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superceded by the new exogenous retro code.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.
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.
- 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.
This simplifies the structure of add_brownfield.py dramatically.
Some other changes need to be make elsewhere because of name
changes (e.g. battery constraints in solve_network.py).
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.
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.
In prepare_costs, you need the min_count=1 in the sum so that it
generates NaNs for missing data (rather than 0) so that NaNs can be
subsituted by .fillna in the next line. Otherwise many values
(discount rates and efficiencies for solar, wind) are set to zero.
Also added carriers, storage and generators for coal, nuclear and
oil. (This needs to be organized better soon so that the carriers are
defined in config.yaml.)
Existing onwind and offwind capacity are now read from IRENA database, similarly to solar capacities. Previously we were using thewindpower.net database which is not open.
