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
**This requires updating to UNFCCC_v23.csv in the data bundle**
This enables that the same function is used to read emissions in 2018, which are assumed to remain constant in 2019 and 2020 and subtracted from carbon budget (estimated from 2018 on).
I checked and 1990 emissions calculated with UNFCCC_v23 are very similar to those calculated with UNFCCC_v21 (<1% differences in all values at EU level).
Some countries show higher deviations, mainly in domestic aviation and navigation. I guess because those values started to be reported later and v23 should include more accurate values.
Before it just had a fixed marginal cost. Now it uses DEA assumptions
for heat, electricity and capital costs.
This necessitates locating it somewhere concrete. Heat is taken from
urban central or decentral buses.
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.
The assumptions for c_b and c_v and eta were arranged assuming
extraction plants (like the coal CHP in DEA).
However, if you look in DEA assumptions at "09b Wood Pellets Medium"
(used for solid biomass CHP) and "Gas turbine simple cycle (large)"
(used for gas CHP) they are not extraction plants but back pressure
plants.
The back pressure coefficient in DEA c_b is simply
c_b = name plate electricity efficiency / name plate heat efficiency
both measured when both heat and electricity are produced at maximum.
For the extraction plants, the efficiency was measured in condensation
mode, i.e. no heat production.
In almost 99.5% of cases the CHP dispatches along the backpressure
line where heat output is proportional to electricity output.
So we can switch to a single link to avoid the burden of modelling the
full electricity-heat feasibility space of CHPs.
This only applies to large CHPs in district heating networks.
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).
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.
