# coding: utf-8
"""
Prepare PyPSA network for solving according to the ``{opts}`` wildcard (:ref:`opts`).
Relevant Settings
-----------------
.. code:: yaml
costs:
emission_prices:
USD2013_to_EUR2013:
discountrate:
marginal_cost:
capital_cost:
electricity:
co2limit:
max_hours:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`costs_cf`, :ref:`electricity_cf`
Inputs
------
- ``data/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/{network}_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
Outputs
-------
- ``networks/{network}_s{simpl}_{clusters}_l{ll}_{opts}.nc``: Complete PyPSA network that will be handed to the ``solve_network`` rule.
Description
-----------
.. tip::
The rule ``prepare_all_networks`` runs
for all ``scenario`` s in the configuration file
the rule ``prepare_network``.
"""
import logging
logger = logging.getLogger(__name__)
import pandas as pd
idx = pd.IndexSlice
import numpy as np
import scipy as sp
import xarray as xr
import re
from six import iteritems
import geopandas as gpd
import pypsa
from add_electricity import load_costs, update_transmission_costs
def add_co2limit(n, Nyears=1.):
n.add("GlobalConstraint", "CO2Limit",
carrier_attribute="co2_emissions", sense="<=",
constant=snakemake.config['electricity']['co2limit'] * Nyears)
def add_emission_prices(n, emission_prices=None, exclude_co2=False):
assert False, "Needs to be fixed, adds NAN"
if emission_prices is None:
emission_prices = snakemake.config['costs']['emission_prices']
if exclude_co2: emission_prices.pop('co2')
ep = (pd.Series(emission_prices).rename(lambda x: x+'_emissions') * n.carriers).sum(axis=1)
n.generators['marginal_cost'] += n.generators.carrier.map(ep)
n.storage_units['marginal_cost'] += n.storage_units.carrier.map(ep)
def set_line_s_max_pu(n):
# set n-1 security margin to 0.5 for 37 clusters and to 0.7 from 200 clusters
n_clusters = len(n.buses)
s_max_pu = np.clip(0.5 + 0.2 * (n_clusters - 37) / (200 - 37), 0.5, 0.7)
n.lines['s_max_pu'] = s_max_pu
def set_line_cost_limit(n, lc, Nyears=1.):
links_dc_b = n.links.carrier == 'DC' if not n.links.empty else pd.Series()
lines_s_nom = n.lines.s_nom.where(
n.lines.type == '',
np.sqrt(3) * n.lines.num_parallel *
n.lines.type.map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom)
)
n.lines['capital_cost_lc'] = n.lines['capital_cost']
n.links['capital_cost_lc'] = n.links['capital_cost']
total_line_cost = ((lines_s_nom * n.lines['capital_cost_lc']).sum() +
n.links.loc[links_dc_b].eval('p_nom * capital_cost_lc').sum())
if lc == 'opt':
costs = load_costs(Nyears, snakemake.input.tech_costs,
snakemake.config['costs'], snakemake.config['electricity'])
update_transmission_costs(n, costs, simple_hvdc_costs=False)
else:
# Either line_volume cap or cost
n.lines['capital_cost'] = 0.
n.links.loc[links_dc_b, 'capital_cost'] = 0.
if lc == 'opt' or float(lc) > 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 lc != 'opt':
n.line_cost_limit = float(lc) * total_line_cost
return n
def set_line_volume_limit(n, lv, Nyears=1.):
links_dc_b = n.links.carrier == 'DC' if not n.links.empty else pd.Series()
lines_s_nom = n.lines.s_nom.where(
n.lines.type == '',
np.sqrt(3) * n.lines.num_parallel *
n.lines.type.map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom)
)
total_line_volume = ((lines_s_nom * n.lines['length']).sum() +
n.links.loc[links_dc_b].eval('p_nom * length').sum())
if lv == 'opt':
costs = load_costs(Nyears, snakemake.input.tech_costs,
snakemake.config['costs'], snakemake.config['electricity'])
update_transmission_costs(n, costs, simple_hvdc_costs=True)
else:
# Either line_volume cap or cost
n.lines['capital_cost'] = 0.
n.links.loc[links_dc_b, 'capital_cost'] = 0.
if lv == 'opt' or float(lv) > 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 lv != 'opt':
n.line_volume_limit = float(lv) * total_line_volume
return n
def average_every_nhours(n, offset):
logger.info('Resampling the network to {}'.format(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 iteritems(c.pnl):
if not df.empty:
pnl[k] = df.resample(offset).mean()
return m
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils.snakemake import MockSnakemake
snakemake = MockSnakemake(
wildcards=dict(network='elec', simpl='', clusters='37', ll='v2', opts='Co2L-3H'),
input=['networks/{network}_s{simpl}_{clusters}.nc'],
output=['networks/{network}_s{simpl}_{clusters}_l{ll}_{opts}.nc']
)
logging.basicConfig(level=snakemake.config['logging_level'])
opts = snakemake.wildcards.opts.split('-')
n = pypsa.Network(snakemake.input[0])
Nyears = n.snapshot_weightings.sum()/8760.
set_line_s_max_pu(n)
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
else:
logger.info("No resampling")
if 'Co2L' in opts:
add_co2limit(n, Nyears)
# add_emission_prices(n, exclude_co2=True)
# if 'Ep' in opts:
# add_emission_prices(n)
ll_type, factor = snakemake.wildcards.ll[0], snakemake.wildcards.ll[1:]
if ll_type == 'v':
set_line_volume_limit(n, factor, Nyears)
elif ll_type == 'c':
set_line_cost_limit(n, factor, Nyears)
n.export_to_netcdf(snakemake.output[0])