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update mocksnakemake for testing

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lisazeyen 2021-09-28 16:36:40 +02:00
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Copyright 2017-2021 The PyPSA-Eur Authors
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

2
README.md
View File

@ -65,6 +65,6 @@ the additional sectors.
# Licence
The code in PyPSA-Eur-Sec is released as free software under the
[GPLv3](http://www.gnu.org/licenses/gpl-3.0.en.html), see LICENSE.txt.
[MIT License](https://opensource.org/licenses/MIT), see `LICENSE.txt`.
However, different licenses and terms of use may apply to the various
input data.

18
Snakefile
View File

@ -188,6 +188,21 @@ rule build_biomass_potentials:
script: 'scripts/build_biomass_potentials.py'
if config["sector"]["biomass_transport"]:
rule build_biomass_transport_costs:
input:
transport_cost_data=HTTP.remote("publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass potentials in europe_web rev.pdf", keep_local=True)
output:
biomass_transport_costs="resources/biomass_transport_costs.csv",
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_biomass_transport_costs"
script: 'scripts/build_biomass_transport_costs.py'
build_biomass_transport_costs_output = rules.build_biomass_transport_costs.output
else:
build_biomass_transport_costs_output = {}
rule build_ammonia_production:
input:
usgs="data/myb1-2017-nitro.xls"
@ -360,7 +375,8 @@ rule prepare_sector_network:
solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc",
**build_retro_cost_output
**build_retro_cost_output,
**build_biomass_transport_costs_output
output: RDIR + '/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
threads: 1
resources: mem_mb=2000

12
config.default.yaml
View File

@ -72,7 +72,7 @@ electricity:
# regulate what components with which carriers are kept from PyPSA-Eur;
# some technologies are removed because they are implemented differently
# (e.g. battery or H2 storage) or have different year-dependent costs
# (e.g. battery or H2 storage) or have different year-dependent costs
# in PyPSA-Eur-Sec
pypsa_eur:
Bus:
@ -223,7 +223,8 @@ sector:
co2_vent: true
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for transport and sequestration of CO2
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
co2_network: false
cc_fraction: 0.9 # default fraction of CO2 captured with post-combustion capture
hydrogen_underground_storage: true
use_fischer_tropsch_waste_heat: true
@ -233,6 +234,7 @@ sector:
electricity_grid_connection: true # only applies to onshore wind and utility PV
gas_distribution_grid: true
gas_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
biomass_transport: false # biomass transport between nodes
conventional_generation: # generator : carrier
OCGT: gas
@ -274,7 +276,6 @@ industry:
hotmaps_locate_missing: false
reference_year: 2015
costs:
lifetime: 25 #default lifetime
# From a Lion Hirth paper, also reflects average of Noothout et al 2016
@ -335,7 +336,7 @@ solving:
plotting:
map:
boundaries: [-11, 30, 34, 71]
boundaries: [-11, 30, 34, 71]
color_geomap:
ocean: white
land: whitesmoke
@ -420,6 +421,7 @@ plotting:
lines: k
transmission lines: k
H2: m
H2 liquefaction: m
hydrogen storage: m
battery: slategray
battery storage: slategray
@ -466,6 +468,7 @@ plotting:
hot water storage: '#BBBBBB'
hot water charging: '#BBBBBB'
hot water discharging: '#999999'
CO2 pipeline: '#999999'
CHP: r
CHP heat: r
CHP electric: r
@ -506,5 +509,6 @@ plotting:
shipping oil: "#6495ED"
shipping oil emissions: "#6495ED"
electricity distribution grid: '#333333'
solid biomass transport: green
H2 for industry: "#222222"
H2 for shipping: "#6495ED"

4
doc/index.rst
View File

@ -134,7 +134,7 @@ it.
Licence
=======
The code in PyPSA-Eur-Sec is released as free software under the `GPLv3
<http://www.gnu.org/licenses/gpl-3.0.en.html>`_, see
The code in PyPSA-Eur-Sec is released as free software under the
`MIT license <https://opensource.org/licenses/MIT>`_, see
`LICENSE <https://github.com/PyPSA/pypsa-eur-sec/blob/master/LICENSE.txt>`_.
However, different licenses and terms of use may apply to the various input data.

10
doc/release_notes.rst
View File

@ -8,6 +8,8 @@ Future release
.. note::
This unreleased version currently requires the master branches of PyPSA, PyPSA-Eur, and the technology-data repository.
* With this release, we change the license from copyleft GPLv3 to the more
liberal MIT license with the consent of all contributors.
* Extended use of ``multiprocessing`` for much better performance
(from up to 20 minutes to less than one minute).
* Compatibility with ``atlite>=0.2``. Older versions of ``atlite`` will no longer work.
@ -60,6 +62,14 @@ Future release
These are included in the environment specifications of PyPSA-Eur.
* Consistent use of ``__main__`` block and further unspecific code cleaning.
* Distinguish costs for home battery storage and inverter from utility-scale battery costs.
* Add option to regionally resolve CO2 storage and add CO2 pipeline transport because geological storage potential,
CO2 utilisation sites and CO2 capture sites may be separated.
The CO2 network is built from zero based on the topology of the electricity grid (greenfield).
Pipelines are assumed to be bidirectional and lossless.
Furthermore, neither retrofitting of natural gas pipelines (required pressures are too high, 80-160 bar vs <80 bar)
nor other modes of CO2 transport (by ship, road or rail) are considered.
The regional representation of CO2 is activated with the config setting ``sector: co2_network: true`` but is deactivated by default.
The global limit for CO2 sequestration now applies to the sum of all CO2 stores via an ``extra_functionality`` constraint.
* Added option for hydrogen liquefaction costs for hydrogen demand in shipping.
This introduces a new ``H2 liquid`` bus at each location.
It is activated via ``sector: shipping_hydrogen_liquefaction: true``.

8
doc/spatial_resolution.rst
View File

@ -44,11 +44,13 @@ Hydrogen network: nodal.
Methane network: single node for Europe, since future demand is so
low and no bottlenecks are expected.
Solid biomass: single node for Europe, until transport costs can be
incorporated.
Solid biomass: choice between single node for Europe and nodal where biomass
potential is regionally disaggregated (currently given per country,
then distributed by population density within)
and transport of solid biomass is possible.
CO2: single node for Europe, but a transport and storage cost is added for
sequestered CO2.
sequestered CO2. Optionally: nodal, with CO2 transport via pipelines.
Liquid hydrocarbons: single node for Europe, since transport costs for
liquids are low.

19
scripts/add_existing_baseyear.py
View File

@ -28,7 +28,7 @@ def add_build_year_to_new_assets(n, baseyear):
# Give assets with lifetimes and no build year the build year baseyear
for c in n.iterate_components(["Link", "Generator", "Store"]):
assets = c.df.index[~c.df.lifetime.isna() & c.df.build_year.isna()]
assets = c.df.index[~c.df.lifetime.isna() & c.df.build_year==0]
c.df.loc[assets, "build_year"] = baseyear
# add -baseyear to name
@ -60,7 +60,7 @@ def add_existing_renewables(df_agg):
}
for tech in ['solar', 'onwind', 'offwind']:
carrier = carriers[tech]
df = pd.read_csv(snakemake.input[f"existing_{tech}"], index_col=0).fillna(0.)
@ -112,9 +112,9 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
Parameters
----------
n : pypsa.Network
grouping_years :
grouping_years :
intervals to group existing capacities
costs :
costs :
to read lifetime to estimate YearDecomissioning
baseyear : int
"""
@ -209,7 +209,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
build_year=grouping_year,
lifetime=costs.at[generator, 'lifetime']
)
else:
n.madd("Link",
@ -268,7 +268,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
df.fillna(0., inplace=True)
# convert GW to MW
df *= 1e3
df *= 1e3
cc = pd.read_csv(snakemake.input.country_codes, index_col=0)
@ -327,7 +327,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
efficiency = cop[heat_pump_type][nodes[name]]
else:
efficiency = costs.at[costs_name, 'efficiency']
for i, grouping_year in enumerate(grouping_years):
if int(grouping_year) + default_lifetime <= int(baseyear):
@ -378,7 +378,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
build_year=int(grouping_year),
lifetime=costs.at[name_type + ' gas boiler', 'lifetime']
)
n.madd("Link",
nodes[name],
suffix=f" {name} oil boiler-{grouping_year}",
@ -410,7 +410,8 @@ if __name__ == "__main__":
simpl='',
clusters=45,
lv=1.0,
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
opts='',
sector_opts='Co2L0-168H-T-H-B-I-solar+p3-dist1',
planning_horizons=2020,
)

90
scripts/build_biomass_transport_costs.py Normal file
View File

@ -0,0 +1,90 @@
"""
Reads biomass transport costs for different countries of the JRC report
"The JRC-EU-TIMES model.
Bioenergy potentials
for EU and neighbouring countries."
(2015)
converts them from units 'EUR per km/ton' -> 'EUR/ (km MWh)'
assuming as an approximation energy content of wood pellets
@author: bw0928
"""
import pandas as pd
import tabula as tbl
ENERGY_CONTENT = 4.8 # unit MWh/t (wood pellets)
def get_countries():
pandas_options = dict(
skiprows=range(6),
header=None,
index_col=0
)
return tbl.read_pdf(
str(snakemake.input.transport_cost_data),
pages="145",
multiple_tables=False,
pandas_options=pandas_options
)[0].index
def get_cost_per_tkm(page, countries):
pandas_options = dict(
skiprows=range(6),
header=0,
sep=' |,',
engine='python',
index_col=False,
)
sc = tbl.read_pdf(
str(snakemake.input.transport_cost_data),
pages=page,
multiple_tables=False,
pandas_options=pandas_options
)[0]
sc.index = countries
sc.columns = sc.columns.str.replace("€", "EUR")
return sc
def build_biomass_transport_costs():
countries = get_countries()
sc1 = get_cost_per_tkm(146, countries)
sc2 = get_cost_per_tkm(147, countries)
# take mean of both supply chains
to_concat = [sc1["EUR/km/ton"], sc2["EUR/km/ton"]]
transport_costs = pd.concat(to_concat, axis=1).mean(axis=1)
# convert tonnes to MWh
transport_costs /= ENERGY_CONTENT
transport_costs.name = "EUR/km/MWh"
# rename country names
to_rename = {
"UK": "GB",
"XK": "KO",
"EL": "GR"
}
transport_costs.rename(to_rename, inplace=True)
# add missing Norway with data from Sweden
transport_costs["NO"] = transport_costs["SE"]
transport_costs.to_csv(snakemake.output[0])
if __name__ == "__main__":
build_biomass_transport_costs()

4
scripts/build_population_layouts.py
View File

@ -90,8 +90,8 @@ if __name__ == '__main__':
for key, pop in pop_cells.items():
ycoords = ('y', cutout.coords['y'])
xcoords = ('x', cutout.coords['x'])
ycoords = ('y', cutout.coords['y'].data)
xcoords = ('x', cutout.coords['x'].data)
values = pop.values.reshape(cutout.shape)
layout = xr.DataArray(values, [ycoords, xcoords])

14
scripts/plot_network.py
View File

@ -289,7 +289,7 @@ def plot_h2_map(network):
title='Electrolyzer capacity',
handler_map=make_handler_map_to_scale_circles_as_in(ax)
)
ax.add_artist(l2)
handles = []
@ -398,7 +398,8 @@ def plot_series(network, carrier="AC", name="test"):
supply = pd.DataFrame(index=n.snapshots)
for c in n.iterate_components(n.branch_components):
for i in range(2):
n_port = 4 if c.name=='Link' else 2
for i in range(n_port):
supply = pd.concat((supply,
(-1) * c.pnl["p" + str(i)].loc[:,
c.df.index[c.df["bus" + str(i)].isin(buses)]].groupby(c.df.carrier,
@ -522,10 +523,11 @@ if __name__ == "__main__":
snakemake = mock_snakemake(
'plot_network',
simpl='',
clusters=48,
lv=1.0,
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
planning_horizons=2050,
clusters=45,
lv=1.5,
opts='',
sector_opts='Co2L0-168H-T-H-B-I-solar+p3-dist1',
planning_horizons=2030,
)
overrides = override_component_attrs(snakemake.input.overrides)

374
scripts/prepare_sector_network.py
View File

@ -19,6 +19,56 @@ from helper import override_component_attrs
import logging
logger = logging.getLogger(__name__)
from types import SimpleNamespace
spatial = SimpleNamespace()
def define_spatial(nodes):
"""
Namespace for spatial
Parameters
----------
nodes : list-like
"""
global spatial
global options
spatial.nodes = nodes
# biomass
spatial.biomass = SimpleNamespace()
if options["biomass_transport"]:
spatial.biomass.nodes = nodes + " solid biomass"
spatial.biomass.locations = nodes
spatial.biomass.industry = nodes + " solid biomass for industry"
spatial.biomass.industry_cc = nodes + " solid biomass for industry CC"
else:
spatial.biomass.nodes = ["EU solid biomass"]
spatial.biomass.locations = ["EU"]
spatial.biomass.industry = ["solid biomass for industry"]
spatial.biomass.industry_cc = ["solid biomass for industry CC"]
spatial.biomass.df = pd.DataFrame(vars(spatial.biomass), index=nodes)
# co2
spatial.co2 = SimpleNamespace()
if options["co2_network"]:
spatial.co2.nodes = nodes + " co2 stored"
spatial.co2.locations = nodes
spatial.co2.vents = nodes + " co2 vent"
else:
spatial.co2.nodes = ["co2 stored"]
spatial.co2.locations = ["EU"]
spatial.co2.vents = ["co2 vent"]
spatial.co2.df = pd.DataFrame(vars(spatial.co2), index=nodes)
def emission_sectors_from_opts(opts):
@ -54,6 +104,40 @@ def get(item, investment_year=None):
return item
def create_network_topology(n, prefix, connector=" -> "):
"""
Create a network topology like the power transmission network.
Parameters
----------
n : pypsa.Network
prefix : str
connector : str
Returns
-------
pd.DataFrame with columns bus0, bus1 and length
"""
ln_attrs = ["bus0", "bus1", "length"]
lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
candidates = pd.concat([
n.lines[ln_attrs],
n.links.loc[n.links.carrier == "DC", lk_attrs]
]).fillna(0)
positive_order = candidates.bus0 < candidates.bus1
candidates_p = candidates[positive_order]
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
candidates_n = candidates[~positive_order].rename(columns=swap_buses)
candidates = pd.concat([candidates_p, candidates_n])
topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
topo.index = topo.apply(lambda c: prefix + c.bus0 + connector + c.bus1, axis=1)
return topo
def co2_emissions_year(countries, opts, year):
"""
Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
@ -75,7 +159,7 @@ def co2_emissions_year(countries, opts, year):
co2_emissions = co2_totals.loc[countries, sectors].sum().sum()
# convert MtCO2 to GtCO2
co2_emissions *= 0.001
co2_emissions *= 0.001
return co2_emissions
@ -102,17 +186,14 @@ def build_carbon_budget(o, fn):
#emissions at the beginning of the path (last year available 2018)
e_0 = co2_emissions_year(countries, opts, year=2018)
#emissions in 2019 and 2020 assumed equal to 2018 and substracted
carbon_budget -= 2 * e_0
planning_horizons = snakemake.config['scenario']['planning_horizons']
t_0 = planning_horizons[0]
if "be" in o:
# final year in the path
t_f = t_0 + (2 * carbon_budget / e_0).round(0)
t_f = t_0 + (2 * carbon_budget / e_0).round(0)
def beta_decay(t):
cdf_term = (t - t_0) / (t_f - t_0)
@ -144,6 +225,53 @@ def add_lifetime_wind_solar(n, costs):
n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, 'lifetime']
def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
"""
Create a network topology like the power transmission network.
Parameters
----------
n : pypsa.Network
prefix : str
connector : str
bidirectional : bool, default True
True: one link for each connection
False: one link for each connection and direction (back and forth)
Returns
-------
pd.DataFrame with columns bus0, bus1 and length
"""
ln_attrs = ["bus0", "bus1", "length"]
lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
candidates = pd.concat([
n.lines[ln_attrs],
n.links.loc[n.links.carrier == "DC", lk_attrs]
]).fillna(0)
positive_order = candidates.bus0 < candidates.bus1
candidates_p = candidates[positive_order]
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
candidates_n = candidates[~positive_order].rename(columns=swap_buses)
candidates = pd.concat([candidates_p, candidates_n])
def make_index(c):
return prefix + c.bus0 + connector + c.bus1
topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
topo.index = topo.apply(make_index, axis=1)
if not bidirectional:
topo_reverse = topo.copy()
topo_reverse.rename(columns=swap_buses, inplace=True)
topo_reverse.index = topo_reverse.apply(make_index, axis=1)
topo = topo.append(topo_reverse)
return topo
# TODO merge issue with PyPSA-Eur
def update_wind_solar_costs(n, costs):
"""
@ -273,6 +401,9 @@ def patch_electricity_network(n):
update_wind_solar_costs(n, costs)
n.loads["carrier"] = "electricity"
n.buses["location"] = n.buses.index
# remove trailing white space of load index until new PyPSA version after v0.18.
n.loads.rename(lambda x: x.strip(), inplace=True)
n.loads_t.p_set.rename(lambda x: x.strip(), axis=1, inplace=True)
def add_co2_tracking(n, options):
@ -299,26 +430,26 @@ def add_co2_tracking(n, options):
)
# this tracks CO2 stored, e.g. underground
n.add("Bus",
"co2 stored",
location="EU",
n.madd("Bus",
spatial.co2.nodes,
location=spatial.co2.locations,
carrier="co2 stored"
)
n.add("Store",
"co2 stored",
n.madd("Store",
spatial.co2.nodes,
e_nom_extendable=True,
e_nom_max=options['co2_sequestration_potential'] * 1e6,
e_nom_max=np.inf,
capital_cost=options['co2_sequestration_cost'],
carrier="co2 stored",
bus="co2 stored"
bus=spatial.co2.nodes
)
if options['co2_vent']:
n.add("Link",
"co2 vent",
bus0="co2 stored",
n.madd("Link",
spatial.co2.vents,
bus0=spatial.co2.nodes,
bus1="co2 atmosphere",
carrier="co2 vent",
efficiency=1.,
@ -326,6 +457,28 @@ def add_co2_tracking(n, options):
)
def add_co2_network(n, costs):
logger.info("Adding CO2 network.")
co2_links = create_network_topology(n, "CO2 pipeline ")
cost_onshore = (1 - co2_links.underwater_fraction) * costs.at['CO2 pipeline', 'fixed'] * co2_links.length
cost_submarine = co2_links.underwater_fraction * costs.at['CO2 submarine pipeline', 'fixed'] * co2_links.length
capital_cost = cost_onshore + cost_submarine
n.madd("Link",
co2_links.index,
bus0=co2_links.bus0.values + " co2 stored",
bus1=co2_links.bus1.values + " co2 stored",
p_min_pu=-1,
p_nom_extendable=True,
length=co2_links.length.values,
capital_cost=capital_cost.values,
carrier="CO2 pipeline",
lifetime=costs.at['CO2 pipeline', 'lifetime']
)
def add_dac(n, costs):
heat_carriers = ["urban central heat", "services urban decentral heat"]
@ -339,7 +492,7 @@ def add_dac(n, costs):
locations,
suffix=" DAC",
bus0="co2 atmosphere",
bus1="co2 stored",
bus1=spatial.co2.df.loc[locations, "nodes"].values,
bus2=locations.values,
bus3=heat_buses,
carrier="DAC",
@ -770,7 +923,8 @@ def insert_electricity_distribution_grid(n, costs):
marginal_cost=n.generators.loc[solar, 'marginal_cost'],
capital_cost=costs.at['solar-rooftop', 'fixed'],
efficiency=n.generators.loc[solar, 'efficiency'],
p_max_pu=n.generators_t.p_max_pu[solar]
p_max_pu=n.generators_t.p_max_pu[solar],
lifetime=costs.at['solar-rooftop', 'lifetime']
)
n.add("Carrier", "home battery")
@ -818,7 +972,7 @@ def insert_gas_distribution_costs(n, costs):
# TODO options?
f_costs = options['gas_distribution_grid_cost_factor']
print("Inserting gas distribution grid with investment cost factor of", f_costs)
capital_cost = costs.loc['electricity distribution grid']["fixed"] * f_costs
@ -827,7 +981,7 @@ def insert_gas_distribution_costs(n, costs):
gas_b = n.links.index[n.links.carrier.str.contains("gas boiler") &
(~n.links.carrier.str.contains("urban central"))]
n.links.loc[gas_b, "capital_cost"] += capital_cost
# micro CHPs
mchp = n.links.index[n.links.carrier.str.contains("micro gas")]
n.links.loc[mchp, "capital_cost"] += capital_cost
@ -989,10 +1143,11 @@ def add_storage(n, costs):
if options['methanation']:
n.madd("Link",
nodes + " Sabatier",
spatial.nodes,
suffix=" Sabatier",
bus0=nodes + " H2",
bus1="EU gas",
bus2="co2 stored",
bus2=spatial.co2.nodes,
p_nom_extendable=True,
carrier="Sabatier",
efficiency=costs.at["methanation", "efficiency"],
@ -1004,10 +1159,11 @@ def add_storage(n, costs):
if options['helmeth']:
n.madd("Link",
nodes + " helmeth",
spatial.nodes,
suffix=" helmeth",
bus0=nodes,
bus1="EU gas",
bus2="co2 stored",
bus2=spatial.co2.nodes,
carrier="helmeth",
p_nom_extendable=True,
efficiency=costs.at["helmeth", "efficiency"],
@ -1020,11 +1176,12 @@ def add_storage(n, costs):
if options['SMR']:
n.madd("Link",
nodes + " SMR CC",
spatial.nodes,
suffix=" SMR CC",
bus0="EU gas",
bus1=nodes + " H2",
bus2="co2 atmosphere",
bus3="co2 stored",
bus3=spatial.co2.nodes,
p_nom_extendable=True,
carrier="SMR CC",
efficiency=costs.at["SMR CC", "efficiency"],
@ -1075,7 +1232,7 @@ def add_land_transport(n, costs):
suffix=" EV battery",
carrier="Li ion"
)
p_set = electric_share * (transport[nodes] + cycling_shift(transport[nodes], 1) + cycling_shift(transport[nodes], 2)) / 3
n.madd("Load",
@ -1086,8 +1243,8 @@ def add_land_transport(n, costs):
p_set=p_set
)
p_nom = nodal_transport_data["number cars"] * options.get("bev_charge_rate", 0.011) * electric_share
p_nom = nodal_transport_data["number cars"] * options.get("bev_charge_rate", 0.011) * electric_share
n.madd("Link",
nodes,
@ -1119,7 +1276,7 @@ def add_land_transport(n, costs):
if electric_share > 0 and options["bev_dsm"]:
e_nom = nodal_transport_data["number cars"] * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share
e_nom = nodal_transport_data["number cars"] * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share
n.madd("Store",
nodes,
@ -1199,7 +1356,7 @@ def add_heat(n, costs):
"services urban decentral",
"urban central"
]
for name in heat_systems:
name_type = "central" if name == "urban central" else "decentral"
@ -1281,16 +1438,16 @@ def add_heat(n, costs):
p_nom_extendable=True
)
if isinstance(options["tes_tau"], dict):
tes_time_constant_days = options["tes_tau"][name_type]
else:
logger.warning("Deprecated: a future version will require you to specify 'tes_tau' ",
"for 'decentral' and 'central' separately.")
tes_time_constant_days = options["tes_tau"] if name_type == "decentral" else 180.
# conversion from EUR/m^3 to EUR/MWh for 40 K diff and 1.17 kWh/m^3/K
capital_cost = costs.at[name_type + ' water tank storage', 'fixed'] / 0.00117 / 40
capital_cost = costs.at[name_type + ' water tank storage', 'fixed'] / 0.00117 / 40
n.madd("Store",
nodes[name] + f" {name} water tanks",
@ -1373,7 +1530,7 @@ def add_heat(n, costs):
bus1=nodes[name],
bus2=nodes[name] + " urban central heat",
bus3="co2 atmosphere",
bus4="co2 stored",
bus4=spatial.co2.df.loc[nodes[name], "nodes"].values,
carrier="urban central gas CHP CC",
p_nom_extendable=True,
capital_cost=costs.at['central gas CHP', 'fixed']*costs.at['central gas CHP', 'efficiency'] + costs.at['biomass CHP capture', 'fixed']*costs.at['gas', 'CO2 intensity'],
@ -1503,9 +1660,9 @@ def create_nodes_for_heat_sector():
# rural are areas with low heating density and individual heating
# urban are areas with high heating density
# urban can be split into district heating (central) and individual heating (decentral)
sectors = ["residential", "services"]
nodes = {}
for sector in sectors:
nodes[sector + " rural"] = pop_layout.index
@ -1516,10 +1673,10 @@ def create_nodes_for_heat_sector():
nodes[sector + " urban decentral"] = pop_layout.index[pop_layout.ct.isin(urban_decentral_ct)]
else:
nodes[sector + " urban decentral"] = pop_layout.index
# for central nodes, residential and services are aggregated
nodes["urban central"] = pop_layout.index.symmetric_difference(nodes["residential urban decentral"])
return nodes
@ -1529,8 +1686,16 @@ def add_biomass(n, costs):
biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)
n.add("Carrier", "biogas")
if options["biomass_transport"]:
# potential per node distributed within country by population
biomass_potentials_spatial = (biomass_potentials.loc[pop_layout.ct]
.set_index(pop_layout.index)
.mul(pop_layout.fraction, axis="index")
.rename(index=lambda x: x + " solid biomass"))
else:
biomass_potentials_spatial = biomass_potentials.sum()
n.add("Carrier", "biogas")
n.add("Carrier", "solid biomass")
n.add("Bus",
@ -1539,9 +1704,9 @@ def add_biomass(n, costs):
carrier="biogas"
)
n.add("Bus",
"EU solid biomass",
location="EU",
n.madd("Bus",
spatial.biomass.nodes,
location=spatial.biomass.locations,
carrier="solid biomass"
)
@ -1554,13 +1719,13 @@ def add_biomass(n, costs):
e_initial=biomass_potentials["biogas"].sum()
)
n.add("Store",
"EU solid biomass",
bus="EU solid biomass",
n.madd("Store",
spatial.biomass.nodes,
bus=spatial.biomass.nodes,
carrier="solid biomass",
e_nom=biomass_potentials["solid biomass"].sum(),
e_nom=biomass_potentials_spatial["solid biomass"],
marginal_cost=costs.at['solid biomass', 'fuel'],
e_initial=biomass_potentials["solid biomass"].sum()
e_initial=biomass_potentials_spatial["solid biomass"]
)
n.add("Link",
@ -1575,6 +1740,32 @@ def add_biomass(n, costs):
p_nom_extendable=True
)
if options["biomass_transport"]:
transport_costs = pd.read_csv(
snakemake.input.biomass_transport_costs,
index_col=0,
squeeze=True
)
# add biomass transport
biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)
# costs
bus0_costs = biomass_transport.bus0.apply(lambda x: transport_costs[x[:2]])
bus1_costs = biomass_transport.bus1.apply(lambda x: transport_costs[x[:2]])
biomass_transport["costs"] = pd.concat([bus0_costs, bus1_costs], axis=1).mean(axis=1)
n.madd("Link",
biomass_transport.index,
bus0=biomass_transport.bus0 + " solid biomass",
bus1=biomass_transport.bus1 + " solid biomass",
p_nom_extendable=True,
length=biomass_transport.length.values,
marginal_cost=biomass_transport.costs * biomass_transport.length.values,
capital_cost=1,
carrier="solid biomass transport"
)
#AC buses with district heating
urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
@ -1585,7 +1776,7 @@ def add_biomass(n, costs):
n.madd("Link",
urban_central + " urban central solid biomass CHP",
bus0="EU solid biomass",
bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
bus1=urban_central,
bus2=urban_central + " urban central heat",
carrier="urban central solid biomass CHP",
@ -1599,11 +1790,11 @@ def add_biomass(n, costs):
n.madd("Link",
urban_central + " urban central solid biomass CHP CC",
bus0="EU solid biomass",
bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
bus1=urban_central,
bus2=urban_central + " urban central heat",
bus3="co2 atmosphere",
bus4="co2 stored",
bus4=spatial.co2.df.loc[urban_central, "nodes"].values,
carrier="urban central solid biomass CHP CC",
p_nom_extendable=True,
capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'] + costs.at['biomass CHP capture', 'fixed'] * costs.at['solid biomass', 'CO2 intensity'],
@ -1625,36 +1816,39 @@ def add_industry(n, costs):
# 1e6 to convert TWh to MWh
industrial_demand = pd.read_csv(snakemake.input.industrial_demand, index_col=0) * 1e6
solid_biomass_by_country = industrial_demand["solid biomass"].groupby(pop_layout.ct).sum()
n.add("Bus",
"solid biomass for industry",
location="EU",
n.madd("Bus",
spatial.biomass.industry,
location=spatial.biomass.locations,
carrier="solid biomass for industry"
)
n.add("Load",
"solid biomass for industry",
bus="solid biomass for industry",
if options["biomass_transport"]:
p_set = industrial_demand.loc[spatial.biomass.locations, "solid biomass"].rename(index=lambda x: x + " solid biomass for industry") / 8760
else:
p_set = industrial_demand["solid biomass"].sum() / 8760
n.madd("Load",
spatial.biomass.industry,
bus=spatial.biomass.industry,
carrier="solid biomass for industry",
p_set=solid_biomass_by_country.sum() / 8760
p_set=p_set
)
n.add("Link",
"solid biomass for industry",
bus0="EU solid biomass",
bus1="solid biomass for industry",
n.madd("Link",
spatial.biomass.industry,
bus0=spatial.biomass.nodes,
bus1=spatial.biomass.industry,
carrier="solid biomass for industry",
p_nom_extendable=True,
efficiency=1.
)
n.add("Link",
"solid biomass for industry CC",
bus0="EU solid biomass",
bus1="solid biomass for industry",
n.madd("Link",
spatial.biomass.industry_cc,
bus0=spatial.biomass.nodes,
bus1=spatial.biomass.industry,
bus2="co2 atmosphere",
bus3="co2 stored",
bus3=spatial.co2.nodes,
carrier="solid biomass for industry CC",
p_nom_extendable=True,
capital_cost=costs.at["cement capture", "fixed"] * costs.at['solid biomass', 'CO2 intensity'],
@ -1687,12 +1881,13 @@ def add_industry(n, costs):
efficiency2=costs.at['gas', 'CO2 intensity']
)
n.add("Link",
"gas for industry CC",
n.madd("Link",
spatial.co2.locations,
suffix=" gas for industry CC",
bus0="EU gas",
bus1="gas for industry",
bus2="co2 atmosphere",
bus3="co2 stored",
bus3=spatial.co2.nodes,
carrier="gas for industry CC",
p_nom_extendable=True,
capital_cost=costs.at["cement capture", "fixed"] * costs.at['gas', 'CO2 intensity'],
@ -1751,9 +1946,9 @@ def add_industry(n, costs):
if shipping_hydrogen_share < 1:
shipping_oil_share = 1 - shipping_hydrogen_share
p_set = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
n.madd("Load",
nodes,
suffix=" shipping oil",
@ -1761,7 +1956,7 @@ def add_industry(n, costs):
carrier="shipping oil",
p_set=p_set
)
co2 = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
n.add("Load",
@ -1780,7 +1975,7 @@ def add_industry(n, costs):
)
if "EU oil Store" not in n.stores.index:
#could correct to e.g. 0.001 EUR/kWh * annuity and O&M
n.add("Store",
"EU oil Store",
@ -1823,7 +2018,7 @@ def add_industry(n, costs):
nodes + " Fischer-Tropsch",
bus0=nodes + " H2",
bus1="EU oil",
bus2="co2 stored",
bus2=spatial.co2.nodes,
carrier="Fischer-Tropsch",
efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
capital_cost=costs.at["Fischer-Tropsch", 'fixed'],
@ -1912,11 +2107,12 @@ def add_industry(n, costs):
)
#assume enough local waste heat for CC
n.add("Link",
"process emissions CC",
n.madd("Link",
spatial.co2.locations,
suffix=" process emissions CC",
bus0="process emissions",
bus1="co2 atmosphere",
bus2="co2 stored",
bus2=spatial.co2.nodes,
carrier="process emissions CC",
p_nom_extendable=True,
capital_cost=costs.at["cement capture", "fixed"],
@ -1947,7 +2143,7 @@ def add_waste_heat(n):
def decentral(n):
"""Removes the electricity transmission system."""
"""Removes the electricity transmission system."""
n.lines.drop(n.lines.index, inplace=True)
n.links.drop(n.links.index[n.links.carrier.isin(["DC", "B2B"])], inplace=True)
@ -1980,7 +2176,7 @@ def maybe_adjust_costs_and_potentials(n, opts):
if attr == 'p_nom_max':
comps = {"Generator", "Link", "StorageUnit"}
elif attr == 'e_nom_max':
comps = {"Store"}
comps = {"Store"}
else:
comps = {"Generator", "Link", "StorageUnit", "Store"}
for c in n.iterate_components(comps):
@ -2006,10 +2202,11 @@ if __name__ == "__main__":
snakemake = mock_snakemake(
'prepare_sector_network',
simpl='',
clusters=48,
clusters="45",
lv=1.0,
opts='',
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
planning_horizons=2020,
planning_horizons=2030,
)
logging.basicConfig(level=snakemake.config['logging_level'])
@ -2034,8 +2231,10 @@ if __name__ == "__main__":
patch_electricity_network(n)
define_spatial(pop_layout.index)
if snakemake.config["foresight"] == 'myopic':
add_lifetime_wind_solar(n, costs)
conventional = snakemake.config['existing_capacities']['conventional_carriers']
@ -2056,6 +2255,8 @@ if __name__ == "__main__":
if o[:4] == "dist":
options['electricity_distribution_grid'] = True
options['electricity_distribution_grid_cost_factor'] = float(o[4:].replace("p", ".").replace("m", "-"))
if o == "biomasstransport":
options["biomass_transport"] = True
nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data = prepare_data(n)
@ -2086,6 +2287,9 @@ if __name__ == "__main__":
if "noH2network" in opts:
remove_h2_network(n)
if options["co2_network"]:
add_co2_network(n, costs)
for o in opts:
m = re.match(r'^\d+h$', o, re.IGNORECASE)
if m is not None:

19
scripts/solve_network.py
View File

@ -3,6 +3,7 @@
import pypsa
import numpy as np
import pandas as pd
from pypsa.linopt import get_var, linexpr, define_constraints
@ -150,8 +151,26 @@ def add_chp_constraints(n):
define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
def add_co2_sequestration_limit(n, sns):
co2_stores = n.stores.loc[n.stores.carrier=='co2 stored'].index
if co2_stores.empty or ('Store', 'e') not in n.variables.index:
return
vars_final_co2_stored = get_var(n, 'Store', 'e').loc[sns[-1], co2_stores]
lhs = linexpr((1, vars_final_co2_stored)).sum()
rhs = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6
name = 'co2_sequestration_limit'
define_constraints(n, lhs, "<=", rhs, 'GlobalConstraint',
'mu', axes=pd.Index([name]), spec=name)
def extra_functionality(n, snapshots):
add_battery_constraints(n)
add_co2_sequestration_limit(n, snapshots)
def solve_network(n, config, opts='', **kwargs):