Sensitivity e nom max (#143)

* 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>

config.default.yaml

@@ -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

doc/release_notes.rst

@@ -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)
 ===================================

scripts/prepare_sector_network.py

@@ -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")