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.
