|
dag = sm.dag.DAG(workflow, rules=[rule])
|
|
wc = Dict(wildcards)
|
|
job = sm.jobs.Job(rule, dag, wc)
|
|
|
|
def make_accessable(*ios):
|
|
for io in ios:
|
|
for i in range(len(io)):
|
|
io[i] = os.path.abspath(io[i])
|
|
|
|
make_accessable(job.input, job.output, job.log)
|
|
snakemake = Snakemake(
|
|
job.input,
|
|
job.output,
|
|
job.params,
|
|
job.wildcards,
|
|
job.threads,
|
|
job.resources,
|
|
job.log,
|
|
job.dag.workflow.config,
|
|
job.rule.name,
|
|
None,
|
|
)
|
|
# create log and output dir if not existent
|
|
for path in list(snakemake.log) + list(snakemake.output):
|
|
Path(path).parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
os.chdir(script_dir)
|
|
return snakemake
|