Merge pull request #73 from PyPSA/biomass-transport

Biomass transport

Snakefile

@@ -1,4 +1,7 @@
 
+from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
+HTTP = HTTPRemoteProvider()
+
 configfile: "config.yaml"
 
 
@@ -180,6 +183,21 @@ rule build_biomass_potentials:
     script: 'scripts/build_biomass_potentials.py'
 
 
+if config["sector"]["biomass_transport"]:
+    rule build_biomass_transport_costs:
+        input:
+            transport_cost_data=HTTP.remote("publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass potentials in europe_web rev.pdf", keep_local=True)
+        output:
+            biomass_transport_costs="resources/biomass_transport_costs.csv",
+        threads: 1
+        resources: mem_mb=1000
+        benchmark: "benchmarks/build_biomass_transport_costs"
+        script: 'scripts/build_biomass_transport_costs.py'
+    build_biomass_transport_costs_output = rules.build_biomass_transport_costs.output
+else:
+    build_biomass_transport_costs_output = {}
+
+
 rule build_ammonia_production:
     input:
         usgs="data/myb1-2017-nitro.xls"
@@ -321,8 +339,8 @@ rule prepare_sector_network:
         energy_totals_name='resources/energy_totals.csv',
         co2_totals_name='resources/co2_totals.csv',
         transport_name='resources/transport_data.csv',
-        traffic_data_KFZ = "data/emobility/KFZ__count",
-        traffic_data_Pkw = "data/emobility/Pkw__count",
+        traffic_data_KFZ="data/emobility/KFZ__count",
+        traffic_data_Pkw="data/emobility/Pkw__count",
         biomass_potentials='resources/biomass_potentials.csv',
         heat_profile="data/heat_load_profile_BDEW.csv",
         costs=CDIR + "costs_{planning_horizons}.csv",
@@ -352,7 +370,8 @@ rule prepare_sector_network:
         solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
         solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
         solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc",
-	    **build_retro_cost_output
+        **build_retro_cost_output,
+        **build_biomass_transport_costs_output
     output: RDIR + '/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
     threads: 1
     resources: mem_mb=2000

config.default.yaml

@@ -234,6 +234,7 @@ sector:
   electricity_grid_connection: true  # only applies to onshore wind and utility PV
   gas_distribution_grid: true
   gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
+  biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
 
@@ -275,7 +276,6 @@ industry:
   hotmaps_locate_missing: false
   reference_year: 2015
 
-
 costs:
   lifetime: 25 #default lifetime
   # From a Lion Hirth paper, also reflects average of Noothout et al 2016
@@ -509,5 +509,6 @@ plotting:
     shipping oil: "#6495ED"
     shipping oil emissions: "#6495ED"
     electricity distribution grid: '#333333'
+    solid biomass transport: green
     H2 for industry: "#222222"
     H2 for shipping: "#6495ED"

doc/release_notes.rst

@@ -76,6 +76,12 @@ Future release
 * 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.
+* Add option to regionally disaggregate biomass potential to individual nodes
+  (currently given per country, then distributed by population density within)
+  and allow the transport of solid biomass.
+  The transport costs are determined based on the `JRC-EU-Times Bioenergy report <http://dx.doi.org/10.2790/01017>`_
+  in the new optional rule ``build_biomass_transport_costs``.
+  Biomass transport can be activated with the setting ``sector: biomass_transport: true``.
 * Compatibility with ``xarray`` version 0.19.
 
 PyPSA-Eur-Sec 0.5.0 (21st May 2021)

doc/spatial_resolution.rst

@@ -44,8 +44,10 @@ Hydrogen network: nodal.
 Methane network: single node for Europe, since future demand is so
 low and no bottlenecks are expected.
 
-Solid biomass:  single node for Europe, until transport costs can be
-incorporated.
+Solid biomass: choice between single node for Europe and nodal where biomass
+potential is regionally disaggregated (currently given per country,
+then distributed by population density within)
+and transport of solid biomass is possible.
 
 CO2:  single node for Europe, but a transport and storage cost is added for
 sequestered CO2. Optionally: nodal, with CO2 transport via pipelines.

scripts/build_biomass_transport_costs.py

@@ -0,0 +1,90 @@
+"""
+Reads biomass transport costs for different countries of the JRC report
+
+    "The JRC-EU-TIMES model.
+    Bioenergy potentials
+    for EU and neighbouring countries."
+    (2015)
+
+converts them from units 'EUR per km/ton' -> 'EUR/ (km MWh)'
+
+assuming as an approximation energy content of wood pellets
+
+@author: bw0928
+"""
+
+import pandas as pd
+import tabula as tbl
+
+ENERGY_CONTENT = 4.8  # unit MWh/t (wood pellets)
+
+def get_countries():
+
+    pandas_options = dict(
+        skiprows=range(6),
+        header=None,
+        index_col=0
+    )
+
+    return tbl.read_pdf(
+        str(snakemake.input.transport_cost_data),
+        pages="145",
+        multiple_tables=False,
+        pandas_options=pandas_options
+    )[0].index
+
+
+def get_cost_per_tkm(page, countries):
+
+    pandas_options = dict(
+        skiprows=range(6),
+        header=0,
+        sep=' |,',
+        engine='python',
+        index_col=False,
+    )
+
+    sc = tbl.read_pdf(
+        str(snakemake.input.transport_cost_data),
+        pages=page,
+        multiple_tables=False,
+        pandas_options=pandas_options
+    )[0]
+    sc.index = countries
+    sc.columns = sc.columns.str.replace("€", "EUR")
+
+    return sc
+
+
+def build_biomass_transport_costs():
+
+    countries = get_countries()
+
+    sc1 = get_cost_per_tkm(146, countries)
+    sc2 = get_cost_per_tkm(147, countries)
+
+    # take mean of both supply chains
+    to_concat = [sc1["EUR/km/ton"], sc2["EUR/km/ton"]]
+    transport_costs = pd.concat(to_concat, axis=1).mean(axis=1)
+
+    # convert tonnes to MWh
+    transport_costs /= ENERGY_CONTENT
+    transport_costs.name = "EUR/km/MWh"
+
+    # rename country names
+    to_rename = {
+        "UK": "GB",
+        "XK": "KO",
+        "EL": "GR"
+    }
+    transport_costs.rename(to_rename, inplace=True)
+
+    # add missing Norway with data from Sweden
+    transport_costs["NO"] = transport_costs["SE"]
+
+    transport_costs.to_csv(snakemake.output[0])
+
+
+if __name__ == "__main__":
+
+    build_biomass_transport_costs()