Fix nans introduced through marginal_cost

This commit is contained in:
Jonas Hörsch 2018-01-30 23:12:36 +01:00
parent 99299564d6
commit abe88c2d18
3 changed files with 49 additions and 4 deletions

View File

@ -4,7 +4,7 @@ logging_level: INFO
scenario:
sectors: [E] # ,E+EV,E+BEV,E+BEV+V2G] # [ E+EV, E+BEV, E+BEV+V2G ]
lv: [1., 1.125, 1.25, 1.5, 2.0, 3.0]
clusters: [37, 45, 64, 90, 128, 181, 256, 362] # np.r_[37, (2**np.arange(5.5, 9, 0.5)).astype(int)]
clusters: [45, 64, 90, 128, 181, 256, 362] # (2**np.r_[5.5:9:.5]).astype(int)
opts: [Co2L] #, LC-FL, LC-T, Ep-T, Co2L-T]
countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK']
@ -107,6 +107,8 @@ costs:
onwind: 0.015
offwind: 0.015
hydro: 0.
H2: 0.
battery: 0.
emission_prices: # only used with the option Ep (emission prices)
co2: 0.

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@ -60,6 +60,7 @@ def load_costs(Nyears=1.):
capital_cost += link2['capital_cost']
efficiency *= link2['efficiency']**0.5
return pd.Series(dict(capital_cost=capital_cost,
marginal_cost=0.,
efficiency=efficiency,
co2_emissions=0.))

View File

@ -8,7 +8,9 @@ idx = pd.IndexSlice
import numpy as np
import scipy as sp
import xarray as xr
import re
from six import iterkeys
import geopandas as gpd
import pypsa
@ -22,6 +24,8 @@ def add_co2limit(n, Nyears=1.):
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')
@ -29,6 +33,16 @@ def add_emission_prices(n, emission_prices=None, exclude_co2=False):
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 45 clusters and to 0.7 from 200 clusters
n_clusters = len(n.buses)
s_max_pu = np.clip(0.5 + 0.2 * (n_clusters - 45) / (200 - 45), 0.5, 0.7)
n.lines['s_max_pu'] = s_max_pu
dc_b = n.links.carrier == 'DC'
n.links.loc[dc_b, 'p_max_pu'] = s_max_pu
n.links.loc[dc_b, 'p_min_pu'] = - s_max_pu
def set_line_volume_limit(n, lv):
# Either line_volume cap or cost
n.lines['capital_cost'] = 0.
@ -52,6 +66,24 @@ def set_line_volume_limit(n, lv):
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 in iterkeys(c.pnl):
df = c.pnl[k]
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():
@ -71,12 +103,22 @@ if __name__ == "__main__":
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)
# add_emission_prices(n, exclude_co2=True)
if 'Ep' in opts:
add_emission_prices(n)
# if 'Ep' in opts:
# add_emission_prices(n)
set_line_volume_limit(n, float(snakemake.wildcards.lv))