bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

Sensitivity e nom max (#143)

Browse Source 
* Update .gitignore

* include e_nom_max sensitivity

* remove change in gitignore

* Update doc/release_notes.rst

Co-authored-by: Fabian Neumann <fabian.neumann@outlook.de>
This commit is contained in:
martavp 2021-07-06 17:12:39 +02:00 committed by GitHub
parent b2872cc6b0
commit 3a6a1f90ab
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 10 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
config.default.yaml
View File

@ -30,6 +30,7 @@ scenario:
# B for biomass supply, I for industry, shipping and aviation
# solar+c0.5 reduces the capital cost of solar to 50\% of reference value
# solar+p3 multiplies the available installable potential by factor 3
# co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
# emissions throughout the transition path in the timeframe determined by the

2
doc/release_notes.rst
View File

@ -62,6 +62,8 @@ Future release
* Distinguish costs for home battery storage and inverter from utility-scale battery costs.
* Include the option to alter the maximum energy capacity of a store via the ``carrier+factor`` in the ``{sector_opts}`` wildcard. This can be useful for sensitivity analyses. Example: ``co2 stored+e2`` multiplies the ``e_nom_max`` by factor 2. In this example, ``e_nom_max`` represents the CO2 sequestration potential in Europe.
PyPSA-Eur-Sec 0.5.0 (21st May 2021)
===================================

9
scripts/prepare_sector_network.py
View File

@ -1925,14 +1925,19 @@ def maybe_adjust_costs_and_potentials(n, opts):
suptechs = map(lambda c: c.split("-", 2)[0], carrier_list)
if oo[0].startswith(tuple(suptechs)):
carrier = oo[0]
attr_lookup = {"p": "p_nom_max", "c": "capital_cost"}
attr_lookup = {"p": "p_nom_max", "e": "e_nom_max", "c": "capital_cost"}
attr = attr_lookup[oo[1][0]]
factor = float(oo[1][1:])
#beware if factor is 0 and p_nom_max is np.inf, 0*np.inf is nan
if carrier == "AC": # lines do not have carrier
n.lines[attr] *= factor
else:
comps = {"Generator", "Link", "StorageUnit"} if attr == 'p_nom_max' else {"Generator", "Link", "StorageUnit", "Store"}
if attr == 'p_nom_max':
comps = {"Generator", "Link", "StorageUnit"}
elif attr = 'e_nom_max':
comps = {"Store"}
else:
comps = {"Generator", "Link", "StorageUnit", "Store"}
for c in n.iterate_components(comps):
if carrier=='solar':
sel = c.df.carrier.str.contains(carrier) & ~c.df.carrier.str.contains("solar rooftop")