# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Prepare PyPSA network for solving according to :ref:`opts` and :ref:`ll`, such as
- adding an annual **limit** of carbon-dioxide emissions,
- adding an exogenous **price** per tonne emissions of carbon-dioxide (or other kinds),
- setting an **N-1 security margin** factor for transmission line capacities,
- specifying an expansion limit on the **cost** of transmission expansion,
- specifying an expansion limit on the **volume** of transmission expansion, and
- reducing the **temporal** resolution by averaging over multiple hours
or segmenting time series into chunks of varying lengths using ``tsam``.
Relevant Settings
-----------------
.. code:: yaml
costs:
year:
version:
fill_values:
emission_prices:
marginal_cost:
capital_cost:
electricity:
co2limit:
max_hours:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`costs_cf`, :ref:`electricity_cf`
Inputs
------
- ``resources/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``networks/elec_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
Outputs
-------
- ``networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: Complete PyPSA network that will be handed to the ``solve_network`` rule.
Description
-----------
.. tip::
The rule :mod:`prepare_all_networks` runs
for all ``scenario`` s in the configuration file
the rule :mod:`prepare_network`.
"""
import logging
from _helpers import configure_logging
import re
import pypsa
import numpy as np
import pandas as pd
from add_electricity import load_costs, update_transmission_costs
idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def add_co2limit(n, co2limit, Nyears=1.):
n.add("GlobalConstraint", "CO2Limit",
carrier_attribute="co2_emissions", sense="<=",
constant=co2limit * Nyears)
def add_gaslimit(n, gaslimit, Nyears=1.):
sel = n.carriers.index.intersection(["OCGT", "CCGT", "CHP"])
n.carriers.loc[sel, "gas_usage"] = 1.
n.add("GlobalConstraint", "GasLimit",
carrier_attribute="gas_usage", sense="<=",
constant=gaslimit * Nyears)
def add_emission_prices(n, emission_prices={'co2': 0.}, exclude_co2=False):
if exclude_co2: emission_prices.pop('co2')
ep = (pd.Series(emission_prices).rename(lambda x: x+'_emissions') *
n.carriers.filter(like='_emissions')).sum(axis=1)
gen_ep = n.generators.carrier.map(ep) / n.generators.efficiency
n.generators['marginal_cost'] += gen_ep
su_ep = n.storage_units.carrier.map(ep) / n.storage_units.efficiency_dispatch
n.storage_units['marginal_cost'] += su_ep
def set_line_s_max_pu(n, s_max_pu = 0.7):
n.lines['s_max_pu'] = s_max_pu
logger.info(f"N-1 security margin of lines set to {s_max_pu}")
def set_transmission_limit(n, ll_type, factor, costs, Nyears=1):
links_dc_b = n.links.carrier == 'DC' if not n.links.empty else pd.Series()
_lines_s_nom = (np.sqrt(3) * n.lines.type.map(n.line_types.i_nom) *
n.lines.num_parallel * n.lines.bus0.map(n.buses.v_nom))
lines_s_nom = n.lines.s_nom.where(n.lines.type == '', _lines_s_nom)
col = 'capital_cost' if ll_type == 'c' else 'length'
ref = (lines_s_nom @ n.lines[col] +
n.links.loc[links_dc_b, "p_nom"] @ n.links.loc[links_dc_b, col])
update_transmission_costs(n, costs)
if factor == 'opt' or float(factor) > 1.0:
n.lines['s_nom_min'] = lines_s_nom
n.lines['s_nom_extendable'] = True
n.links.loc[links_dc_b, 'p_nom_min'] = n.links.loc[links_dc_b, 'p_nom']
n.links.loc[links_dc_b, 'p_nom_extendable'] = True
if factor != 'opt':
con_type = 'expansion_cost' if ll_type == 'c' else 'volume_expansion'
rhs = float(factor) * ref
n.add('GlobalConstraint', f'l{ll_type}_limit',
type=f'transmission_{con_type}_limit',
sense='<=', constant=rhs, carrier_attribute='AC, DC')
return n
def average_every_nhours(n, offset):
logger.info(f"Resampling the network to {offset}")
m = n.copy(with_time=False)
snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
m.set_snapshots(snapshot_weightings.index)
m.snapshot_weightings = snapshot_weightings
for c in n.iterate_components():
pnl = getattr(m, c.list_name+"_t")
for k, df in c.pnl.items():
if not df.empty:
pnl[k] = df.resample(offset).mean()
return m
def apply_time_segmentation(n, segments, solver_name="cbc"):
logger.info(f"Aggregating time series to {segments} segments.")
try:
import tsam.timeseriesaggregation as tsam
except:
raise ModuleNotFoundError("Optional dependency 'tsam' not found."
"Install via 'pip install tsam'")
p_max_pu_norm = n.generators_t.p_max_pu.max()
p_max_pu = n.generators_t.p_max_pu / p_max_pu_norm
load_norm = n.loads_t.p_set.max()
load = n.loads_t.p_set / load_norm
inflow_norm = n.storage_units_t.inflow.max()
inflow = n.storage_units_t.inflow / inflow_norm
raw = pd.concat([p_max_pu, load, inflow], axis=1, sort=False)
agg = tsam.TimeSeriesAggregation(raw, hoursPerPeriod=len(raw),
noTypicalPeriods=1, noSegments=int(segments),
segmentation=True, solver=solver_name)
segmented = agg.createTypicalPeriods()
weightings = segmented.index.get_level_values("Segment Duration")
offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
snapshots = [n.snapshots[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
n.set_snapshots(pd.DatetimeIndex(snapshots, name='name'))
n.snapshot_weightings = pd.Series(weightings, index=snapshots, name="weightings", dtype="float64")
segmented.index = snapshots
n.generators_t.p_max_pu = segmented[n.generators_t.p_max_pu.columns] * p_max_pu_norm
n.loads_t.p_set = segmented[n.loads_t.p_set.columns] * load_norm
n.storage_units_t.inflow = segmented[n.storage_units_t.inflow.columns] * inflow_norm
return n
def enforce_autarky(n, only_crossborder=False):
if only_crossborder:
lines_rm = n.lines.loc[
n.lines.bus0.map(n.buses.country) !=
n.lines.bus1.map(n.buses.country)
].index
links_rm = n.links.loc[
n.links.bus0.map(n.buses.country) !=
n.links.bus1.map(n.buses.country)
].index
else:
lines_rm = n.lines.index
links_rm = n.links.loc[n.links.carrier=="DC"].index
n.mremove("Line", lines_rm)
n.mremove("Link", links_rm)
def set_line_nom_max(n, s_nom_max_set=np.inf, p_nom_max_set=np.inf):
n.lines.s_nom_max.clip(upper=s_nom_max_set, inplace=True)
n.links.p_nom_max.clip(upper=p_nom_max_set, inplace=True)
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('prepare_network', simpl='',
clusters='40', ll='v0.3', opts='Co2L-24H')
configure_logging(snakemake)
opts = snakemake.wildcards.opts.split('-')
n = pypsa.Network(snakemake.input[0])
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
set_line_s_max_pu(n, snakemake.config['lines']['s_max_pu'])
for o in opts:
m = re.match(r'^\d+h$', o, re.IGNORECASE)
if m is not None:
n = average_every_nhours(n, m.group(0))
break
for o in opts:
m = re.match(r'^\d+seg$', o, re.IGNORECASE)
if m is not None:
solver_name = snakemake.config["solving"]["solver"]["name"]
n = apply_time_segmentation(n, m.group(0)[:-3], solver_name)
break
for o in opts:
if "Co2L" in o:
m = re.findall("[0-9]*\.?[0-9]+$", o)
if len(m) > 0:
co2limit = float(m[0]) * snakemake.config['electricity']['co2base']
add_co2limit(n, co2limit, Nyears)
logger.info("Setting CO2 limit according to wildcard value.")
else:
add_co2limit(n, snakemake.config['electricity']['co2limit'], Nyears)
logger.info("Setting CO2 limit according to config value.")
break
for o in opts:
if "CH4L" in o:
m = re.findall("[0-9]*\.?[0-9]+$", o)
if len(m) > 0:
limit = float(m[0]) * 1e6
add_gaslimit(n, limit, Nyears)
logger.info("Setting gas usage limit according to wildcard value.")
else:
add_gaslimit(n, snakemake.config["electricity"].get("gaslimit"), Nyears)
logger.info("Setting gas usage limit according to config value.")
break
for o in opts:
oo = o.split("+")
suptechs = map(lambda c: c.split("-", 2)[0], n.carriers.index)
if oo[0].startswith(tuple(suptechs)):
carrier = oo[0]
# handles only p_nom_max as stores and lines have no potentials
attr_lookup = {"p": "p_nom_max", "c": "capital_cost", "m": "marginal_cost"}
attr = attr_lookup[oo[1][0]]
factor = float(oo[1][1:])
if carrier == "AC": # lines do not have carrier
n.lines[attr] *= factor
else:
comps = {"Generator", "Link", "StorageUnit", "Store"}
for c in n.iterate_components(comps):
sel = c.df.carrier.str.contains(carrier)
c.df.loc[sel,attr] *= factor
for o in opts:
if 'Ep' in o:
m = re.findall("[0-9]*\.?[0-9]+$", o)
if len(m) > 0:
logger.info("Setting emission prices according to wildcard value.")
add_emission_prices(n, dict(co2=float(m[0])))
else:
logger.info("Setting emission prices according to config value.")
add_emission_prices(n, snakemake.config['costs']['emission_prices'])
break
ll_type, factor = snakemake.wildcards.ll[0], snakemake.wildcards.ll[1:]
set_transmission_limit(n, ll_type, factor, costs, Nyears)
set_line_nom_max(n, s_nom_max_set=snakemake.config["lines"].get("s_nom_max,", np.inf),
p_nom_max_set=snakemake.config["links"].get("p_nom_max,", np.inf))
if "ATK" in opts:
enforce_autarky(n)
elif "ATKc" in opts:
enforce_autarky(n, only_crossborder=True)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])