def map_dual(c, attr):
# If c is a pypsa component name the dual is store at n.pnl(c)
# or n.df(c). For the second case the index of the constraints have to
# be a subset of n.df(c).index otherwise the dual is stored at
# n.duals[c].df
constraints = get_con(n, c, attr, pop=pop)
is_pnl = isinstance(constraints, pd.DataFrame)
sign = 1 if 'upper' in attr or attr == 'marginal_price' else -1
n.dualvalues.at[(c, attr), 'pnl'] = is_pnl
to_component = c in n.all_components
if is_pnl:
n.dualvalues.at[(c, attr), 'in_comp'] = to_component
# changed for netzbooster
duals = constraints.apply(lambda x: x.map(sign * constraints_dual))
if c not in n.duals and not to_component:
n.duals[c] = Dict(df=pd.DataFrame(), pnl={})
pnl = n.pnl(c) if to_component else n.duals[c].pnl
set_from_frame(pnl, attr, duals)
else:
# here to_component can change
duals = constraints.map(sign * constraints_dual)
if to_component:
to_component = (duals.index.isin(n.df(c).index).all())
n.dualvalues.at[(c, attr), 'in_comp'] = to_component
if c not in n.duals and not to_component:
n.duals[c] = Dict(df=pd.DataFrame(), pnl={})
df = n.df(c) if to_component else n.duals[c].df
df[attr] = duals
def map_solution(c, attr):
variables = get_var(n, c, attr, pop=pop)
predefined = True
if (c, attr) not in lookup.index:
predefined = False
n.sols[c] = n.sols[c] if c in n.sols else Dict(df=pd.DataFrame(), pnl={})
n.solutions.at[(c, attr), 'in_comp'] = predefined
if isinstance(variables, pd.DataFrame):
# case that variables are timedependent
n.solutions.at[(c, attr), 'pnl'] = True
pnl = n.pnl(c) if predefined else n.sols[c].pnl
values = variables.apply(lambda x: x.map(variables_sol))
# values = variables.stack().map(variables_sol).unstack()
if c in n.passive_branch_components and attr == "s":
set_from_frame(pnl, 'p0', values)
set_from_frame(pnl, 'p1', - values)
elif c == 'Link' and attr == "p":
set_from_frame(pnl, 'p0', values)
for i in ['1'] + additional_linkports(n):
i_eff = '' if i == '1' else i
eff = get_as_dense(n, 'Link', f'efficiency{i_eff}', sns)
set_from_frame(pnl, f'p{i}', - values * eff)
pnl[f'p{i}'].loc[sns, n.links.index[n.links[f'bus{i}'] == ""]] = \
n.component_attrs['Link'].loc[f'p{i}','default']
else:
set_from_frame(pnl, attr, values)
else:
# case that variables are static
n.solutions.at[(c, attr), 'pnl'] = False
sol = variables.map(variables_sol)
if predefined:
non_ext = n.df(c)[attr]
n.df(c)[attr + '_opt'] = sol.reindex(non_ext.index).fillna(non_ext)
else:
n.sols[c].df[attr] = sol
import xarray as xr
import numpy as np
if __name__ == '__main__':
if 'snakemake' not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_solar_thermal_profiles',
simpl='',
clusters=48,
)
if 'snakemake' not in globals():
from vresutils import Dict
import yaml
snakemake = Dict()
with open('config.yaml') as f:
snakemake.config = yaml.safe_load(f)
snakemake.input = Dict()
snakemake.output = Dict()
config = snakemake.config['solar_thermal']
time = pd.date_range(freq='h', **snakemake.config['snapshots'])
cutout_config = snakemake.config['atlite']['cutout']
cutout = atlite.Cutout(cutout_config).sel(time=time)
clustered_regions = gpd.read_file(
snakemake.input.regions_onshore).set_index('name').buffer(0).squeeze()
I = cutout.indicatormatrix(clustered_regions)
fontsize=18,
loc=(0.01, 0.01),
facecolor='white',
frameon=True)
path_cb_plot = snakemake.config['results_dir'] + snakemake.config[
'run'] + '/graphs/'
plt.savefig(path_cb_plot + 'carbon_budget_plot.pdf', dpi=300)
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils import Dict
import yaml
snakemake = Dict()
with open('config.yaml', encoding='utf8') as f:
snakemake.config = yaml.safe_load(f)
snakemake.input = Dict()
snakemake.output = Dict()
snakemake.wildcards = Dict()
#snakemake.wildcards['sector_opts']='3H-T-H-B-I-solar3-dist1-cb48be3'
for item in ["costs", "energy"]:
snakemake.input[item] = snakemake.config[
'summary_dir'] + '/{name}/csvs/{item}.csv'.format(
name=snakemake.config['run'], item=item)
snakemake.output[item] = snakemake.config[
'summary_dir'] + '/{name}/graphs/{item}.pdf'.format(
name=snakemake.config['run'], item=item)
snakemake.input["balances"] = snakemake.config[
print(missing)
transport_data.loc[missing, "average fuel efficiency"] = transport_data[
"average fuel efficiency"].mean()
transport_data.to_csv(snakemake.output.transport_name)
return transport_data
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils import Dict
snakemake = Dict()
snakemake.output = Dict(energy_name="data/energy_totals.csv")
snakemake.output = Dict(co2_name="data/co2_totals.csv")
snakemake.output = Dict(transport_name="data/transport_data.csv")
year = 2011
eurostat = build_eurostat(year)
swiss_df = build_swiss()
odyssee = build_odyssee()
build_energy_totals(year)
swiss_co2 = build_swiss_co2()
#pd.Series nuts3 code -> polygon
nuts3 = pd.Series(vshapes.nuts3(tolerance=None, minarea=0.))
#takes 10 minutes
pop_map = pd.DataFrame()
for country in countries:
print(country)
country_nuts = mapping.index[mapping == country]
trans_matrix = vtransfer.Shapes2Shapes(np.asarray(nuts3[country_nuts]),
grid_cells)
#CH has missing bits
country_pop = pop[country_nuts].fillna(0.)
pop_map[country] = np.array(
trans_matrix.multiply(np.asarray(country_pop)).sum(axis=1))[:, 0]
with pd.HDFStore(snakemake.output.pop_map_name, mode='w',
complevel=4) as store:
store['population_gridcell_map'] = pop_map
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils import Dict
snakemake = Dict()
snakemake.output = Dict(pop_map_name='data/population_gridcell_map.h5')
build_population_map()
def assign_solution_netzbooster(n,
sns,
variables_sol,
constraints_dual,
keep_references=False,
keep_shadowprices=None):
"""
Helper function. Assigns the solution of a succesful optimization to the
network.
"""
def set_from_frame(pnl, attr, df):
if attr not in pnl: #use this for subnetworks_t
pnl[attr] = df.reindex(n.snapshots)
elif pnl[attr].empty:
pnl[attr] = df.reindex(n.snapshots)
else:
pnl[attr].loc[sns, :] = df.reindex(columns=pnl[attr].columns)
pop = not keep_references
def map_solution(c, attr):
variables = get_var(n, c, attr, pop=pop)
predefined = True
if (c, attr) not in lookup.index:
predefined = False
n.sols[c] = n.sols[c] if c in n.sols else Dict(df=pd.DataFrame(),
pnl={})
n.solutions.at[(c, attr), 'in_comp'] = predefined
if isinstance(variables, pd.DataFrame):
# case that variables are timedependent
n.solutions.at[(c, attr), 'pnl'] = True
pnl = n.pnl(c) if predefined else n.sols[c].pnl
values = variables.apply(lambda x: x.map(variables_sol))
# values = variables.stack().map(variables_sol).unstack()
if c in n.passive_branch_components and attr == "s":
set_from_frame(pnl, 'p0', values)
set_from_frame(pnl, 'p1', -values)
elif c == 'Link' and attr == "p":
set_from_frame(pnl, 'p0', values)
for i in ['1'] + additional_linkports(n):
i_eff = '' if i == '1' else i
eff = get_as_dense(n, 'Link', f'efficiency{i_eff}', sns)
set_from_frame(pnl, f'p{i}', -values * eff)
pnl[f'p{i}'].loc[sns, n.links.index[n.links[f'bus{i}'] == ""]] = \
n.component_attrs['Link'].loc[f'p{i}','default']
else:
set_from_frame(pnl, attr, values)
else:
# case that variables are static
n.solutions.at[(c, attr), 'pnl'] = False
sol = variables.map(variables_sol)
if predefined:
non_ext = n.df(c)[attr]
n.df(c)[attr + '_opt'] = sol.reindex(
non_ext.index).fillna(non_ext)
else:
n.sols[c].df[attr] = sol
n.sols = Dict()
n.solutions = pd.DataFrame(index=n.variables.index,
columns=['in_comp', 'pnl'])
for c, attr in n.variables.index:
map_solution(c, attr)
# if nominal capcity was no variable set optimal value to nominal
for c, attr in lookup.query('nominal').index.difference(n.variables.index):
n.df(c)[attr + '_opt'] = n.df(c)[attr]
# recalculate storageunit net dispatch
if not n.df('StorageUnit').empty:
c = 'StorageUnit'
n.pnl(c)['p'] = n.pnl(c)['p_dispatch'] - n.pnl(c)['p_store']
# duals
if keep_shadowprices == False:
keep_shadowprices = []
sp = n.constraints.index
if isinstance(keep_shadowprices, list):
sp = sp[sp.isin(keep_shadowprices, level=0)]
def map_dual(c, attr):
# If c is a pypsa component name the dual is store at n.pnl(c)
# or n.df(c). For the second case the index of the constraints have to
# be a subset of n.df(c).index otherwise the dual is stored at
# n.duals[c].df
constraints = get_con(n, c, attr, pop=pop)
is_pnl = isinstance(constraints, pd.DataFrame)
sign = 1 if 'upper' in attr or attr == 'marginal_price' else -1
n.dualvalues.at[(c, attr), 'pnl'] = is_pnl
to_component = c in n.all_components
if is_pnl:
n.dualvalues.at[(c, attr), 'in_comp'] = to_component
# changed for netzbooster
duals = constraints.apply(lambda x: x.map(sign * constraints_dual))
if c not in n.duals and not to_component:
n.duals[c] = Dict(df=pd.DataFrame(), pnl={})
pnl = n.pnl(c) if to_component else n.duals[c].pnl
set_from_frame(pnl, attr, duals)
else:
# here to_component can change
duals = constraints.map(sign * constraints_dual)
if to_component:
to_component = (duals.index.isin(n.df(c).index).all())
n.dualvalues.at[(c, attr), 'in_comp'] = to_component
if c not in n.duals and not to_component:
n.duals[c] = Dict(df=pd.DataFrame(), pnl={})
df = n.df(c) if to_component else n.duals[c].df
df[attr] = duals
n.duals = Dict()
n.dualvalues = pd.DataFrame(index=sp, columns=['in_comp', 'pnl'])
# extract shadow prices attached to components
for c, attr in sp:
map_dual(c, attr)
#correct prices for snapshot weightings
n.buses_t.marginal_price.loc[sns] = n.buses_t.marginal_price.loc[
sns].divide(n.snapshot_weightings.loc[sns], axis=0)
# discard remaining if wanted
if not keep_references:
for c, attr in n.constraints.index.difference(sp):
get_con(n, c, attr, pop)
#load
if len(n.loads):
set_from_frame(n.pnl('Load'), 'p',
get_as_dense(n, 'Load', 'p_set', sns))
#clean up vars and cons
for c in list(n.vars):
if n.vars[c].df.empty and n.vars[c].pnl == {}: n.vars.pop(c)
for c in list(n.cons):
if n.cons[c].df.empty and n.cons[c].pnl == {}: n.cons.pop(c)
# recalculate injection
ca = [('Generator', 'p', 'bus'), ('Store', 'p', 'bus'),
('Load', 'p', 'bus'), ('StorageUnit', 'p', 'bus'),
('Link', 'p0', 'bus0'), ('Link', 'p1', 'bus1')]
for i in additional_linkports(n):
ca.append(('Link', f'p{i}', f'bus{i}'))
sign = lambda c: n.df(c).sign if 'sign' in n.df(c
) else -1 #sign for 'Link'
n.buses_t.p = pd.concat(
[n.pnl(c)[attr].mul(sign(c)).rename(columns=n.df(c)[group])
for c, attr, group in ca], axis=1).groupby(level=0, axis=1).sum()\
.reindex(columns=n.buses.index, fill_value=0)
def v_ang_for_(sub):
buses_i = sub.buses_o
if len(buses_i) == 1:
return pd.DataFrame(0, index=sns, columns=buses_i)
sub.calculate_B_H(skip_pre=True)
Z = pd.DataFrame(np.linalg.pinv((sub.B).todense()), buses_i, buses_i)
Z -= Z[sub.slack_bus]
return n.buses_t.p.reindex(columns=buses_i) @ Z
n.buses_t.v_ang = (pd.concat(
[v_ang_for_(sub) for sub in n.sub_networks.obj],
axis=1).reindex(columns=n.buses.index, fill_value=0))
from pypsa.pf import get_switchable_as_dense as get_as_dense, _as_snapshots
import pandas as pd
from numpy import inf
import numpy as np
import os
import logging
logger = logging.getLogger(__name__)
from tempfile import mkstemp
import gc
# for testing
if 'snakemake' not in globals():
os.chdir("/home/ws/bw0928/mnt/lisa/netzbooster")
from vresutils import Dict
import yaml
snakemake = Dict()
snakemake.input = ["networks/prenetwork4.nc"]
snakemake.output = ["networks/postnetwork4.nc"]
with open('config.yaml', encoding='utf8') as f:
snakemake.config = yaml.safe_load(f)
lookup = pd.read_csv('variables.csv', index_col=['component', 'variable'])
# functions ------------------------------------------------------------------
def assign_solution_netzbooster(n,
sns,
variables_sol,
constraints_dual,
keep_references=False,
keep_shadowprices=None):
ax.set_ylabel("Power [GW]")
fig.tight_layout()
fig.savefig("{}{}/maps/series-{}-{}-{}-{}-{}.pdf".format(
snakemake.config['results_dir'], snakemake.config['run'],
snakemake.wildcards["lv"], carrier, start, stop, name),
transparent=True)
# %%
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils import Dict
import yaml
snakemake = Dict()
with open('config.yaml') as f:
snakemake.config = yaml.safe_load(f)
snakemake.config['run'] = "retro_vs_noretro"
snakemake.wildcards = {"lv": "1.0"} # lv1.0, lv1.25, lvopt
name = "elec_s_48_lv{}__Co2L0-3H-T-H-B".format(
snakemake.wildcards["lv"])
suffix = "_retro_tes"
name = name + suffix
snakemake.input = Dict()
snakemake.output = Dict(
map=(snakemake.config['results_dir'] + snakemake.config['run'] +
"/maps/{}".format(name)),
today=(snakemake.config['results_dir'] + snakemake.config['run'] +
"/maps/{}.pdf".format(name)))
snakemake.input.scenario = "lv" + snakemake.wildcards["lv"]
#pd.Series nuts3 code -> 2-letter country codes
mapping = vmapping.countries_to_nuts3()
#Swiss fix
pop["CH040"] = pop["CH04"]
pop["CH070"] = pop["CH07"]
#Separately researched for Montenegro, Albania, Bosnia, Serbia
pop["ME000"] = 650
pop["AL1"] = 2893
pop["BA1"] = 3871
pop["RS1"] = 7210
population = 1e3*pop.groupby(mapping).sum()
population.name = "population"
population.to_csv(snakemake.output.csv_name)
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils import Dict
snakemake = Dict()
snakemake.output = Dict(csv_name="data/population.csv")
build_population()
ax.set_xlabel("")
ax.grid(axis="y")
ax.legend(handles, labels, ncol=4, loc="upper left")
fig.tight_layout()
fig.savefig(snakemake.output.energy, transparent=True)
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils import Dict
import yaml
snakemake = Dict()
with open('../config.yaml') as f:
snakemake.config = yaml.load(f)
snakemake.input = Dict(costs="../" + snakemake.config['results_dir'] +
'version-{version}/costs.csv'.format(
version=snakemake.config['version']))
snakemake.output = Dict(costs="../" + snakemake.config['results_dir'] +
'version-{version}/graphs/costs.pdf'.format(
version=snakemake.config['version']))
plot_costs()
plot_energy()
if 'snakemake' not in globals():
from vresutils import Dict
from snakemake.rules import expand
import yaml
snakemake = Dict()
snakemake.wildcards = Dict( #cost=#'IRP2016-Apr2016',
cost='csir-aggressive',
mask='redz',
sectors='E',
opts='Co2L',
attr='p_nom')
snakemake.input = Dict(
network=
'../results/version-0.5/networks/{cost}_{mask}_{sectors}_{opts}.nc'.
format(**snakemake.wildcards),
supply_regions='../data/supply_regions/supply_regions.shp',
resarea="../data/bundle/REDZ_DEA_Unpublished_Draft_2015")
snakemake.output = (expand(
'../results/plots/network_{cost}_{mask}_{sectors}_{opts}_{attr}.pdf',
**snakemake.wildcards
) + expand(
'../results/plots/network_{cost}_{mask}_{sectors}_{opts}_{attr}_ext.pdf',
**snakemake.wildcards))
snakemake.params = Dict(ext=['png'])
with open('../config.yaml') as f:
snakemake.config = yaml.load(f)
else:
import matplotlib as mpl
mpl.use('Agg')
from add_electricity import add_emission_prices
