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.
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.
Reasoning: we can also have fossil and biomass liquid hydrocarbons, as
well as production from the Fischer-Tropsch process, particularly for
simulations before 2050.
This substitutes the previous way of doing it. Now, to multiply the reference p_nom_max by 3, one should include in the config file 'solar+p3' (instead of the previous solarx3)
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
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.
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superceded by the new exogenous retro code.
It is identical to config.default.yaml except for two parameters
(foresight and planning_horizons) so I decided to consolidate the
example configs. Instructions for how to use the myopic foresight can
be found in the documentation (now updated).
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).
List classes in config.yaml, rather than integer selection in
build_biomass_potentials.py.
Also output potentials for all years and scenarios for analysis.
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.
- 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.
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.
