def override_component_attrs(directory):
"""Tell PyPSA that links can have multiple outputs by
overriding the component_attrs. This can be done for
as many buses as you need with format busi for i = 2,3,4,5,....
See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
Parameters
----------
directory : string
Folder where component attributes to override are stored
analogous to ``pypsa/component_attrs``, e.g. `links.csv`.
Returns
-------
Dictionary of overriden component attributes.
"""
attrs = Dict({k: v.copy() for k, v in component_attrs.items()})
for component, list_name in components.list_name.items():
fn = f"{directory}/{list_name}.csv"
if os.path.isfile(fn):
overrides = pd.read_csv(fn, index_col=0, na_values="n/a")
attrs[component] = overrides.combine_first(attrs[component])
return attrs
def approximate(pcs, M=0):
'''
Approximation for decomposition components, with updated mean value. The
latter changes as it consists of the mean values of none included components.
'''
vec = pcs.vec.loc[:, :M]
val = pcs.val[:M]
beta = pcs.beta.loc[:, :M]
mean = pcs.mean
mean = mean - (mean @ vec * vec).sum(1)
return Dict(vec=vec, val=val, beta=beta, mean=mean)
def decomposition_pcs(df, abbrev=None):
'''
PCA without substracting mean without scaling the covariance matrix. The
eigenvalues of those components reflect the real variance covered by the
components.
'''
mean = df.mean()
C = covariance(df)
val, vec = eig(C)
beta = df @ vec
return Dict(vec=vec, val=val, beta=beta, mean=mean, C=C, abbr=abbrev)
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 snakemake as sm
import os
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
workflow = sm.Workflow(snakefile)
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
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 variance_pcs(df, abbrev=None):
'''
Ordinary PCA with substracting mean from original data set and scaling of the
Covariance matrix.
'''
mean = df.mean()
C = covariance(df - mean)
trace = np.trace(C)
C /= trace
val, vec = eig(C)
beta = (df - df.mean()) @ vec
return Dict(vec=vec,
val=val,
beta=beta,
mean=mean,
C=C,
abbr=abbrev,
trace=trace)
fig.tight_layout()
fig.savefig("paper_graphics/{}/compare-sys_cost-co2-{}.pdf".format(
scenario, scenario),
transparent=True)
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' in globals():
fn = snakemake.input["summary"]
config = snakemake.config
else:
from pypsa.descriptors import Dict
config = Dict()
config["run"] = "190919-final"
config["results_dir"] = "results"
fn = "{}/{}/csvs/summary.csv".format(config["results_dir"],
config["run"])
scenario = config["run"]
df = pd.read_csv(fn, index_col=[0, 1])
assumptions = "wind1040-sola510"
pen = "075"
ylim_comparison = 140.
for policy in [
"pen{}{}-{}-nuclNone-lCCSNone".format(pen, "{}", assumptions),
import pypsa, pandas as pd, numpy as np
from pypsa.descriptors import Dict
from pyomo.environ import Constraint
override_components = pypsa.components.components.copy()
override_components.loc["ShadowPrice"] = [
"shadow_prices", "Shadow price for a global constraint.", np.nan
]
override_components.loc["CHP"] = [
"chps", "Combined heat and power plant.", np.nan
]
override_component_attrs = Dict(
{k: v.copy()
for k, v in pypsa.components.component_attrs.items()})
override_component_attrs["ShadowPrice"] = pd.DataFrame(
columns=["type", "unit", "default", "description", "status"])
override_component_attrs["ShadowPrice"].loc["name"] = [
"string", "n/a", "n/a", "Unique name", "Input (required)"
]
override_component_attrs["ShadowPrice"].loc["value"] = [
"float", "n/a", 0., "shadow value", "Output"
]
override_component_attrs["CHP"] = pd.DataFrame(
columns=["type", "unit", "default", "description", "status"])
override_component_attrs["CHP"].loc["name"] = [
"string", "n/a", "n/a", "Unique name", "Input (required)"
]
network.generators.p_nom_opt).sum()
used = network.generators_t.p.sum()
curtailment = (available - used) / available
load = network.loads_t.p.sum().sum()
supply = available / load
stats.loc["wcurt", (scenario, ct)] = curtailment[ct + " wind"]
stats.loc["scurt", (scenario, ct)] = curtailment[ct + " solar"]
stats.loc["wsupply", (scenario, ct)] = supply[ct + " wind"]
stats.loc["ssupply", (scenario, ct)] = supply[ct + " solar"]
stats.to_csv(snakemake.output[0])
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from pypsa.descriptors import Dict
import yaml
snakemake = Dict()
with open('config.yaml') as f:
snakemake.config = yaml.load(f)
snakemake["output"] = [
"{}summary.csv".format(snakemake.config["results_dir"])
]
make_csv()
network.consistency_check()
network.lopf(solver_name=solver_name,
solver_options=solver_options,
extra_functionality=extra_functionality)
network.export_to_netcdf(snakemake.output[0])
return network
if __name__ == "__main__":
# Detect running outside of snakemake and mock up snakemake for testing
if 'snakemake' not in globals():
from pypsa.descriptors import Dict
import yaml
snakemake = Dict()
with open('config.yaml') as f:
snakemake.config = yaml.load(f)
snakemake["wildcards"] = Dict({ "country" : "DE",
"scenario" : "2020"})
snakemake["output"] = ["results/{}-{}.nc".format(snakemake.wildcards.country,
snakemake.wildcards.scenario)]
network = solve_network(snakemake.wildcards.country,
snakemake.wildcards.scenario)
