diff --git a/.gitignore b/.gitignore
index ac252a84..10243def 100644
--- a/.gitignore
+++ b/.gitignore
@@ -28,7 +28,8 @@ gurobi.log
/data/.nfs*
/data/Industrial_Database.csv
/data/retro/tabula-calculator-calcsetbuilding.csv
-/data
+/data/nuts*
+
*.org
*.nc
diff --git a/LICENSE.txt b/LICENSE.txt
index 9cecc1d4..dc10fd32 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,674 +1,20 @@
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-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
- 17. Interpretation of Sections 15 and 16.
-
- If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
- END OF TERMS AND CONDITIONS
-
- How to Apply These Terms to Your New Programs
-
- If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
- To do so, attach the following notices to the program. It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
- {one line to give the program's name and a brief idea of what it does.}
- Copyright (C) {year} {name of author}
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see .
-
-Also add information on how to contact you by electronic and paper mail.
-
- If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
- {project} Copyright (C) {year} {fullname}
- This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License. Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
- You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-.
-
- 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
-.
+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.
\ No newline at end of file
diff --git a/README.md b/README.md
index 31aaa0ef..f4cc34cc 100644
--- a/README.md
+++ b/README.md
@@ -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.
diff --git a/Snakefile b/Snakefile
index db53f7c8..0b72ae4b 100644
--- a/Snakefile
+++ b/Snakefile
@@ -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
diff --git a/config.default.yaml b/config.default.yaml
index 05a4a751..fc53e4a0 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -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"
diff --git a/data/district_heat_share.csv b/data/district_heat_share.csv
new file mode 100644
index 00000000..5afd65c8
--- /dev/null
+++ b/data/district_heat_share.csv
@@ -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,
diff --git a/doc/data.csv b/doc/data.csv
index 8e316281..01fa04b3 100644
--- a/doc/data.csv
+++ b/doc/data.csv
@@ -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,,
+
diff --git a/doc/index.rst b/doc/index.rst
index 1bf307f5..0c6f929d 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -134,7 +134,7 @@ it.
Licence
=======
-The code in PyPSA-Eur-Sec is released as free software under the `GPLv3
-`_, see
+The code in PyPSA-Eur-Sec is released as free software under the
+`MIT license `_, see
`LICENSE `_.
However, different licenses and terms of use may apply to the various input data.
diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index 0c65c8c2..8a1ed562 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -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 `_
+ 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 `_ to
+ spatially disaggregate biomass potentials to PyPSA-Eur regions based on overlaps with NUTS2 regions from ENSPRESO
+ (proportional to area) (`#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 `_.
+* Separate basic chemicals into HVC, chlorine, methanol and ammonia [`#166 `_].
+* Add option to specify reuse, primary production, and mechanical and chemical recycling fraction of platics [`#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 `_].
+* 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 `_].
PyPSA-Eur-Sec 0.5.0 (21st May 2021)
===================================
diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 1be9f3ad..83a33f73 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -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.
diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 31563c43..3ab00d8e 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -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
diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 47c31c0e..bb35e378 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -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,
)
diff --git a/scripts/build_biomass_potentials.py b/scripts/build_biomass_potentials.py
index f02c9093..68d87808 100644
--- a/scripts/build_biomass_potentials.py
+++ b/scripts/build_biomass_potentials.py
@@ -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)
diff --git a/scripts/build_biomass_transport_costs.py b/scripts/build_biomass_transport_costs.py
new file mode 100644
index 00000000..aaec215b
--- /dev/null
+++ b/scripts/build_biomass_transport_costs.py
@@ -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()
diff --git a/scripts/build_energy_totals.py b/scripts/build_energy_totals.py
index 4b4dc2bc..b21200b5 100644
--- a/scripts/build_energy_totals.py
+++ b/scripts/build_energy_totals.py
@@ -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
diff --git a/scripts/build_industrial_energy_demand_per_country_today.py b/scripts/build_industrial_energy_demand_per_country_today.py
index 1d906b24..0adf84e7 100644
--- a/scripts/build_industrial_energy_demand_per_country_today.py
+++ b/scripts/build_industrial_energy_demand_per_country_today.py
@@ -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
diff --git a/scripts/build_industrial_production_per_country.py b/scripts/build_industrial_production_per_country.py
index 1754752a..eadfb224 100644
--- a/scripts/build_industrial_production_per_country.py
+++ b/scripts/build_industrial_production_per_country.py
@@ -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')
diff --git a/scripts/build_industrial_production_per_country_tomorrow.py b/scripts/build_industrial_production_per_country_tomorrow.py
index ba69e0a6..ccf31839 100644
--- a/scripts/build_industrial_production_per_country_tomorrow.py
+++ b/scripts/build_industrial_production_per_country_tomorrow.py
@@ -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')
diff --git a/scripts/build_industrial_production_per_node.py b/scripts/build_industrial_production_per_node.py
index b5361e6b..4ceffee9 100644
--- a/scripts/build_industrial_production_per_node.py
+++ b/scripts/build_industrial_production_per_node.py
@@ -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
diff --git a/scripts/build_industry_sector_ratios.py b/scripts/build_industry_sector_ratios.py
index adfb1d3c..49c82138 100644
--- a/scripts/build_industry_sector_ratios.py
+++ b/scripts/build_industry_sector_ratios.py
@@ -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"
diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index cd74d3ea..5182123b 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -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)
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index c256c251..645dc9e1 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -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:
diff --git a/scripts/solve_network.py b/scripts/solve_network.py
index a46acc30..eac1f581 100644
--- a/scripts/solve_network.py
+++ b/scripts/solve_network.py
@@ -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):