Merge branch 'master' of github.com:nworbmot/PyPSA-Eur-Sec

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_KFZ="data/emobility/KFZ__count",
-        traffic_data_Pkw = "data/emobility/Pkw__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

@@ -223,7 +223,8 @@ sector:
   co2_vent: true
   SMR: true
   co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for transport and sequestration of CO2
+  co2_sequestration_cost: 10   #EUR/tCO2 for sequestration of CO2
+  co2_network: false
   cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
   hydrogen_underground_storage: true
   use_fischer_tropsch_waste_heat: true
@@ -233,6 +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
 
@@ -480,6 +482,7 @@ plotting:
     hot water storage: '#BBBBBB'
     hot water charging: '#BBBBBB'
     hot water discharging: '#999999'
+    CO2 pipeline: '#999999'
     CHP: r
     CHP heat: r
     CHP electric: r
@@ -520,5 +523,6 @@ 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

@@ -62,12 +62,26 @@ 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.
+* Add option to regionally resolve CO2 storage and add CO2 pipeline transport because geological storage potential,
+  CO2 utilisation sites and CO2 capture sites may be separated.
+  The CO2 network is built from zero based on the topology of the electricity grid (greenfield).
+  Pipelines are assumed to be bidirectional and lossless.
+  Furthermore, neither retrofitting of natural gas pipelines (required pressures are too high, 80-160 bar vs <80 bar)
+  nor other modes of CO2 transport (by ship, road or rail) are considered.
+  The regional representation of CO2 is activated with the config setting ``sector: co2_network: true`` but is deactivated by default.
+  The global limit for CO2 sequestration now applies to the sum of all CO2 stores via an ``extra_functionality`` constraint.
 * 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.
+* 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.
 * Separate basic chemicals into HVC, chlorine, methanol and ammonia [`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
 * Add option to specify reuse, primary production, and mechanical and chemical recycling fraction of platics [`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].

doc/spatial_resolution.rst

@@ -44,11 +44,13 @@ 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
+Solid biomass: choice between single node for Europe and nodal where biomass
-incorporated.
+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.
+sequestered CO2. Optionally: nodal, with CO2 transport via pipelines.
 
 Liquid hydrocarbons: single node for Europe, since transport costs for
 liquids are low.

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()

scripts/prepare_sector_network.py

@@ -19,10 +19,60 @@ from helper import override_component_attrs
 import logging
 logger = logging.getLogger(__name__)
 
+from types import SimpleNamespace
+spatial = SimpleNamespace()
+
+
+def define_spatial(nodes):
+    """
+    Namespace for spatial
+    
+    Parameters
+    ----------
+    nodes : list-like
+    """
+
+    global spatial
+    global options
+
+    spatial.nodes = nodes
+    
+    # biomass
+
+    spatial.biomass = SimpleNamespace()
+
+    if options["biomass_transport"]:
+        spatial.biomass.nodes = nodes + " solid biomass"
+        spatial.biomass.locations = nodes
+        spatial.biomass.industry = nodes + " solid biomass for industry"
+        spatial.biomass.industry_cc = nodes + " solid biomass for industry CC"
+    else:
+        spatial.biomass.nodes = ["EU solid biomass"]
+        spatial.biomass.locations = ["EU"]
+        spatial.biomass.industry = ["solid biomass for industry"]
+        spatial.biomass.industry_cc = ["solid biomass for industry CC"]
+
+    spatial.biomass.df = pd.DataFrame(vars(spatial.biomass), index=nodes)
+    
+    # co2
+
+    spatial.co2 = SimpleNamespace()
+    
+    if options["co2_network"]:
+        spatial.co2.nodes = nodes + " co2 stored"
+        spatial.co2.locations = nodes
+        spatial.co2.vents = nodes + " co2 vent"
+    else:
+        spatial.co2.nodes = ["co2 stored"]
+        spatial.co2.locations = ["EU"]
+        spatial.co2.vents = ["co2 vent"]
+
+    spatial.co2.df = pd.DataFrame(vars(spatial.co2), index=nodes)
+
 
 def emission_sectors_from_opts(opts):
 
-    sectors = ["electricity"]
+    sectors = ["electricity"]   
     if "T" in opts:
         sectors += [
             "rail non-elec",
@@ -54,6 +104,40 @@ def get(item, investment_year=None):
         return item
 
 
+def create_network_topology(n, prefix, connector=" -> "):
+    """
+    Create a network topology like the power transmission network.
+    
+    Parameters
+    ----------
+    n : pypsa.Network
+    prefix : str
+    connector : str
+        
+    Returns
+    -------
+    pd.DataFrame with columns bus0, bus1 and length
+    """
+
+    ln_attrs = ["bus0", "bus1", "length"]
+    lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
+
+    candidates = pd.concat([
+        n.lines[ln_attrs],
+        n.links.loc[n.links.carrier == "DC", lk_attrs]
+    ]).fillna(0)
+
+    positive_order = candidates.bus0 < candidates.bus1
+    candidates_p = candidates[positive_order]
+    swap_buses = {"bus0": "bus1", "bus1": "bus0"}
+    candidates_n = candidates[~positive_order].rename(columns=swap_buses)
+    candidates = pd.concat([candidates_p, candidates_n])
+
+    topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
+    topo.index = topo.apply(lambda c: prefix + c.bus0 + connector + c.bus1, axis=1)
+    return topo
+
+
 def co2_emissions_year(countries, opts, year):
     """
     Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
@@ -141,6 +225,53 @@ def add_lifetime_wind_solar(n, costs):
         n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, 'lifetime']
 
 
+def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
+    """
+    Create a network topology like the power transmission network.
+    
+    Parameters
+    ----------
+    n : pypsa.Network
+    prefix : str
+    connector : str
+    bidirectional : bool, default True
+        True: one link for each connection
+        False: one link for each connection and direction (back and forth)
+        
+    Returns
+    -------
+    pd.DataFrame with columns bus0, bus1 and length
+    """
+
+    ln_attrs = ["bus0", "bus1", "length"]
+    lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
+
+    candidates = pd.concat([
+        n.lines[ln_attrs],
+        n.links.loc[n.links.carrier == "DC", lk_attrs]
+    ]).fillna(0)
+
+    positive_order = candidates.bus0 < candidates.bus1
+    candidates_p = candidates[positive_order]
+    swap_buses = {"bus0": "bus1", "bus1": "bus0"}
+    candidates_n = candidates[~positive_order].rename(columns=swap_buses)
+    candidates = pd.concat([candidates_p, candidates_n])
+    
+    def make_index(c):
+        return prefix + c.bus0 + connector + c.bus1
+
+    topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
+    topo.index = topo.apply(make_index, axis=1)
+    
+    if not bidirectional:
+        topo_reverse = topo.copy()
+        topo_reverse.rename(columns=swap_buses, inplace=True)
+        topo_reverse.index = topo_reverse.apply(make_index, axis=1)
+        topo = topo.append(topo_reverse)
+    
+    return topo
+
+
 # TODO merge issue with PyPSA-Eur
 def update_wind_solar_costs(n, costs):
     """
@@ -299,26 +430,26 @@ def add_co2_tracking(n, options):
     )
 
     # this tracks CO2 stored, e.g. underground
-    n.add("Bus",
+    n.madd("Bus",
-        "co2 stored",
+        spatial.co2.nodes,
-        location="EU",
+        location=spatial.co2.locations,
         carrier="co2 stored"
     )
 
-    n.add("Store",
+    n.madd("Store",
-        "co2 stored",
+        spatial.co2.nodes,
         e_nom_extendable=True,
-        e_nom_max=options['co2_sequestration_potential'] * 1e6,
+        e_nom_max=np.inf, 
         capital_cost=options['co2_sequestration_cost'],
         carrier="co2 stored",
-        bus="co2 stored"
+        bus=spatial.co2.nodes
     )
 
     if options['co2_vent']:
 
-        n.add("Link",
+        n.madd("Link",
-            "co2 vent",
+            spatial.co2.vents,
-            bus0="co2 stored",
+            bus0=spatial.co2.nodes,
             bus1="co2 atmosphere",
             carrier="co2 vent",
             efficiency=1.,
@@ -326,6 +457,28 @@ def add_co2_tracking(n, options):
         )
 
 
+def add_co2_network(n, costs):
+    
+    logger.info("Adding CO2 network.")
+    co2_links = create_network_topology(n, "CO2 pipeline ")
+
+    cost_onshore = (1 - co2_links.underwater_fraction) * costs.at['CO2 pipeline', 'fixed'] * co2_links.length
+    cost_submarine = co2_links.underwater_fraction * costs.at['CO2 submarine pipeline', 'fixed'] * co2_links.length
+    capital_cost = cost_onshore + cost_submarine
+    
+    n.madd("Link",
+        co2_links.index,
+        bus0=co2_links.bus0.values + " co2 stored",
+        bus1=co2_links.bus1.values + " co2 stored",
+        p_min_pu=-1,
+        p_nom_extendable=True,
+        length=co2_links.length.values,
+        capital_cost=capital_cost.values,
+        carrier="CO2 pipeline",
+        lifetime=costs.at['CO2 pipeline', 'lifetime']
+    )
+
+
 def add_dac(n, costs):
 
     heat_carriers = ["urban central heat", "services urban decentral heat"]
@@ -339,7 +492,7 @@ def add_dac(n, costs):
         locations,
         suffix=" DAC",
         bus0="co2 atmosphere",
-        bus1="co2 stored",
+        bus1=spatial.co2.df.loc[locations, "nodes"].values,
         bus2=locations.values,
         bus3=heat_buses,
         carrier="DAC",
@@ -990,10 +1143,11 @@ def add_storage(n, costs):
     if options['methanation']:
 
         n.madd("Link",
-            nodes + " Sabatier",
+            spatial.nodes,
+            suffix=" Sabatier",
             bus0=nodes + " H2",
             bus1="EU gas",
-            bus2="co2 stored",
+            bus2=spatial.co2.nodes,
             p_nom_extendable=True,
             carrier="Sabatier",
             efficiency=costs.at["methanation", "efficiency"],
@@ -1005,10 +1159,11 @@ def add_storage(n, costs):
     if options['helmeth']:
 
         n.madd("Link",
-            nodes + " helmeth",
+            spatial.nodes,
+            suffix=" helmeth",
             bus0=nodes,
             bus1="EU gas",
-            bus2="co2 stored",
+            bus2=spatial.co2.nodes,
             carrier="helmeth",
             p_nom_extendable=True,
             efficiency=costs.at["helmeth", "efficiency"],
@@ -1021,11 +1176,12 @@ def add_storage(n, costs):
     if options['SMR']:
 
         n.madd("Link",
-            nodes + " SMR CC",
+            spatial.nodes,
+            suffix=" SMR CC",
             bus0="EU gas",
             bus1=nodes + " H2",
             bus2="co2 atmosphere",
-            bus3="co2 stored",
+            bus3=spatial.co2.nodes,
             p_nom_extendable=True,
             carrier="SMR CC",
             efficiency=costs.at["SMR CC", "efficiency"],
@@ -1374,7 +1530,7 @@ def add_heat(n, costs):
                 bus1=nodes[name],
                 bus2=nodes[name] + " urban central heat",
                 bus3="co2 atmosphere",
-                bus4="co2 stored",
+                bus4=spatial.co2.df.loc[nodes[name], "nodes"].values,
                 carrier="urban central gas CHP CC",
                 p_nom_extendable=True,
                 capital_cost=costs.at['central gas CHP', 'fixed']*costs.at['central gas CHP', 'efficiency'] + costs.at['biomass CHP capture', 'fixed']*costs.at['gas', 'CO2 intensity'],
@@ -1533,8 +1689,16 @@ def add_biomass(n, costs):
 
     biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)
 
-    n.add("Carrier", "biogas")
+    if options["biomass_transport"]:
+        # potential per node distributed within country by population
+        biomass_potentials_spatial = (biomass_potentials.loc[pop_layout.ct]
+                            .set_index(pop_layout.index)
+                            .mul(pop_layout.fraction, axis="index")
+                            .rename(index=lambda x: x + " solid biomass"))
+    else:
+        biomass_potentials_spatial = biomass_potentials.sum()
 
+    n.add("Carrier", "biogas")
     n.add("Carrier", "solid biomass")
 
     n.add("Bus",
@@ -1543,9 +1707,9 @@ def add_biomass(n, costs):
         carrier="biogas"
     )
 
-    n.add("Bus",
+    n.madd("Bus",
-        "EU solid biomass",
+        spatial.biomass.nodes,
-        location="EU",
+        location=spatial.biomass.locations,
         carrier="solid biomass"
     )
 
@@ -1558,13 +1722,13 @@ def add_biomass(n, costs):
         e_initial=biomass_potentials.loc[countries, "biogas"].sum()
     )
 
-    n.add("Store",
+    n.madd("Store",
-        "EU solid biomass",
+        spatial.biomass.nodes,
-        bus="EU solid biomass",
+        bus=spatial.biomass.nodes,
         carrier="solid biomass",
-        e_nom=biomass_potentials.loc[countries, "solid biomass"].sum(),
+        e_nom=biomass_potentials_spatial["solid biomass"],
         marginal_cost=costs.at['solid biomass', 'fuel'],
-        e_initial=biomass_potentials.loc[countries, "solid biomass"].sum()
+        e_initial=biomass_potentials_spatial["solid biomass"]
     )
 
     n.add("Link",
@@ -1579,6 +1743,32 @@ def add_biomass(n, costs):
         p_nom_extendable=True
     )
 
+    if options["biomass_transport"]:
+
+        transport_costs = pd.read_csv(
+            snakemake.input.biomass_transport_costs,
+            index_col=0,
+            squeeze=True
+        )
+        
+        # add biomass transport
+        biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)
+
+        # costs
+        bus0_costs = biomass_transport.bus0.apply(lambda x: transport_costs[x[:2]])
+        bus1_costs = biomass_transport.bus1.apply(lambda x: transport_costs[x[:2]])
+        biomass_transport["costs"] = pd.concat([bus0_costs, bus1_costs], axis=1).mean(axis=1)
+
+        n.madd("Link",
+            biomass_transport.index,
+            bus0=biomass_transport.bus0 + " solid biomass",
+            bus1=biomass_transport.bus1 + " solid biomass",
+            p_nom_extendable=True,
+            length=biomass_transport.length.values,
+            marginal_cost=biomass_transport.costs * biomass_transport.length.values,
+            capital_cost=1,
+            carrier="solid biomass transport"
+        )
 
     #AC buses with district heating
     urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
@@ -1589,7 +1779,7 @@ def add_biomass(n, costs):
 
         n.madd("Link",
             urban_central + " urban central solid biomass CHP",
-            bus0="EU solid biomass",
+            bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
             bus1=urban_central,
             bus2=urban_central + " urban central heat",
             carrier="urban central solid biomass CHP",
@@ -1603,11 +1793,11 @@ def add_biomass(n, costs):
 
         n.madd("Link",
             urban_central + " urban central solid biomass CHP CC",
-            bus0="EU solid biomass",
+            bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
             bus1=urban_central,
             bus2=urban_central + " urban central heat",
             bus3="co2 atmosphere",
-            bus4="co2 stored",
+            bus4=spatial.co2.df.loc[urban_central, "nodes"].values,
             carrier="urban central solid biomass CHP CC",
             p_nom_extendable=True,
             capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'] + costs.at['biomass CHP capture', 'fixed'] * costs.at['solid biomass', 'CO2 intensity'],
@@ -1629,36 +1819,39 @@ def add_industry(n, costs):
     # 1e6 to convert TWh to MWh
     industrial_demand = pd.read_csv(snakemake.input.industrial_demand, index_col=0) * 1e6
 
-    solid_biomass_by_country = industrial_demand["solid biomass"].groupby(pop_layout.ct).sum()
+    n.madd("Bus",
-
+        spatial.biomass.industry,
-    n.add("Bus",
+        location=spatial.biomass.locations,
-        "solid biomass for industry",
-        location="EU",
         carrier="solid biomass for industry"
     )
 
-    n.add("Load",
+    if options["biomass_transport"]:
-        "solid biomass for industry",
+        p_set = industrial_demand.loc[spatial.biomass.locations, "solid biomass"].rename(index=lambda x: x + " solid biomass for industry") / 8760
-        bus="solid biomass for industry",
+    else:
+        p_set = industrial_demand["solid biomass"].sum() / 8760
+
+    n.madd("Load",
+        spatial.biomass.industry,
+        bus=spatial.biomass.industry,
         carrier="solid biomass for industry",
-        p_set=solid_biomass_by_country.sum() / 8760
+        p_set=p_set
     )
 
-    n.add("Link",
+    n.madd("Link",
-        "solid biomass for industry",
+        spatial.biomass.industry,
-        bus0="EU solid biomass",
+        bus0=spatial.biomass.nodes,
-        bus1="solid biomass for industry",
+        bus1=spatial.biomass.industry,
         carrier="solid biomass for industry",
         p_nom_extendable=True,
         efficiency=1.
     )
 
-    n.add("Link",
+    n.madd("Link",
-        "solid biomass for industry CC",
+        spatial.biomass.industry_cc,
-        bus0="EU solid biomass",
+        bus0=spatial.biomass.nodes,
-        bus1="solid biomass for industry",
+        bus1=spatial.biomass.industry,
         bus2="co2 atmosphere",
-        bus3="co2 stored",
+        bus3=spatial.co2.nodes,
         carrier="solid biomass for industry CC",
         p_nom_extendable=True,
         capital_cost=costs.at["cement capture", "fixed"] * costs.at['solid biomass', 'CO2 intensity'],
@@ -1691,12 +1884,13 @@ def add_industry(n, costs):
         efficiency2=costs.at['gas', 'CO2 intensity']
     )
 
-    n.add("Link",
+    n.madd("Link",
-        "gas for industry CC",
+        spatial.co2.locations,
+        suffix=" gas for industry CC",
         bus0="EU gas",
         bus1="gas for industry",
         bus2="co2 atmosphere",
-        bus3="co2 stored",
+        bus3=spatial.co2.nodes,
         carrier="gas for industry CC",
         p_nom_extendable=True,
         capital_cost=costs.at["cement capture", "fixed"] * costs.at['gas', 'CO2 intensity'],
@@ -1827,7 +2021,7 @@ def add_industry(n, costs):
         nodes + " Fischer-Tropsch",
         bus0=nodes + " H2",
         bus1="EU oil",
-        bus2="co2 stored",
+        bus2=spatial.co2.nodes,
         carrier="Fischer-Tropsch",
         efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
         capital_cost=costs.at["Fischer-Tropsch", 'fixed'],
@@ -1916,11 +2110,12 @@ def add_industry(n, costs):
     )
 
     #assume enough local waste heat for CC
-    n.add("Link",
+    n.madd("Link",
-        "process emissions CC",
+        spatial.co2.locations,
+        suffix=" process emissions CC",
         bus0="process emissions",
         bus1="co2 atmosphere",
-        bus2="co2 stored",
+        bus2=spatial.co2.nodes,
         carrier="process emissions CC",
         p_nom_extendable=True,
         capital_cost=costs.at["cement capture", "fixed"],
@@ -2039,6 +2234,8 @@ if __name__ == "__main__":
 
     patch_electricity_network(n)
 
+    define_spatial(pop_layout.index)
+
     if snakemake.config["foresight"] == 'myopic':
 
         add_lifetime_wind_solar(n, costs)
@@ -2061,6 +2258,8 @@ if __name__ == "__main__":
         if o[:4] == "dist":
             options['electricity_distribution_grid'] = True
             options['electricity_distribution_grid_cost_factor'] = float(o[4:].replace("p", ".").replace("m", "-"))
+        if o == "biomasstransport":
+            options["biomass_transport"] = True
 
     nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data = prepare_data(n)
 
@@ -2091,6 +2290,9 @@ if __name__ == "__main__":
     if "noH2network" in opts:
         remove_h2_network(n)
 
+    if options["co2_network"]:
+        add_co2_network(n, costs)
+
     for o in opts:
         m = re.match(r'^\d+h$', o, re.IGNORECASE)
         if m is not None:

scripts/solve_network.py

@@ -3,6 +3,7 @@
 import pypsa
 
 import numpy as np
+import pandas as pd
 
 from pypsa.linopt import get_var, linexpr, define_constraints
 
@@ -150,8 +151,26 @@ def add_chp_constraints(n):
         define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
 
 
+def add_co2_sequestration_limit(n, sns):
+    
+    co2_stores = n.stores.loc[n.stores.carrier=='co2 stored'].index
+
+    if co2_stores.empty or ('Store', 'e') not in n.variables.index:
+        return
+    
+    vars_final_co2_stored = get_var(n, 'Store', 'e').loc[sns[-1], co2_stores]
+    
+    lhs = linexpr((1, vars_final_co2_stored)).sum()
+    rhs = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6
+    
+    name = 'co2_sequestration_limit'
+    define_constraints(n, lhs, "<=", rhs, 'GlobalConstraint',
+                       'mu', axes=pd.Index([name]), spec=name)
+
+
 def extra_functionality(n, snapshots):
     add_battery_constraints(n)
+    add_co2_sequestration_limit(n, snapshots)
 
 
 def solve_network(n, config, opts='', **kwargs):