If the user selects a date range of less than a year in `config.yaml` the snapshot weightings should be scaled to add up to represet 8760 hours (1 year) such that operational expenses and investments are aligned in the objective function.
These are different for residential and services demand.
Also include Snakefile in config files copied for each run.
Use gurobi settings from gurobi support for speed.
Commented out settings for testing randomness for noise.
All urban central (i.e. district heating) is aggregated to the same
profile and bus.
The code is now written to cycle over each heating sector to add
demand and supply technologies, only changing what is necessary to
change, rather than just copying chunks of code and modifying
parameters there. This should make it easier to get an overview of
what's going on.
These are specified in data/heat_load_profile.csv.
The resulting heat_demand df has MultiIndex columns, where the first
level is ["sector use"], and the second level level is nodes.
Because there was insufficient solid biomass in 3-4 countries to
supply industry for it locally, and we need to account for transport
of solid biomass.
Should be replaced by transport cost links between countries.
Heat buses renamed to:
rural (for low-density areas where district heating not possible)
urban decentral (for high-density areas without district heating and
individual heating technologies) (used to be called "urban")
urban central (for high-density areas with district heating) (used to
be called "central")
District heating losses applied only to urban central.
* skip_iterating flag: solve network only once without updating impedances
* extra_functionality parameter: add function to modify pyomo model
* extra_functionality args: function arguments for extra_functionality
* extra_postprocessing: add function for postprocessing steps depending on n.model
