* Make code more readable; remove misleading function arguments and add necessary ones
* Update scripts/build_energy_totals.py
Co-authored-by: Fabian Neumann <fabian.neumann@outlook.de>
Recently ICE vehicles were added, but the emissions were not accounted
for. Now, like aviation emissions, they are added as a negative load
to the "co2 atmosphere" bus.
The heat output from the carbon capture (CC) was being subtracted from
the CHP rather than the heat input.
Since the heat output and heat input are the same in the DEA
technology database (but at different temperatures), this bug has no
consequence, but still better to correct it.
Reasoning: we can also have fossil and biomass liquid hydrocarbons, as
well as production from the Fischer-Tropsch process, particularly for
simulations before 2050.
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 is almost a direct copy PyPSA-Eur #167https://github.com/PyPSA/pypsa-eur/pull/167
A factor altering the maximum capacity (p_nom_max) can also be specified by e.g. solar+p3
One should be careful when using this for solar because the factor is applied to all the generators whose carrier includes the string 'solar' (i.e., it is applied to both utility and rooftop solar)
I would suggest implementing 'solar utility' and 'solar rooftop' as carriers, since this can be useful for other selecting processes. Is there is any reason for keeping 'solar' as a carrier for 'solar utility'?
The previous way of increasing maximum capacity via the config file (e.g 'solar3') is still present in the code.
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
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. 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.
