* Omitting unnecessary constraints on the CHP units
All CHPs in pypsa-eur-sec are operating on the backpressure line and this set of extra constraints to ensure working in the operational space of the CHPs is hence not necessary at the moment.
* remove chp constraints function call
* ammonia_production: minor cleaning and move into __main__ (#106)
* biomass_potentials: code cleaning and automatic country index inferral (#107)
* Revision: build energy totals (#111)
* blacken
* energy_totals: preliminaries
* energy_totals: update build_swiss
* energy_totals: update build_eurostat
* energy_totals: update build_idees
* energy_totals: update build_energy_totals
* energy_totals: update build_eea_co2
* energy_totals: update build_eurostat_co2
* energy_totals: update build_co2_totals
* energy_totals: update build_transport_data
* energy_totals: add tqdm progressbar to idees
* energy_totals: adjust __main__ section
* energy_totals: handle inputs via Snakefile and config
* energy_totals: handle data and emissions year via config
* energy_totals: fix reading in eurostat for different years
* energy_totals: fix erroneous drop duplicates
This caused problems for waste management in HU and SI
* energy_totals: make scope selection of CO2 or GHG a config option
* Revision: build industrial production per country (#114)
* industry-ppc: format
* industry-ppc: rewrite for performance
* industry-ppc: move reference year to config
* industry-ppct: tidy up and format (#115)
* remove stale industry demand rules (#116)
* industry-epc: rewrite for performance (#117)
* Revision: industrial distribution key (#118)
* industry-distribution: first tidying
* industry-distribution: first tidying
* industry-distribution: fix syntax
* Revision: industrial energy demand per node today (#119)
* industry-epn: minor code cleaning
* industry-epn: remove accidental artifact
* industry-epn: remove accidental artifact II
* industry-ppn: code cleaning (#120)
* minor code cleaning (#121)
* Revision: industry sector ratios (#122)
* sector-ratios: basic reformatting
* sector-ratios: add new read_excel function that filters year already
* sector-ratios: rename jrc to idees
* sector-ratios: rename conv_factor to toe_to_MWh
* sector-ratios: modularise into functions
* Move overriding of component attributes to function and into data (#123)
* move overriding of component attributes to central function and store in separate folder
* fix return of helper.override_component_attrs
* prepare: fix accidental syntax error
* override_component_attrs: bugfix that aligns with pypsa components
* Revision: build population layout (#108)
* population_layouts: move inside __main__ and blacken
* population_layouts: misc code cleaning and multiprocessing
* population_layouts: fix fill_values assignment of urban fractions
* population_layouts: bugfig for UK-GB naming ambiguity
* population_layouts: sort countries alphabetically for better overview
* config: change path to atlite cutout
* Revision: build clustered population layouts (#112)
* population_layouts: move inside __main__ and blacken
* population_layouts: misc code cleaning and multiprocessing
* population_layouts: fix fill_values assignment of urban fractions
* population_layouts: bugfig for UK-GB naming ambiguity
* population_layouts: sort countries alphabetically for better overview
* cl_pop_layout: blacken
* cl_pop_layout: turn GeoDataFrame into GeoSeries + code cleaning
* cl_pop_layout: add fraction column which is repeatedly calculated downstream
* Revision: build various heating-related time series (#113)
* population_layouts: move inside __main__ and blacken
* population_layouts: misc code cleaning and multiprocessing
* population_layouts: fix fill_values assignment of urban fractions
* population_layouts: bugfig for UK-GB naming ambiguity
* population_layouts: sort countries alphabetically for better overview
* cl_pop_layout: blacken
* cl_pop_layout: turn GeoDataFrame into GeoSeries + code cleaning
* gitignore: add .vscode
* heating_profiles: update to new atlite and move into __main__
* heating_profiles: remove extra cutout
* heating_profiles: load regions with .buffer(0) and remove clean_invalid_geometries
* heating_profiles: load regions with .buffer(0) before squeeze()
* heating_profiles: account for transpose of dataarray
* heating_profiles: account for transpose of dataarray in add_exiting_baseyear
* Reduce verbosity of Snakefile (2) (#128)
* tidy Snakefile light
* Snakefile: fix indents
* Snakefile: add missing RDIR
* tidy config by removing quotes and expanding lists (#109)
* bugfix: reorder squeeze() and buffer()
* plot/summary: cosmetic changes including: (#131)
- matplotlibrc for default style and backend
- remove unused config options
- option to configure geomap colors
- option to configure geomap bounds
* solve: align with pypsa-eur using ilopf (#129)
* tidy myopic code scripts (#132)
* use mock_snakemake from pypsa-eur (#133)
* Snakefile: add benchmark files to each rule
* Snakefile: only run build_retro_cost if endogenously optimised
* Snakefile: remove old {network} wildcard constraints
* WIP: Revision: prepare_sector_network (#124)
* population_layouts: move inside __main__ and blacken
* population_layouts: misc code cleaning and multiprocessing
* population_layouts: fix fill_values assignment of urban fractions
* population_layouts: bugfig for UK-GB naming ambiguity
* population_layouts: sort countries alphabetically for better overview
* cl_pop_layout: blacken
* cl_pop_layout: turn GeoDataFrame into GeoSeries + code cleaning
* move overriding of component attributes to central function and store in separate folder
* prepare: sort imports and remove six dependency
* prepare: remove add_emission_prices
* prepare: remove unused set_line_s_max_pu
This is a function from prepare_network
* prepare: remove unused set_line_volume_limit
This is a PyPSA-Eur function from prepare_network
* prepare: tidy add_co2limit
* remove six dependency
* prepare: tidy code first batch
* prepare: extend override_component_attrs to avoid hacky madd
* prepare: remove hacky madd() for individual components
* prepare: tidy shift function
* prepare: nodes and countries from n.buses not pop_layout
* prepare: tidy loading of pop_layout
* prepare: fix prepare_costs function
* prepare: optimise loading of traffic data
* prepare: move localizer into generate_periodic profiles
* prepare: some formatting of transport data
* prepare: eliminate some code duplication
* prepare: fix remove_h2_network
- only try to remove EU H2 store if it exists
- remove readding nodal Stores because they are never removed
* prepare: move cost adjustment to own function
* prepare: fix a syntax error
* prepare: add investment_year to get() assuming global variable
* prepare: move co2_totals out of prepare_data()
* Snakefile: remove unused prepare_sector_network inputs
* prepare: move limit p/s_nom of lines/links into function
* prepare: tidy add_co2limit file handling
* Snakefile: fix tabs
* override_component_attrs: add n/a defaults
* README: Add network picture to make scope clear
* README: Fix date of preprint (was too optimistic...)
* prepare: move some more config options to config.yaml
* prepare: runtime bugfixes
* fix benchmark path
* adjust plot ylims
* add unit attribute to bus, correct cement capture efficiency
* bugfix: land usage constrained missed inplace operation
Co-authored-by: Tom Brown <tom@nworbmot.org>
* add release notes
* remove old fix_branches() function
* deps: make geopy optional, remove unused imports
* increase default BarConvTol
* get ready for upcoming PyPSA release
* re-remove ** bug
* amend release notes
Co-authored-by: Tom Brown <tom@nworbmot.org>
Previously, the transformation of the Steel and Aluminum production was assumed to occur overnight.
This commit enables the definition of a transformation path via the config.yaml file.
This requires adding the {planning_horizon} to the input and output file name of the following rules:
build_industrial_production_per_country_tomorrow
build_industrial_production_per_node
build_industry_energy_demand_per_node
prepare_sector_network
When plotting the balance for H2, the rename dictionary merges all the demands containing H2.
This commit disables such merging and keeps different colours for H2 in shipping and H2 in industry. This is useful when one wants to look at the H2 balance and have an overview of where the H2 is consumed in the model.
The share of shipping demand that is transformed is defined now for different years to be used with the myopic code.
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.
Fixing that the code stops with an error if running without the industry sector, e.g. only including electricity and heat. When the industry sector is not included, balances_df.index.levels[0] does not include "process emissions". Using the XOR operator "^" does then add it instead of removing it when compared to the co2_carriers list. The XOR operator does only work as intended, i.e. excluding the three emission-related strings, when these strings are already in the original list. The proposed change fixes the issue.
* Make code more readable; remove misleading function arguments and add necessary ones
* Update scripts/build_energy_totals.py
Co-authored-by: Fabian Neumann <fabian.neumann@outlook.de>
Recently ICE vehicles were added, but the emissions were not accounted
for. Now, like aviation emissions, they are added as a negative load
to the "co2 atmosphere" bus.
The heat output from the carbon capture (CC) was being subtracted from
the CHP rather than the heat input.
Since the heat output and heat input are the same in the DEA
technology database (but at different temperatures), this bug has no
consequence, but still better to correct it.
Reasoning: we can also have fossil and biomass liquid hydrocarbons, as
well as production from the Fischer-Tropsch process, particularly for
simulations before 2050.
For the myopic method, based on the carbon budget indicated in the config file (sector_opts), a CO2 limit is calculated for every planning_horizon following an exponential or beta decay. A file with CO2 limit in every planning_horizon and a plot showing historical and planned CO2 emissions
are saved in the results
**This requires updating to UNFCCC_v23.csv in the data bundle**
This enables that the same function is used to read emissions in 2018, which are assumed to remain constant in 2019 and 2020 and subtracted from carbon budget (estimated from 2018 on).
I checked and 1990 emissions calculated with UNFCCC_v23 are very similar to those calculated with UNFCCC_v21 (<1% differences in all values at EU level).
Some countries show higher deviations, mainly in domestic aviation and navigation. I guess because those values started to be reported later and v23 should include more accurate values.
Running the rule solve_network in the university cluster, I was getting a "No space left on device" error. Making solve_dir=tmpdir by default avoids this error and makes it easier to identify any problem with temp files
This was breaking add_existing_baseyear.py and it is now fixed. Column name for commissioning year in powerplantmatching has changed. Now "DataIn" is used as column name, also when renewable capacities per country are added to the power plants dataframe
This is almost a direct copy PyPSA-Eur #167https://github.com/PyPSA/pypsa-eur/pull/167
A factor altering the maximum capacity (p_nom_max) can also be specified by e.g. solar+p3
One should be careful when using this for solar because the factor is applied to all the generators whose carrier includes the string 'solar' (i.e., it is applied to both utility and rooftop solar)
I would suggest implementing 'solar utility' and 'solar rooftop' as carriers, since this can be useful for other selecting processes. Is there is any reason for keeping 'solar' as a carrier for 'solar utility'?
The previous way of increasing maximum capacity via the config file (e.g 'solar3') is still present in the code.
For the myopic method, based on the carbon budget indicated in the config file (sector_opts), a CO2 limit is calculated for every planning_horizon following an exponential or beta decay. A file with CO2 limit in every planning_horizon and a plot showing historical and planned CO2 emissions
are saved in the results
**This requires updating to UNFCCC_v23.csv in the data bundle**
This enables that the same function is used to read emissions in 2018, which are assumed to remain constant in 2019 and 2020 and subtracted from carbon budget (estimated from 2018 on).
I checked and 1990 emissions calculated with UNFCCC_v23 are very similar to those calculated with UNFCCC_v21 (<1% differences in all values at EU level).
Some countries show higher deviations, mainly in domestic aviation and navigation. I guess because those values started to be reported later and v23 should include more accurate values.
Running the rule solve_network in the university cluster, I was getting a "No space left on device" error. Making solve_dir=tmpdir by default avoids this error and makes it easier to identify any problem with temp files
This was breaking add_existing_baseyear.py and it is now fixed. Column name for commissioning year in powerplantmatching has changed. Now "DataIn" is used as column name, also when renewable capacities per country are added to the power plants dataframe
Before it just had a fixed marginal cost. Now it uses DEA assumptions
for heat, electricity and capital costs.
This necessitates locating it somewhere concrete. Heat is taken from
urban central or decentral buses.
Use DEA assumptions for post-combustion carbon capture.
Also rename CCS as CC whenever only carbon capture is involved, since
sequestration (or CCU) is a separate step.
Only change was to remove the Store-Link-Bus combinations for
batteries and H2 storage from PyPSA-Eur, since they are implemented
with different names, costs and voltage level in PyPSA-Eur-Sec.
Removals are now done in a more transparent way in the config.yaml.
The assumptions for c_b and c_v and eta were arranged assuming
extraction plants (like the coal CHP in DEA).
However, if you look in DEA assumptions at "09b Wood Pellets Medium"
(used for solid biomass CHP) and "Gas turbine simple cycle (large)"
(used for gas CHP) they are not extraction plants but back pressure
plants.
The back pressure coefficient in DEA c_b is simply
c_b = name plate electricity efficiency / name plate heat efficiency
both measured when both heat and electricity are produced at maximum.
For the extraction plants, the efficiency was measured in condensation
mode, i.e. no heat production.
In almost 99.5% of cases the CHP dispatches along the backpressure
line where heat output is proportional to electricity output.
So we can switch to a single link to avoid the burden of modelling the
full electricity-heat feasibility space of CHPs.
This only applies to large CHPs in district heating networks.
Specify as dictionary, use get_parameter to get correct value.
Also remove old parameter "space_heating_fraction" since this is
superceded by the new exogenous retro code.
Strategy is too keep as much of configuration in config.yaml as
possible.
We also aim to allow exogenous investment-year-dependent
configurations to be done in a similar manner (e.g. share of district
heating or FCEV transport).
Since today's industrial electricity demand is distributed by
population and GDP, subtract this from the regular electricity demand
(which already has space/water heating subtracted).
Now regular electricity demand is only non-heating electricity demand
in residential and tertiary sectors.
Add back new industry electricity demand at the correct locations, as
determined using the hotmaps database.
I.e. per sector geographical distribution of industrial facilities
within each country.
Drop facilities outside Europe and with no geocoordinates.
Use ETS emissions as a distribution key; where emissions data is
missing, substitute with an average for that sector and that country
(strong assumption).
I.e. when the generators are clustered to the "simplified" network
resolution, but the grid is clustered further, e.g. by using the
clusters = 37m "m" option.
List classes in config.yaml, rather than integer selection in
build_biomass_potentials.py.
Also output potentials for all years and scenarios for analysis.
I.e. when the generators are clustered to the "simplified" network
resolution, but the grid is clustered further, e.g. by using the
clusters = 37m "m" option.
Don't fix uniform ratios e.g. of 0.3:0.7 primary:secondary for steel
and aluminium, but convert the necessary amount of existing primary in
each country so that the overall ratio applies at European level.
This stops sudden swings from primary to secondary in countries
dominated by primary production.
Remove non-existing biomass from chemicals and cement, since these
need higher temperatures than achievable with residues and waste.
Increase biomass in pulp and paper (since already used extensively
here and T < 500), and replace methane with biomass in food, beverages
and tobacco, since temperatures needed are low (T < 500).
This allows us to control the substitution of natural gas for hydrogen
in NH3 production.
Remaining basic chemicals are olefins, BTX and chlorine.
For 2015 NH3 production, we use the USGS data source.
This was handled before in industry_sector_ratios.csv which was
confusing.
Now industry_sector_ratios.csv represents the genuine energy
consumption per tonne of material for each industrial route
(MWh/tMaterial).
An new file is created with ktMaterial/a in
industrial_production_per_country_tomorrow.csv which contains changes
to the fraction of primary/secondary routes compared to today's
production in industrial_production_per_country.csv.
This is less confusing I think.
Rather than taking a mean of the clustered connection costs.
Apply cost update also for overnight scenarios based on planning year.
Add land costs for onshore wind.
- add_brownfield.py: Have to make sure that for each CHP there is both
a heat and electric link, but they have different p_nom for each
CHP, so have to make sure we don't remove one without the other.
- solve_network.py: Make sure extra_functionality constraints for CHP
power-heat feasibility graph also work for non-extendable CHPs.
This simplifies the structure of add_brownfield.py dramatically.
Some other changes need to be make elsewhere because of name
changes (e.g. battery constraints in solve_network.py).
In order to calculate connection costs, average values for underground_fraction and average_distance are calculated for all the buses in the initial network mapped to the clustered network.
Previously they were distributed only by country to the first node in
the country.
Now conventional power plants are assigned to the correct node using
the bus map from PyPSA-Eur.
Wind and solar are distributed in each country by capacity factor.
The code has been refactored and a bug was fixed whereby total
capacities of wind and solar in each country were not correct.
Now the years in the config.yaml for myopic are integers not strings.
In prepare_costs, you need the min_count=1 in the sum so that it
generates NaNs for missing data (rather than 0) so that NaNs can be
subsituted by .fillna in the next line. Otherwise many values
(discount rates and efficiencies for solar, wind) are set to zero.
Also added carriers, storage and generators for coal, nuclear and
oil. (This needs to be organized better soon so that the carriers are
defined in config.yaml.)
Existing onwind and offwind capacity are now read from IRENA database, similarly to solar capacities. Previously we were using thewindpower.net database which is not open.
