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
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superceded by the new exogenous retro code.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).
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.
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.
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.
This reinstates old behaviour for both myopic and overnight foresight
settings.
Also account for the fact that the planning_horizon is now integer in
config.yaml rather than a string.
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.
This commit now work with PyPSA-Eur 0.1.0 (tested with commit
bb3477 from 14th April 2020).
Changes to line/link_widths/colors for plotting networks in PyPSA
0.17.0.
Other corrections to plotting code so it works with this version.
Include oil boilers in colors in config.yaml.
* changes for retrofitting
* changed Snakefile to work with new pypsa-eur version, change solve_network.py to clip also n.generators_t.p_min_pu
* removed retrofitting data files
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.
