Merge branch 'master' into biomass-transport

.gitignore

@@ -28,7 +28,7 @@ gurobi.log
 /data/.nfs*
 /data/Industrial_Database.csv
 /data/retro/tabula-calculator-calcsetbuilding.csv
-
+/data
 *.org
 
 *.nc

Snakefile

@@ -237,10 +237,10 @@ rule build_industrial_production_per_country_tomorrow:
     input:
         industrial_production_per_country="resources/industrial_production_per_country.csv"
     output:
-        industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow.csv"
+        industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow_{planning_horizons}.csv"
     threads: 1
     resources: mem_mb=1000
-    benchmark: "benchmarks/build_industrial_production_per_country_tomorrow"
+    benchmark: "benchmarks/build_industrial_production_per_country_tomorrow_{planning_horizons}"
     script: 'scripts/build_industrial_production_per_country_tomorrow.py'
 
 
@@ -260,25 +260,25 @@ rule build_industrial_distribution_key:
 rule build_industrial_production_per_node:
     input:
         industrial_distribution_key="resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv",
-        industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow.csv"
+        industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow_{planning_horizons}.csv"
     output:
-        industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}.csv"
+        industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
     threads: 1
     resources: mem_mb=1000
-    benchmark: "benchmarks/build_industrial_production_per_node/s{simpl}_{clusters}"
+    benchmark: "benchmarks/build_industrial_production_per_node/s{simpl}_{clusters}_{planning_horizons}"
     script: 'scripts/build_industrial_production_per_node.py'
 
 
 rule build_industrial_energy_demand_per_node:
     input:
         industry_sector_ratios="resources/industry_sector_ratios.csv",
-        industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}.csv",
+        industrial_production_per_node="resources/industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
         industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_elec_s{simpl}_{clusters}.csv"
     output:
-        industrial_energy_demand_per_node="resources/industrial_energy_demand_elec_s{simpl}_{clusters}.csv"
+        industrial_energy_demand_per_node="resources/industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
     threads: 1
     resources: mem_mb=1000
-    benchmark: "benchmarks/build_industrial_energy_demand_per_node/s{simpl}_{clusters}"
+    benchmark: "benchmarks/build_industrial_energy_demand_per_node/s{simpl}_{clusters}_{planning_horizons}"
     script: 'scripts/build_industrial_energy_demand_per_node.py'
 
 
@@ -350,7 +350,7 @@ rule prepare_sector_network:
         busmap=pypsaeur("resources/busmap_elec_s{simpl}_{clusters}.csv"),
         clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
         simplified_pop_layout="resources/pop_layout_elec_s{simpl}.csv",
-        industrial_demand="resources/industrial_energy_demand_elec_s{simpl}_{clusters}.csv",
+        industrial_demand="resources/industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
         heat_demand_urban="resources/heat_demand_urban_elec_s{simpl}_{clusters}.nc",
         heat_demand_rural="resources/heat_demand_rural_elec_s{simpl}_{clusters}.nc",
         heat_demand_total="resources/heat_demand_total_elec_s{simpl}_{clusters}.nc",

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
@@ -71,7 +72,8 @@ electricity:
 
 # regulate what components with which carriers are kept from PyPSA-Eur;
 # some technologies are removed because they are implemented differently
-# or have different year-dependent costs in PyPSA-Eur-Sec
+# (e.g. battery or H2 storage) or have different year-dependent costs 
+# in PyPSA-Eur-Sec
 pypsa_eur:
   Bus:
     - AC
@@ -173,6 +175,15 @@ sector:
   transport_fuel_cell_efficiency: 0.5
   transport_internal_combustion_efficiency: 0.3
   shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
+  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
+  shipping_hydrogen_share: # 1 means all hydrogen FC
+    2020: 0
+    2025: 0
+    2030: 0.05
+    2035: 0.15
+    2040: 0.3
+    2045: 0.6
+    2050: 1
   time_dep_hp_cop: true #time dependent heat pump coefficient of performance
   heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
    # conservatively high to cover hot water and space heating in poorly-insulated buildings
@@ -228,10 +239,32 @@ sector:
 
 
 industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route (DRI + EAF) versus secondary route (EAF); today fraction is 0.6
+  St_primary_fraction: # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
+    2020: 0.6
+    2025: 0.55
+    2030: 0.5
+    2035: 0.45
+    2040: 0.4
+    2045: 0.35
+    2050: 0.3
+  DRI_fraction: # fraction of the primary route converted to DRI + EAF
+    2020: 0
+    2025: 0
+    2030: 0.05
+    2035: 0.2
+    2040: 0.4
+    2045: 0.7
+    2050: 1
   H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
   elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
+  Al_primary_fraction:  # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
+    2020: 0.4
+    2025: 0.375
+    2030: 0.35
+    2035: 0.325
+    2040: 0.3
+    2045: 0.25
+    2050: 0.2
   MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
   MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
   MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
@@ -470,5 +503,9 @@ plotting:
     solid biomass: '#DAA520'
     today: '#D2691E'
     shipping: '#6495ED'
+    shipping oil: "#6495ED"
+    shipping oil emissions: "#6495ED"
     electricity distribution grid: '#333333'
     solid biomass transport: green
+    H2 for industry: "#222222"
+    H2 for shipping: "#6495ED"

doc/installation.rst

@@ -89,10 +89,8 @@ The data licences and sources are given in the following table.
 Set up the default configuration
 ================================
 
-First make your own copy of the ``config.yaml``. For overnight
-scenarios, use ``config.default.yaml``. For a pathway optimization
-with myopic foresight (which is still experimental), use
-``config.myopic.yaml``. For example:
+First make your own copy of the ``config.yaml`` based on 
+ ``config.default.yaml``. For example:
 
 .. code:: bash
 

doc/release_notes.rst

@@ -60,8 +60,12 @@ Future release
   These are included in the environment specifications of PyPSA-Eur.
 * Consistent use of ``__main__`` block and further unspecific code cleaning.
 * Distinguish costs for home battery storage and inverter from utility-scale battery costs.
-
-
+* Added option for hydrogen liquefaction costs for hydrogen demand in shipping.
+  This introduces a new ``H2 liquid`` bus at each location.
+  It is activated via ``sector: shipping_hydrogen_liquefaction: true``.
+* The share of shipping transformed into hydrogen fuel cell can be now defined for different years in the ``config.yaml`` file. The carbon emission from the remaining share is treated as a negative load on the atmospheric carbon dioxide bus, just like aviation and land transport emissions.
+* The transformation of the Steel and Aluminium production can be now defined for different years in the ``config.yaml`` file.
+* 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/build_industrial_production_per_country_tomorrow.py

@@ -2,6 +2,8 @@
 
 import pandas as pd
 
+from prepare_sector_network import get
+
 if __name__ == '__main__':
     if 'snakemake' not in globals():
         from helper import mock_snakemake
@@ -9,27 +11,35 @@ if __name__ == '__main__':
 
     config = snakemake.config["industry"]
 
+    investment_year = int(snakemake.wildcards.planning_horizons)
+
     fn = snakemake.input.industrial_production_per_country
     production = pd.read_csv(fn, index_col=0)
 
     keys = ["Integrated steelworks", "Electric arc"]
     total_steel = production[keys].sum(axis=1)
 
+    st_primary_fraction = get(config["St_primary_fraction"], investment_year)
+    dri_fraction = get(config["DRI_fraction"], investment_year)
     int_steel = production["Integrated steelworks"].sum()
-    fraction_persistent_primary = config["St_primary_fraction"] * total_steel.sum() / int_steel
+    fraction_persistent_primary = st_primary_fraction * total_steel.sum() / int_steel
 
-    dri = fraction_persistent_primary * production["Integrated steelworks"]
+    dri = dri_fraction * fraction_persistent_primary * production["Integrated steelworks"]
     production.insert(2, "DRI + Electric arc", dri)
 
-    production["Electric arc"] = total_steel - production["DRI + Electric arc"]
-    production["Integrated steelworks"] = 0.
+    not_dri = (1 - dri_fraction)
+    production["Integrated steelworks"] = not_dri * fraction_persistent_primary * production["Integrated steelworks"]
+    production["Electric arc"] = total_steel - production["DRI + Electric arc"] - production["Integrated steelworks"]
 
     keys = ["Aluminium - primary production", "Aluminium - secondary production"]
     total_aluminium = production[keys].sum(axis=1)
 
     key_pri = "Aluminium - primary production"
     key_sec = "Aluminium - secondary production"
-    fraction_persistent_primary = config["Al_primary_fraction"] * total_aluminium.sum() / production[key_pri].sum()
+
+    al_primary_fraction = get(config["Al_primary_fraction"], investment_year)
+    fraction_persistent_primary = al_primary_fraction * total_aluminium.sum() / production[key_pri].sum()
+    
     production[key_pri] = fraction_persistent_primary * production[key_pri]
     production[key_sec] = total_aluminium - production[key_pri]
 

scripts/plot_summary.py

@@ -34,7 +34,9 @@ def rename_techs(label):
     rename_if_contains_dict = {
         "water tanks": "hot water storage",
         "retrofitting": "building retrofitting",
-        "H2": "hydrogen storage",
+        "H2 Electrolysis": "hydrogen storage",
+        "H2 Fuel Cell": "hydrogen storage",
+        "H2 pipeline": "hydrogen storage",
         "battery": "battery storage",
         "CC": "CC"
     }

scripts/prepare_sector_network.py

@@ -986,7 +986,7 @@ def add_storage(n, costs):
         )
 
     # hydrogen stored overground (where not already underground)
-    h2_capital_cost = costs.at["hydrogen storage tank", "fixed"]
+    h2_capital_cost = costs.at["hydrogen storage tank incl. compressor", "fixed"]
     nodes_overground = cavern_nodes.index.symmetric_difference(nodes)
 
     n.madd("Store",
@@ -1021,9 +1021,9 @@ def add_storage(n, costs):
         p_min_pu=-1,
         p_nom_extendable=True,
         length=h2_links.length.values,
-        capital_cost=costs.at['H2 pipeline', 'fixed'] * h2_links.length.values,
+        capital_cost=costs.at['H2 (g) pipeline', 'fixed'] * h2_links.length.values,
         carrier="H2 pipeline",
-        lifetime=costs.at['H2 pipeline', 'lifetime']
+        lifetime=costs.at['H2 (g) pipeline', 'lifetime']
     )
 
     n.add("Carrier", "battery")
@@ -1077,7 +1077,7 @@ def add_storage(n, costs):
             carrier="Sabatier",
             efficiency=costs.at["methanation", "efficiency"],
             efficiency2=-costs.at["methanation", "efficiency"] * costs.at['gas', 'CO2 intensity'],
-            capital_cost=costs.at["methanation", "fixed"],
+            capital_cost=costs.at["methanation", "fixed"] * costs.at["methanation", "efficiency"],  # costs given per kW_gas
             lifetime=costs.at['methanation', 'lifetime']
         )
 
@@ -1824,18 +1824,66 @@ def add_industry(n, costs):
         p_set=industrial_demand.loc[nodes, "hydrogen"] / 8760
     )
 
+    if options["shipping_hydrogen_liquefaction"]:
+
+        n.madd("Bus",
+            nodes,
+            suffix=" H2 liquid",
+            carrier="H2 liquid",
+            location=nodes
+        )
+
+        n.madd("Link",
+            nodes + " H2 liquefaction",
+            bus0=nodes + " H2",
+            bus1=nodes + " H2 liquid",
+            carrier="H2 liquefaction",
+            efficiency=costs.at["H2 liquefaction", 'efficiency'],
+            capital_cost=costs.at["H2 liquefaction", 'fixed'],
+            p_nom_extendable=True,
+            lifetime=costs.at['H2 liquefaction', 'lifetime']
+        )
+
+        shipping_bus = nodes + " H2 liquid"
+    else:
+        shipping_bus = nodes + " H2"
+
     all_navigation = ["total international navigation", "total domestic navigation"]
     efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
-    p_set = nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
+    shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
+    p_set = shipping_hydrogen_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
 
     n.madd("Load",
         nodes,
         suffix=" H2 for shipping",
-        bus=nodes + " H2",
+        bus=shipping_bus,
         carrier="H2 for shipping",
         p_set=p_set
     )
 
+    if shipping_hydrogen_share < 1:
+
+        shipping_oil_share = 1 - shipping_hydrogen_share
+        
+        p_set = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
+        
+        n.madd("Load",
+            nodes,
+            suffix=" shipping oil",
+            bus="EU oil",
+            carrier="shipping oil",
+            p_set=p_set
+        )
+        
+        co2 = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
+
+        n.add("Load",
+            "shipping oil emissions",
+            bus="co2 atmosphere",
+            carrier="shipping oil emissions",
+            p_set=-co2
+        )
+
     if "EU oil" not in n.buses.index:
 
         n.add("Bus",
@@ -2035,14 +2083,19 @@ 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")

scripts/solve_network.py

@@ -151,7 +151,6 @@ def add_chp_constraints(n):
 
 
 def extra_functionality(n, snapshots):
-    add_chp_constraints(n)
     add_battery_constraints(n)