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/data/Industrial_Database.csv
/data/retro/tabula-calculator-calcsetbuilding.csv
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/data/nuts*
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*.nc

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<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
MIT License
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.

39
Snakefile
View File

@ -1,4 +1,7 @@
from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
HTTP = HTTPRemoteProvider()
configfile: "config.yaml"
@ -20,7 +23,6 @@ subworkflow pypsaeur:
snakefile: "../pypsa-eur/Snakefile"
configfile: "../pypsa-eur/config.yaml"
rule all:
input: SDIR + '/graphs/costs.pdf'
@ -156,6 +158,7 @@ rule build_energy_totals:
co2="data/eea/UNFCCC_v23.csv",
swiss="data/switzerland-sfoe/switzerland-new_format.csv",
idees="data/jrc-idees-2015",
district_heat_share='data/district_heat_share.csv',
eurostat=input_eurostat
output:
energy_name='resources/energy_totals.csv',
@ -169,16 +172,37 @@ rule build_energy_totals:
rule build_biomass_potentials:
input:
jrc_potentials="data/biomass/JRC Biomass Potentials.xlsx"
enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
nuts3_population="../pypsa-eur/data/bundle/nama_10r_3popgdp.tsv.gz",
swiss_cantons="../pypsa-eur/data/bundle/ch_cantons.csv",
swiss_population="../pypsa-eur/data/bundle/je-e-21.03.02.xls",
country_shapes=pypsaeur('resources/country_shapes.geojson')
output:
biomass_potentials_all='resources/biomass_potentials_all.csv',
biomass_potentials='resources/biomass_potentials.csv'
biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',
biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv'
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_biomass_potentials"
benchmark: "benchmarks/build_biomass_potentials_s{simpl}_{clusters}"
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"
@ -322,7 +346,7 @@ rule prepare_sector_network:
transport_name='resources/transport_data.csv',
traffic_data_KFZ = "data/emobility/KFZ__count",
traffic_data_Pkw = "data/emobility/Pkw__count",
biomass_potentials='resources/biomass_potentials.csv',
biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
heat_profile="data/heat_load_profile_BDEW.csv",
costs=CDIR + "costs_{planning_horizons}.csv",
profile_offwind_ac=pypsaeur("resources/profile_offwind-ac.nc"),
@ -351,7 +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

77
config.default.yaml
View File

@ -73,7 +73,7 @@ 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:
@ -100,28 +100,28 @@ energy:
biomass:
year: 2030
scenario: Med
scenario: ENS_Med
classes:
solid biomass:
- Primary agricultural residues
- Forestry energy residue
- Secondary forestry residues
- Secondary Forestry residues sawdust
- Forestry residues from landscape care biomass
- Agricultural waste
- Fuelwood residues
- Secondary Forestry residues - woodchips
- Sawdust
- Residues from landscape care
- Municipal waste
not included:
- Bioethanol sugar beet biomass
- Rapeseeds for biodiesel
- sunflower and soya for Biodiesel
- Starchy crops biomass
- Grassy crops biomass
- Willow biomass
- Poplar biomass potential
- Roundwood fuelwood
- Roundwood Chips & Pellets
- Sugar from sugar beet
- Rape seed
- "Sunflower, soya seed "
- Bioethanol barley, wheat, grain maize, oats, other cereals and rye
- Miscanthus, switchgrass, RCG
- Willow
- Poplar
- FuelwoodRW
- C&P_RW
biogas:
- Manure biomass potential
- Sludge biomass
- Manure solid, liquid
- Sludge
solar_thermal:
@ -142,8 +142,16 @@ existing_capacities:
sector:
central: true
central_fraction: 0.6
district_heating:
potential: 0.6 # maximum fraction of urban demand which can be supplied by district heating
# increase of today's district heating demand to potential maximum district heating share
# progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
progress:
2020: 0.0
2030: 0.3
2040: 0.6
2050: 1.0
district_heating_loss: 0.15
bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM
bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
transport_heating_deadband_upper: 20.
@ -152,7 +160,6 @@ sector:
ICE_upper_degree_factor: 1.6
EV_lower_degree_factor: 0.98
EV_upper_degree_factor: 0.63
district_heating_loss: 0.15
bev_dsm: true #turns on EV battery
bev_availability: 0.5 #How many cars do smart charging
bev_energy: 0.05 #average battery size in MWh
@ -179,7 +186,7 @@ sector:
agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
agriculture_machinery_electric_efficiency: 0.3 # electricity per use
shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
shipping_hydrogen_share: # 1 means all hydrogen FC
2020: 0
2025: 0
@ -227,7 +234,8 @@ 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
@ -237,6 +245,7 @@ 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
@ -274,10 +283,23 @@ industry:
MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
HVC_primary_fraction: 1.0 #fraction of current non-ammonia basic chemicals produced via primary route
HVC_primary_fraction: 1. # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0. # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0. # fraction of today's HVC produced via chemical recycling
HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
MWh_H2_per_tCl: -0.9372 # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
hotmaps_locate_missing: false
reference_year: 2015
# references:
# DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
costs:
lifetime: 25 #default lifetime
@ -339,7 +361,7 @@ solving:
plotting:
map:
boundaries: [-11, 30, 34, 71]
boundaries: [-11, 30, 34, 71]
color_geomap:
ocean: white
land: whitesmoke
@ -424,6 +446,7 @@ plotting:
lines: k
transmission lines: k
H2: m
H2 liquefaction: m
hydrogen storage: m
battery: slategray
battery storage: slategray
@ -470,6 +493,7 @@ plotting:
hot water storage: '#BBBBBB'
hot water charging: '#BBBBBB'
hot water discharging: '#999999'
CO2 pipeline: '#999999'
CHP: r
CHP heat: r
CHP electric: r
@ -510,5 +534,6 @@ plotting:
shipping oil: "#6495ED"
shipping oil emissions: "#6495ED"
electricity distribution grid: '#333333'
solid biomass transport: green
H2 for industry: "#222222"
H2 for shipping: "#6495ED"

34
data/district_heat_share.csv Normal file
View File

@ -0,0 +1,34 @@
country,share to satisfy heat demand (residential) in percent,capacity[MWth]
AT,14,11200
BG,16,6162
BA,8,
HR,6.3,2221
CZ,40,
DK,65,
FI,38,23390
FR,5,
DE,13.8,
HU,7.92875588637399,8549
IS,90,8079000
IE,0.8,
IT,3,8727
LV,73,2254
LT,56,
MK,23.7745607009008,636
NO,4,3400
PL,42,54912
PT,0.070754716981132,34
RS,25,5821
SI,8.86,1739
ES,0.251589260787732,1273
SE,50.4,
UK,2,
BY,70,
EE,52,5406
KO,3,207
RO,23,9962
SK,54,15000
NL,4,9800
CH,4,2792
AL,0,
ME,0,
1 country share to satisfy heat demand (residential) in percent capacity[MWth]
2 AT 14 11200
3 BG 16 6162
4 BA 8
5 HR 6.3 2221
6 CZ 40
7 DK 65
8 FI 38 23390
9 FR 5
10 DE 13.8
11 HU 7.92875588637399 8549
12 IS 90 8079000
13 IE 0.8
14 IT 3 8727
15 LV 73 2254
16 LT 56
17 MK 23.7745607009008 636
18 NO 4 3400
19 PL 42 54912
20 PT 0.070754716981132 34
21 RS 25 5821
22 SI 8.86 1739
23 ES 0.251589260787732 1273
24 SE 50.4
25 UK 2
26 BY 70
27 EE 52 5406
28 KO 3 207
29 RO 23 9962
30 SK 54 15000
31 NL 4 9800
32 CH 4 2792
33 AL 0
34 ME 0

4
doc/data.csv
View File

@ -2,6 +2,7 @@ description,file/folder,licence,source
JRC IDEES database,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees
urban/rural fraction,urban_percent.csv,unknown,unknown
JRC biomass potentials,biomass/,unknown,https://doi.org/10.2790/39014
JRC ENSPRESO biomass potentials,remote,CC BY 4.0,https://data.jrc.ec.europa.eu/dataset/74ed5a04-7d74-4807-9eab-b94774309d9f
EEA emission statistics,eea/UNFCCC_v23.csv,EEA standard re-use policy,https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
Eurostat Energy Balances,eurostat-energy_balances-*/,Eurostat,https://ec.europa.eu/eurostat/web/energy/data/energy-balances
Swiss energy statistics from Swiss Federal Office of Energy,switzerland-sfoe/,unknown,http://www.bfe.admin.ch/themen/00526/00541/00542/02167/index.html?dossier_id=02169
@ -24,3 +25,6 @@ Comparative level investment,comparative_level_investment.csv,Eurostat,https://e
Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
District heating missing countries,district_heat_share.csv,unkown,https://www.euroheat.org/knowledge-hub/country-profiles,,

Can't render this file because it has a wrong number of fields in line 28.

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.

23
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.
@ -60,17 +62,38 @@ 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``.
* The share of shipping transformed into hydrogen fuel cell can be now defined for different years in the ``config.yaml`` file. The carbon emission from the remaining share is treated as a negative load on the atmospheric carbon dioxide bus, just like aviation and land transport emissions.
* The transformation of the Steel and Aluminium production can be now defined for different years in the ``config.yaml`` file.
* Include the option to alter the maximum energy capacity of a store via the ``carrier+factor`` in the ``{sector_opts}`` wildcard. This can be useful for sensitivity analyses. Example: ``co2 stored+e2`` multiplies the ``e_nom_max`` by factor 2. In this example, ``e_nom_max`` represents the CO2 sequestration potential in Europe.
* Add option to regionally disaggregate biomass potential to individual nodes
(currently given per country, then distributed by population density within)
and allow the transport of solid biomass.
The transport costs are determined based on the `JRC-EU-Times Bioenergy report <http://dx.doi.org/10.2790/01017>`_
in the new optional rule ``build_biomass_transport_costs``.
Biomass transport can be activated with the setting ``sector: biomass_transport: true``.
* Use `JRC ENSPRESO database <https://data.jrc.ec.europa.eu/dataset/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ to
spatially disaggregate biomass potentials to PyPSA-Eur regions based on overlaps with NUTS2 regions from ENSPRESO
(proportional to area) (`#151 <https://github.com/PyPSA/pypsa-eur-sec/pull/151>`_).
* Compatibility with ``xarray`` version 0.19.
* Added option to include emissions and energy demands of agriculture, forestry and fishing sector via the letter ``A`` in the ``{sector_opts}`` wildcard.
Demands are separated into electricity, heat and oil for machinery.
Fuel-switching for machinery from oil to electricity can be set exogenously in the ``config.yaml``
`#147 <https://github.com/PyPSA/PyPSA/pull/147>`_.
* Separate basic chemicals into HVC, chlorine, methanol and ammonia [`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
* Add option to specify reuse, primary production, and mechanical and chemical recycling fraction of platics [`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
* Include today's district heating shares in myopic optimisation and add option to specify exogenous path for district heating share increase under ``sector: district_heating:`` [`#149 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/149>`_].
* The myopic option can now be used together with different clustering for the generators and the network. The existing renewable capacities are split evenly among the regions in every country [`#144 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/144>`_].
PyPSA-Eur-Sec 0.5.0 (21st May 2021)
===================================

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.

2
doc/supply_demand.rst
View File

@ -183,7 +183,7 @@ Solid biomass provides process heat up to 500 Celsius in industry, as well as fe
Solid biomass supply
=====================
Only wastes and residues from the JRC biomass dataset.
Only wastes and residues from the JRC ENSPRESO biomass dataset.
Oil product demand

86
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
"""
@ -155,6 +155,11 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
# assign clustered bus
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0, squeeze=True)
busmap = pd.read_csv(snakemake.input.busmap, index_col=0, squeeze=True)
inv_busmap = {}
for k, v in busmap.iteritems():
inv_busmap[v] = inv_busmap.get(v, []) + [k]
clustermaps = busmap_s.map(busmap)
clustermaps.index = clustermaps.index.astype(int)
@ -192,24 +197,54 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
if generator in ['solar', 'onwind', 'offwind']:
rename = {"offwind": "offwind-ac"}
p_max_pu=n.generators_t.p_max_pu[capacity.index + ' ' + rename.get(generator, generator) + '-' + str(baseyear)]
n.madd("Generator",
capacity.index,
suffix=' ' + generator +"-"+ str(grouping_year),
bus=capacity.index,
carrier=generator,
p_nom=capacity,
marginal_cost=costs.at[generator, 'VOM'],
capital_cost=costs.at[generator, 'fixed'],
efficiency=costs.at[generator, 'efficiency'],
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
build_year=grouping_year,
lifetime=costs.at[generator, 'lifetime']
)
suffix = '-ac' if generator == 'offwind' else ''
name_suffix = f' {generator}{suffix}-{baseyear}'
if 'm' in snakemake.wildcards.clusters:
for ind in capacity.index:
# existing capacities are split evenly among regions in every country
inv_ind = [i for i in inv_busmap[ind]]
# for offshore the spliting only inludes coastal regions
inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index]
p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]
p_max_pu.columns=[i + name_suffix for i in inv_ind ]
n.madd("Generator",
[i + name_suffix for i in inv_ind],
bus=ind,
carrier=generator,
p_nom=capacity[ind] / len(inv_ind), # split among regions in a country
marginal_cost=costs.at[generator,'VOM'],
capital_cost=costs.at[generator,'fixed'],
efficiency=costs.at[generator, 'efficiency'],
p_max_pu=p_max_pu,
build_year=grouping_year,
lifetime=costs.at[generator,'lifetime']
)
else:
p_max_pu = n.generators_t.p_max_pu[capacity.index + name_suffix]
n.madd("Generator",
capacity.index,
suffix=' ' + generator +"-"+ str(grouping_year),
bus=capacity.index,
carrier=generator,
p_nom=capacity,
marginal_cost=costs.at[generator, 'VOM'],
capital_cost=costs.at[generator, 'fixed'],
efficiency=costs.at[generator, 'efficiency'],
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
build_year=grouping_year,
lifetime=costs.at[generator, 'lifetime']
)
else:
n.madd("Link",
@ -268,7 +303,7 @@ 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 +362,7 @@ 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 +413,7 @@ 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 +445,8 @@ 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,
)

239
scripts/build_biomass_potentials.py
View File

@ -1,55 +1,194 @@
import pandas as pd
rename = {"UK" : "GB", "BH" : "BA"}
import geopandas as gpd
def build_biomass_potentials():
def build_nuts_population_data(year=2013):
config = snakemake.config['biomass']
year = config["year"]
scenario = config["scenario"]
pop = pd.read_csv(
snakemake.input.nuts3_population,
sep=r'\,| \t|\t',
engine='python',
na_values=[":"],
index_col=1
)[str(year)]
# only countries
pop.drop("EU28", inplace=True)
df = pd.read_excel(snakemake.input.jrc_potentials,
"Potentials (PJ)",
index_col=[0,1])
# mapping from Cantons to NUTS3
cantons = pd.read_csv(snakemake.input.swiss_cantons)
cantons = cantons.set_index(cantons.HASC.str[3:]).NUTS
cantons = cantons.str.pad(5, side='right', fillchar='0')
df.rename(columns={"Unnamed: 18": "Municipal waste"}, inplace=True)
df.drop(columns="Total", inplace=True)
df.replace("-", 0., inplace=True)
# get population by NUTS3
swiss = pd.read_excel(snakemake.input.swiss_population, skiprows=3, index_col=0).loc["Residents in 1000"]
swiss = swiss.rename(cantons).filter(like="CH")
column = df.iloc[:,0]
countries = column.where(column.str.isalpha()).pad()
countries = [rename.get(ct, ct) for ct in countries]
countries_i = pd.Index(countries, name='country')
df.set_index(countries_i, append=True, inplace=True)
# aggregate also to higher order NUTS levels
swiss = [swiss.groupby(swiss.index.str[:i]).sum() for i in range(2, 6)]
df.drop(index='MS', level=0, inplace=True)
# merge Europe + Switzerland
pop = pd.DataFrame(pop.append(swiss), columns=["total"])
# add missing manually
pop["AL"] = 2893
pop["BA"] = 3871
pop["RS"] = 7210
pop["ct"] = pop.index.str[:2]
return pop
# convert from PJ to MWh
df = df / 3.6 * 1e6
df.to_csv(snakemake.output.biomass_potentials_all)
def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
"""
Loads the JRC ENSPRESO biomass potentials.
Parameters
----------
year : int
The year for which potentials are to be taken.
Can be {2010, 2020, 2030, 2040, 2050}.
scenario : str
The scenario. Can be {"ENS_Low", "ENS_Med", "ENS_High"}.
Returns
-------
pd.DataFrame
Biomass potentials for given year and scenario
in TWh/a by commodity and NUTS2 region.
"""
# solid biomass includes:
# Primary agricultural residues (MINBIOAGRW1),
# Forestry energy residue (MINBIOFRSF1),
# Secondary forestry residues (MINBIOWOOW1),
# Secondary Forestry residues – sawdust (MINBIOWOO1a)',
# Forestry residues from landscape care biomass (MINBIOFRSF1a),
# Municipal waste (MINBIOMUN1)',
glossary = pd.read_excel(
str(snakemake.input.enspreso_biomass),
sheet_name="Glossary",
usecols="B:D",
skiprows=1,
index_col=0
)
df = pd.read_excel(
str(snakemake.input.enspreso_biomass),
sheet_name="ENER - NUTS2 BioCom E",
usecols="A:H"
)
# biogas includes:
# Manure biomass potential (MINBIOGAS1),
# Sludge biomass (MINBIOSLU1),
df["group"] = df["E-Comm"].map(glossary.group)
df["commodity"] = df["E-Comm"].map(glossary.description)
df = df.loc[year, scenario, :]
to_rename = {
"NUTS2 Potential available by Bio Commodity": "potential",
"NUST2": "NUTS2",
}
df.rename(columns=to_rename, inplace=True)
# fill up with NUTS0 if NUTS2 is not given
df.NUTS2 = df.apply(lambda x: x.NUTS0 if x.NUTS2 == '-' else x.NUTS2, axis=1)
grouper = {v: k for k, vv in config["classes"].items() for v in vv}
df = df.groupby(grouper, axis=1).sum()
# convert PJ to TWh
df.potential /= 3.6
df.Unit = "TWh/a"
df.index.name = "MWh/a"
dff = df.query("Year == @year and Scenario == @scenario")
df.to_csv(snakemake.output.biomass_potentials)
bio = dff.groupby(["NUTS2", "commodity"]).potential.sum().unstack()
# currently Serbia and Kosovo not split, so aggregate
bio.loc["RS"] += bio.loc["XK"]
bio.drop("XK", inplace=True)
return bio
def disaggregate_nuts0(bio):
"""
Some commodities are only given on NUTS0 level.
These are disaggregated here using the NUTS2
population as distribution key.
Parameters
----------
bio : pd.DataFrame
from enspreso_biomass_potentials()
Returns
-------
pd.DataFrame
"""
pop = build_nuts_population_data()
# get population in nuts2
pop_nuts2 = pop.loc[pop.index.str.len() == 4]
by_country = pop_nuts2.total.groupby(pop_nuts2.ct).sum()
pop_nuts2["fraction"] = pop_nuts2.total / pop_nuts2.ct.map(by_country)
# distribute nuts0 data to nuts2 by population
bio_nodal = bio.loc[pop_nuts2.ct]
bio_nodal.index = pop_nuts2.index
bio_nodal = bio_nodal.mul(pop_nuts2.fraction, axis=0)
# update inplace
bio.update(bio_nodal)
return bio
def build_nuts2_shapes():
"""
- load NUTS2 geometries
- add RS, AL, BA country shapes (not covered in NUTS 2013)
- consistently name ME, MK
"""
nuts2 = gpd.GeoDataFrame(gpd.read_file(snakemake.input.nuts2).set_index('id').geometry)
countries = gpd.read_file(snakemake.input.country_shapes).set_index('name')
missing = countries.loc[["AL", "RS", "BA"]]
nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)
return nuts2.append(missing)
def area(gdf):
"""Returns area of GeoDataFrame geometries in square kilometers."""
return gdf.to_crs(epsg=3035).area.div(1e6)
def convert_nuts2_to_regions(bio_nuts2, regions):
"""
Converts biomass potentials given in NUTS2 to PyPSA-Eur regions based on the
overlay of both GeoDataFrames in proportion to the area.
Parameters
----------
bio_nuts2 : gpd.GeoDataFrame
JRC ENSPRESO biomass potentials indexed by NUTS2 shapes.
regions : gpd.GeoDataFrame
PyPSA-Eur clustered onshore regions
Returns
-------
gpd.GeoDataFrame
"""
# calculate area of nuts2 regions
bio_nuts2["area_nuts2"] = area(bio_nuts2)
overlay = gpd.overlay(regions, bio_nuts2)
# calculate share of nuts2 area inside region
overlay["share"] = area(overlay) / overlay["area_nuts2"]
# multiply all nuts2-level values with share of nuts2 inside region
adjust_cols = overlay.columns.difference({"name", "area_nuts2", "geometry", "share"})
overlay[adjust_cols] = overlay[adjust_cols].multiply(overlay["share"], axis=0)
bio_regions = overlay.groupby("name").sum()
bio_regions.drop(["area_nuts2", "share"], axis=1, inplace=True)
return bio_regions
if __name__ == "__main__":
@ -57,12 +196,28 @@ if __name__ == "__main__":
from helper import mock_snakemake
snakemake = mock_snakemake('build_biomass_potentials')
config = snakemake.config['biomass']
year = config["year"]
scenario = config["scenario"]
# This is a hack, to be replaced once snakemake is unicode-conform
enspreso = enspreso_biomass_potentials(year, scenario)
solid_biomass = snakemake.config['biomass']['classes']['solid biomass']
if 'Secondary Forestry residues sawdust' in solid_biomass:
solid_biomass.remove('Secondary Forestry residues sawdust')
solid_biomass.append('Secondary Forestry residues – sawdust')
enspreso = disaggregate_nuts0(enspreso)
build_biomass_potentials()
nuts2 = build_nuts2_shapes()
df_nuts2 = gpd.GeoDataFrame(nuts2.geometry).join(enspreso)
regions = gpd.read_file(snakemake.input.regions_onshore)
df = convert_nuts2_to_regions(df_nuts2, regions)
df.to_csv(snakemake.output.biomass_potentials_all)
grouper = {v: k for k, vv in config["classes"].items() for v in vv}
df = df.groupby(grouper, axis=1).sum()
df *= 1e6 # TWh/a to MWh/a
df.index.name = "MWh/a"
df.to_csv(snakemake.output.biomass_potentials)

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()

31
scripts/build_energy_totals.py
View File

@ -213,6 +213,12 @@ def idees_per_country(ct, year):
assert df.index[47] == "Electricity"
ct_totals["electricity residential"] = df[47]
assert df.index[46] == "Derived heat"
ct_totals["Derived heat residential"] = df[46]
assert df.index[50] == 'Thermal uses'
ct_totals["thermal uses residential"] = df[50]
# services
df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year]
@ -240,6 +246,13 @@ def idees_per_country(ct, year):
assert df.index[50] == "Electricity"
ct_totals["electricity services"] = df[50]
assert df.index[49] == "Derived heat"
ct_totals["derived heat services"] = df[49]
assert df.index[53] == 'Thermal uses'
ct_totals["thermal uses services"] = df[53]
# agriculture, forestry and fishing
start = "Detailed split of energy consumption (ktoe)"
@ -371,6 +384,7 @@ def build_idees(countries, year):
with mp.Pool(processes=nprocesses) as pool:
totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
totals = pd.concat(totals_list, axis=1)
# convert ktoe to TWh
@ -380,6 +394,13 @@ def build_idees(countries, year):
# convert TWh/100km to kWh/km
totals.loc["passenger car efficiency"] *= 10
# district heating share
district_heat = totals.loc[["derived heat residential",
"derived heat services"]].sum()
total_heat = totals.loc[["thermal uses residential",
"thermal uses services"]].sum()
totals.loc["district heat share"] = district_heat.div(total_heat)
return totals.T
@ -522,7 +543,7 @@ def build_energy_totals(countries, eurostat, swiss, idees):
for purpose in ["passenger", "freight"]:
attrs = [f"total domestic aviation {purpose}", f"total international aviation {purpose}"]
df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(axis=1)
df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(axis=1)
if "BA" in df.index:
# fill missing data for BA (services and road energy data)
@ -531,6 +552,14 @@ def build_energy_totals(countries, eurostat, swiss, idees):
ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
df.loc['BA', missing] = ratio * df.loc["RS", missing]
# Missing district heating share
dh_share = pd.read_csv(snakemake.input.district_heat_share,
index_col=0, usecols=[0, 1])
# make conservative assumption and take minimum from both data sets
df["district heat share"] = (pd.concat([df["district heat share"],
dh_share.reindex(index=df.index)/100],
axis=1).min(axis=1))
return df

6
scripts/build_industrial_energy_demand_per_country_today.py
View File

@ -103,6 +103,7 @@ def add_ammonia_energy_demand(demand):
demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - demand["Ammonia"]
demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True)
demand.drop(columns='Basic chemicals', inplace=True)
return demand
@ -114,6 +115,11 @@ def add_non_eu28_industrial_energy_demand(demand):
fn = snakemake.input.industrial_production_per_country
production = pd.read_csv(fn, index_col=0) / 1e3
#recombine HVC, Chlorine and Methanol to Basic chemicals (without ammonia)
chemicals = ["HVC", "Chlorine", "Methanol"]
production["Basic chemicals (without ammonia)"] = production[chemicals].sum(axis=1)
production.drop(columns=chemicals, inplace=True)
eu28_production = production.loc[eu28].sum()
eu28_energy = demand.groupby(level=1).sum()
eu28_averages = eu28_energy / eu28_production

18
scripts/build_industrial_production_per_country.py
View File

@ -179,8 +179,8 @@ def industry_production(countries):
return demand
def add_ammonia_demand_separately(demand):
"""Include ammonia demand separately and remove ammonia from basic chemicals."""
def separate_basic_chemicals(demand):
"""Separate basic chemicals into ammonia, chlorine, methanol and HVC."""
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
@ -189,7 +189,7 @@ def add_ammonia_demand_separately(demand):
print("Following countries have no ammonia demand:", missing)
demand.insert(2, "Ammonia", 0.)
demand["Ammonia"] = 0.
demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
@ -198,9 +198,13 @@ def add_ammonia_demand_separately(demand):
# EE, HR and LT got negative demand through subtraction - poor data
demand['Basic chemicals'].clip(lower=0., inplace=True)
to_rename = {"Basic chemicals": "Basic chemicals (without ammonia)"}
demand.rename(columns=to_rename, inplace=True)
# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
demand["HVC"] = config["HVC_production_today"] * 1e3 * distribution_key
demand["Chlorine"] = config["chlorine_production_today"] * 1e3 * distribution_key
demand["Methanol"] = config["methanol_production_today"] * 1e3 * distribution_key
demand.drop(columns=["Basic chemicals"], inplace=True)
if __name__ == '__main__':
if 'snakemake' not in globals():
@ -211,12 +215,14 @@ if __name__ == '__main__':
year = snakemake.config['industry']['reference_year']
config = snakemake.config["industry"]
jrc_dir = snakemake.input.jrc
eurostat_dir = snakemake.input.eurostat
demand = industry_production(countries)
add_ammonia_demand_separately(demand)
separate_basic_chemicals(demand)
fn = snakemake.output.industrial_production_per_country
demand.to_csv(fn, float_format='%.2f')

7
scripts/build_industrial_production_per_country_tomorrow.py
View File

@ -39,11 +39,14 @@ if __name__ == '__main__':
al_primary_fraction = get(config["Al_primary_fraction"], investment_year)
fraction_persistent_primary = al_primary_fraction * total_aluminium.sum() / production[key_pri].sum()
production[key_pri] = fraction_persistent_primary * production[key_pri]
production[key_sec] = total_aluminium - production[key_pri]
production["Basic chemicals (without ammonia)"] *= config['HVC_primary_fraction']
production["HVC (mechanical recycling)"] = get(config["HVC_mechanical_recycling_fraction"], investment_year) * production["HVC"]
production["HVC (chemical recycling)"] = get(config["HVC_chemical_recycling_fraction"], investment_year) * production["HVC"]
production["HVC"] *= get(config['HVC_primary_fraction'], investment_year)
fn = snakemake.output.industrial_production_per_country_tomorrow
production.to_csv(fn, float_format='%.2f')

10
scripts/build_industrial_production_per_node.py
View File

@ -9,7 +9,11 @@ sector_mapping = {
'Integrated steelworks': 'Iron and steel',
'DRI + Electric arc': 'Iron and steel',
'Ammonia': 'Chemical industry',
'Basic chemicals (without ammonia)': 'Chemical industry',
'HVC': 'Chemical industry',
'HVC (mechanical recycling)': 'Chemical industry',
'HVC (chemical recycling)': 'Chemical industry',
'Methanol': 'Chemical industry',
'Chlorine': 'Chemical industry',
'Other chemicals': 'Chemical industry',
'Pharmaceutical products etc.': 'Chemical industry',
'Cement': 'Cement',
@ -40,12 +44,12 @@ def build_nodal_industrial_production():
countries = keys.country.unique()
sectors = industrial_production.columns
for country, sector in product(countries, sectors):
buses = keys.index[keys.country == country]
mapping = sector_mapping.get(sector, "population")
key = keys.loc[buses, mapping]
nodal_production.loc[buses, sector] = industrial_production.at[country, sector] * key

64
scripts/build_industry_sector_ratios.py
View File

@ -279,7 +279,7 @@ def chemicals_industry():
df = pd.DataFrame(index=index)
# Basid chemicals
# Basic chemicals
sector = "Basic chemicals"
@ -374,52 +374,82 @@ def chemicals_industry():
# putting in ammonia demand for H2 and electricity separately
s_emi = idees["emi"][3:57]
s_out = idees["out"][8:9]
assert s_emi.index[0] == sector
assert sector in str(s_out.index)
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
# ktNH3/a
ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
s_out -= ammonia_total
# convert from MtHVC/a to ktHVC/a
s_out = config["HVC_production_today"] * 1e3
# tCO2/t material
df.loc["process emission", sector] += (
s_emi["Process emissions"]
- config["petrochemical_process_emissions"] * 1e3
- config["NH3_process_emissions"] * 1e3
) / s_out.values
) / s_out
# emissions originating from feedstock, could be non-fossil origin
# tCO2/t material
df.loc["process emission from feedstock", sector] += (
config["petrochemical_process_emissions"] * 1e3
) / s_out.values
) / s_out
# convert from ktoe/a to GWh/a
sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
df.loc[sources, sector] *= toe_to_MWh
# subtract ammonia energy demand (in ktNH3/a)
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
df.loc["methane", sector] -= ammonia_total * config["MWh_CH4_per_tNH3_SMR"]
df.loc["elec", sector] -= ammonia_total * config["MWh_elec_per_tNH3_SMR"]
# MWh/t material
df.loc[sources, sector] = df.loc[sources, sector] / s_out.values
# subtract chlorine demand
chlorine_total = config["chlorine_production_today"]
df.loc["hydrogen", sector] -= chlorine_total * config["MWh_H2_per_tCl"]
df.loc["elec", sector] -= chlorine_total * config["MWh_elec_per_tCl"]
to_rename = {sector: f"{sector} (without ammonia)"}
df.rename(columns=to_rename, inplace=True)
# subtract methanol demand
methanol_total = config["methanol_production_today"]
df.loc["methane", sector] -= methanol_total * config["MWh_CH4_per_tMeOH"]
df.loc["elec", sector] -= methanol_total * config["MWh_elec_per_tMeOH"]
# MWh/t material
df.loc[sources, sector] = df.loc[sources, sector] / s_out
df.rename(columns={sector: "HVC"}, inplace=True)
# HVC mechanical recycling
sector = "HVC (mechanical recycling)"
df[sector] = 0.0
df.loc["elec", sector] = config["MWh_elec_per_tHVC_mechanical_recycling"]
# HVC chemical recycling
sector = "HVC (chemical recycling)"
df[sector] = 0.0
df.loc["elec", sector] = config["MWh_elec_per_tHVC_chemical_recycling"]
# Ammonia
sector = "Ammonia"
df[sector] = 0.0
df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
# Chlorine
sector = "Chlorine"
df[sector] = 0.0
df.loc["hydrogen", sector] = config["MWh_H2_per_tCl"]
df.loc["elec", sector] = config["MWh_elec_per_tCl"]
# Methanol
sector = "Methanol"
df[sector] = 0.0
df.loc["methane", sector] = config["MWh_CH4_per_tMeOH"]
df.loc["elec", sector] = config["MWh_elec_per_tMeOH"]
# Other chemicals
sector = "Other chemicals"

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)

444
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):
@ -58,6 +108,40 @@ 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).
@ -79,7 +163,7 @@ 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
@ -106,17 +190,14 @@ 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)
@ -148,6 +229,53 @@ 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):
"""
@ -277,6 +405,9 @@ 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):
@ -303,26 +434,26 @@ 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.,
@ -330,6 +461,28 @@ 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"]
@ -340,10 +493,9 @@ def add_dac(n, costs):
efficiency3 = -(costs.at['direct air capture', 'heat-input'] - costs.at['direct air capture', 'compression-heat-output'])
n.madd("Link",
locations,
suffix=" DAC",
heat_buses.str.replace(" heat", " DAC"),
bus0="co2 atmosphere",
bus1="co2 stored",
bus1=spatial.co2.df.loc[locations, "nodes"].values,
bus2=locations.values,
bus3=heat_buses,
carrier="DAC",
@ -487,6 +639,8 @@ def prepare_data(n):
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
nodal_energy_totals.index = pop_layout.index
# district heat share not weighted by population
district_heat_share = nodal_energy_totals["district heat share"].round(2)
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
@ -609,7 +763,7 @@ def prepare_data(n):
)
return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data
return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share
# TODO checkout PyPSA-Eur script
@ -775,7 +929,8 @@ 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")
@ -823,7 +978,7 @@ 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
@ -832,7 +987,7 @@ 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
@ -994,10 +1149,11 @@ 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"],
@ -1009,10 +1165,11 @@ 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"],
@ -1025,11 +1182,12 @@ 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"],
@ -1080,7 +1238,7 @@ 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",
@ -1091,8 +1249,8 @@ 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,
@ -1124,7 +1282,7 @@ 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,
@ -1184,12 +1342,11 @@ def add_heat(n, costs):
sectors = ["residential", "services"]
nodes = create_nodes_for_heat_sector()
nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector()
#NB: must add costs of central heating afterwards (EUR 400 / kWpeak, 50a, 1% FOM from Fraunhofer ISE)
urban_fraction = options['central_fraction'] * pop_layout["urban"] / pop_layout[["urban", "rural"]].sum(axis=1)
# exogenously reduce space heat demand
if options["reduce_space_heat_exogenously"]:
dE = get(options["reduce_space_heat_exogenously_factor"], investment_year)
@ -1204,7 +1361,7 @@ def add_heat(n, costs):
"services urban decentral",
"urban central"
]
for name in heat_systems:
name_type = "central" if name == "urban central" else "decentral"
@ -1220,15 +1377,22 @@ def add_heat(n, costs):
## Add heat load
for sector in sectors:
# heat demand weighting
if "rural" in name:
factor = 1 - urban_fraction[nodes[name]]
elif "urban" in name:
factor = urban_fraction[nodes[name]]
elif "urban central" in name:
factor = dist_fraction[nodes[name]]
elif "urban decentral" in name:
factor = urban_fraction[nodes[name]] - \
dist_fraction[nodes[name]]
else:
raise NotImplementedError(f" {name} not in " f"heat systems: {heat_systems}")
if sector in name:
heat_load = heat_demand[[sector + " water",sector + " space"]].groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor)
if name == "urban central":
heat_load = heat_demand.groupby(level=1,axis=1).sum()[nodes[name]].multiply(urban_fraction[nodes[name]] * (1 + options['district_heating_loss']))
heat_load = heat_demand.groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor * (1 + options['district_heating']['district_heating_loss']))
n.madd("Load",
nodes[name],
@ -1286,16 +1450,16 @@ 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",
@ -1378,7 +1542,7 @@ 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'],
@ -1508,37 +1672,54 @@ 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)
ct_urban = pop_layout.urban.groupby(pop_layout.ct).sum()
# distribution of urban population within a country
pop_layout["urban_ct_fraction"] = pop_layout.urban / pop_layout.ct.map(ct_urban.get)
sectors = ["residential", "services"]
nodes = {}
urban_fraction = pop_layout.urban / pop_layout[["rural", "urban"]].sum(axis=1)
for sector in sectors:
nodes[sector + " rural"] = pop_layout.index
nodes[sector + " urban decentral"] = pop_layout.index
if options["central"]:
# TODO: this looks hardcoded, move to config
urban_decentral_ct = pd.Index(["ES", "GR", "PT", "IT", "BG"])
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
# maximum potential of urban demand covered by district heating
central_fraction = options['district_heating']["potential"]
# district heating share at each node
dist_fraction_node = district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"]
nodes["urban central"] = dist_fraction_node.index
# if district heating share larger than urban fraction -> set urban
# fraction to district heating share
urban_fraction = pd.concat([urban_fraction, dist_fraction_node],
axis=1).max(axis=1)
# difference of max potential and today's share of district heating
diff = (urban_fraction * central_fraction) - dist_fraction_node
progress = get(options["district_heating"]["potential"], investment_year)
dist_fraction_node += diff * progress
print(
"The current district heating share compared to the maximum",
f"possible is increased by a progress factor of\n{progress}",
f"resulting in a district heating share of\n{dist_fraction_node}"
)
return nodes, dist_fraction_node, urban_fraction
def add_biomass(n, costs):
print("adding biomass")
# biomass distributed at country level - i.e. transport within country allowed
countries = n.buses.country.dropna().unique()
biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)
n.add("Carrier", "biogas")
if options["biomass_transport"]:
biomass_potentials_spatial = biomass_potentials.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",
@ -1547,9 +1728,9 @@ 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"
)
@ -1557,18 +1738,18 @@ def add_biomass(n, costs):
"EU biogas",
bus="EU biogas",
carrier="biogas",
e_nom=biomass_potentials.loc[countries, "biogas"].sum(),
e_nom=biomass_potentials["biogas"].sum(),
marginal_cost=costs.at['biogas', 'fuel'],
e_initial=biomass_potentials.loc[countries, "biogas"].sum()
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.loc[countries, "solid biomass"].sum(),
e_nom=biomass_potentials_spatial["solid biomass"],
marginal_cost=costs.at['solid biomass', 'fuel'],
e_initial=biomass_potentials.loc[countries, "solid biomass"].sum()
e_initial=biomass_potentials_spatial["solid biomass"]
)
n.add("Link",
@ -1583,6 +1764,32 @@ 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"]
@ -1593,7 +1800,7 @@ 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",
@ -1607,11 +1814,11 @@ 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'],
@ -1633,36 +1840,39 @@ 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'],
@ -1695,12 +1905,13 @@ 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'],
@ -1759,9 +1970,9 @@ 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",
@ -1769,7 +1980,7 @@ 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",
@ -1788,7 +1999,7 @@ 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",
@ -1810,7 +2021,7 @@ def add_industry(n, costs):
if options["oil_boilers"]:
nodes_heat = create_nodes_for_heat_sector()
nodes_heat = create_nodes_for_heat_sector()[0]
for name in ["residential rural", "services rural", "residential urban decentral", "services urban decentral"]:
@ -1831,7 +2042,7 @@ 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'],
@ -1920,11 +2131,12 @@ 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"],
@ -2020,7 +2232,7 @@ def add_agriculture(n, costs):
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)
@ -2053,7 +2265,7 @@ 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):
@ -2072,17 +2284,18 @@ def limit_individual_line_extension(n, maxext):
hvdc = n.links.index[n.links.carrier == 'DC']
n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext
#%%
if __name__ == "__main__":
if 'snakemake' not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'prepare_sector_network',
simpl='',
clusters=48,
opts="",
clusters="37",
lv=1.0,
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
planning_horizons=2020,
planning_horizons="2020",
)
logging.basicConfig(level=snakemake.config['logging_level'])
@ -2107,8 +2320,10 @@ 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']
@ -2129,11 +2344,13 @@ 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)
nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share = prepare_data(n)
if "nodistrict" in opts:
options["central"] = False
options["district_heating"]["progress"] = 0.0
if "T" in opts:
add_land_transport(n, costs)
@ -2162,6 +2379,9 @@ 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:

64
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
@ -19,12 +20,47 @@ pypsa.pf.logger.setLevel(logging.WARNING)
def add_land_use_constraint(n):
#warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
for carrier in ['solar', 'onwind', 'offwind-ac', 'offwind-dc']:
existing = n.generators.loc[n.generators.carrier == carrier, "p_nom"].groupby(n.generators.bus.map(n.buses.location)).sum()
existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
n.generators.loc[existing.index, "p_nom_max"] -= existing
if 'm' in snakemake.wildcards.clusters:
_add_land_use_constraint_m(n)
else:
_add_land_use_constraint(n)
def _add_land_use_constraint(n):
#warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
for carrier in ['solar', 'onwind', 'offwind-ac', 'offwind-dc']:
existing = n.generators.loc[n.generators.carrier==carrier,"p_nom"].groupby(n.generators.bus.map(n.buses.location)).sum()
existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
n.generators.loc[existing.index,"p_nom_max"] -= existing
n.generators.p_nom_max.clip(lower=0, inplace=True)
def _add_land_use_constraint_m(n):
# if generators clustering is lower than network clustering, land_use accounting is at generators clusters
planning_horizons = snakemake.config["scenario"]["planning_horizons"]
grouping_years = snakemake.config["existing_capacities"]["grouping_years"]
current_horizon = snakemake.wildcards.planning_horizons
for carrier in ['solar', 'onwind', 'offwind-ac', 'offwind-dc']:
existing = n.generators.loc[n.generators.carrier==carrier,"p_nom"]
ind = list(set([i.split(sep=" ")[0] + ' ' + i.split(sep=" ")[1] for i in existing.index]))
previous_years = [
str(y) for y in
planning_horizons + grouping_years
if y < int(snakemake.wildcards.planning_horizons)
]
for p_year in previous_years:
ind2 = [i for i in ind if i + " " + carrier + "-" + p_year in existing.index]
sel_current = [i + " " + carrier + "-" + current_horizon for i in ind2]
sel_p_year = [i + " " + carrier + "-" + p_year for i in ind2]
n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[sel_p_year].rename(lambda x: x[:-4] + current_horizon)
n.generators.p_nom_max.clip(lower=0, inplace=True)
@ -150,8 +186,26 @@ 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):