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.
* Propagate the solver log file name to the solver
Previously, the PyPSA network solving functions were not told about
the solver logfile specified in the Snakemake file.
* Pass solver_logfile on as kwargs
The `solve_network` function passes any additional arguments on to the
pypsa `network_lopf` and `ilopf` functions. Now we also pass
`solver_logfile` on as part of kwargs.
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.
* Add focus_weights to pre-clustering
Hey guys,
another quick fix since I noticed it wasn't implemented yet: When pre-clustering the network, the focus_weights have not yet been considered. This may distort clustering results when pre-clustering to a low resolution.
* Update release_notes.rst
Hey guys,
please correct me if I'm wrong, but I think pricing both the charge and discharge of the battery store component with the inverter's capital costs results in duplicating costs since a bi-directional inverter is considered (I checked the source of the cost assumption, Budischak 2013).
Before working with PyPSA-EUR, I have worked with the toy model WHOBS where only the charging-link of the battery store component is priced with its capital costs (cf. https://github.com/PyPSA/WHOBS/blob/master/run_single_simulation.ipynb). The proposed change in code is equivalent.
