Merge pull request #929 from PyPSA/industry-pathway

Industry pathway

config/config.default.yaml

@@ -637,6 +637,15 @@ industry:
     2040: 0.12
     2045: 0.16
     2050: 0.20
+  sector_ratios_fraction_future:
+    2020: 0.0
+    2025: 0.1
+    2030: 0.3
+    2035: 0.5
+    2040: 0.7
+    2045: 0.9
+    2050: 1.0
+  basic_chemicals_without_NH3_production_today: 69. #Mt/a, = 86 Mtethylene-equiv - 17 MtNH3
   HVC_production_today: 52.
   MWh_elec_per_tHVC_mechanical_recycling: 0.547
   MWh_elec_per_tHVC_chemical_recycling: 6.9

doc/configtables/industry.csv

@@ -17,6 +17,8 @@ HVC_primary_fraction,--,float,The fraction of high value chemicals (HVC) produce
 HVC_mechanical_recycling _fraction,--,float,The fraction of high value chemicals (HVC) produced using mechanical recycling
 HVC_chemical_recycling _fraction,--,float,The fraction of high value chemicals (HVC) produced using chemical recycling
 ,,,
+sector_ratios_fraction_future,--,Dictionary with planning horizons as keys.,The fraction of total progress in fuel and process switching achieved in the industry sector.
+basic_chemicals_without_NH3_production_today,Mt/a,float,"The amount of basic chemicals produced without ammonia (= 86 Mtethylene-equiv - 17 MtNH3)."
 HVC_production_today,MtHVC/a,float,"The amount of high value chemicals (HVC) produced. This includes ethylene, propylene and BTX. From `DECHEMA (2017) <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf>`_, Figure 16, page 107"
 Mwh_elec_per_tHVC _mechanical_recycling,MWh/tHVC,float,"The energy amount of electricity needed to produce a ton of high value chemical (HVC) using mechanical recycling. From SI of `Meys et al (2020) <https://doi.org/10.1016/j.resconrec.2020.105010>`_, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756."
 Mwh_elec_per_tHVC _chemical_recycling,MWh/tHVC,float,"The energy amount of electricity needed to produce a ton of high value chemical (HVC) using chemical recycling. The default value is based on pyrolysis and electric steam cracking. From `Material Economics (2019) <https://materialeconomics.com/latest-updates/industrial-transformation-2050>`_, page 125"

doc/release_notes.rst

@@ -10,6 +10,15 @@ Release Notes
 Upcoming Release
 ================
 
+* Improved representation of industry transition pathways. A new script was
+  added to interpolate industry sector ratios from today's status quo to future
+  systems (i.e. specific emissions and demands for energy and feedstocks). For
+  each country we gradually switch industry processes from today's specific
+  energy carrier usage per ton material output to the best-in-class energy
+  consumption of tomorrow. This is done on a per-country basis. The ratio of
+  today to tomorrow's energy consumption is set with the ``industry:
+  sector_ratios_fraction_future:`` parameter.
+
 * Bugfix: Correct units of subtracted chlorine and methanol demand in
   :mod:`build_industry_sector_ratios`.
 

rules/build_sector.smk

@@ -431,6 +431,31 @@ rule build_industry_sector_ratios:
         "../scripts/build_industry_sector_ratios.py"
 
 
+rule build_industry_sector_ratios_intermediate:
+    params:
+        industry=config["industry"],
+    input:
+        industry_sector_ratios=RESOURCES + "industry_sector_ratios.csv",
+        industrial_energy_demand_per_country_today=RESOURCES
+        + "industrial_energy_demand_per_country_today.csv",
+        industrial_production_per_country=RESOURCES
+        + "industrial_production_per_country.csv",
+    output:
+        industry_sector_ratios=RESOURCES
+        + "industry_sector_ratios_{planning_horizons}.csv",
+    threads: 1
+    resources:
+        mem_mb=1000,
+    log:
+        LOGS + "build_industry_sector_ratios_{planning_horizons}.log",
+    benchmark:
+        BENCHMARKS + "build_industry_sector_ratios_{planning_horizons}"
+    conda:
+        "../envs/environment.yaml"
+    script:
+        "../scripts/build_industry_sector_ratios_intermediate.py"
+
+
 rule build_industrial_production_per_country:
     params:
         industry=config["industry"],
@@ -533,7 +558,8 @@ rule build_industrial_production_per_node:
 
 rule build_industrial_energy_demand_per_node:
     input:
-        industry_sector_ratios=RESOURCES + "industry_sector_ratios.csv",
+        industry_sector_ratios=RESOURCES
+        + "industry_sector_ratios_{planning_horizons}.csv",
         industrial_production_per_node=RESOURCES
         + "industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
         industrial_energy_demand_per_node_today=RESOURCES
@@ -564,7 +590,6 @@ rule build_industrial_energy_demand_per_country_today:
         industry=config["industry"],
     input:
         jrc="data/bundle-sector/jrc-idees-2015",
-        ammonia_production=RESOURCES + "ammonia_production.csv",
         industrial_production_per_country=RESOURCES
         + "industrial_production_per_country.csv",
     output:

scripts/build_industrial_energy_demand_per_country_today.py

@@ -73,7 +73,7 @@ def industrial_energy_demand_per_country(country, year, jrc_dir):
     def get_subsector_data(sheet):
         df = df_dict[sheet][year].groupby(fuels).sum()
 
-        df["ammonia"] = 0.0
+        df["hydrogen"] = 0.0
 
         df["other"] = df["all"] - df.loc[df.index != "all"].sum()
 
@@ -94,51 +94,50 @@ def industrial_energy_demand_per_country(country, year, jrc_dir):
     return df
 
 
-def add_ammonia_energy_demand(demand):
-    # MtNH3/a
-    fn = snakemake.input.ammonia_production
-    ammonia = pd.read_csv(fn, index_col=0)[str(year)] / 1e3
+def separate_basic_chemicals(demand, production):
 
-    def get_ammonia_by_fuel(x):
-        fuels = {
-            "gas": params["MWh_CH4_per_tNH3_SMR"],
-            "electricity": params["MWh_elec_per_tNH3_SMR"],
+    ammonia = pd.DataFrame(
+        {
+            "hydrogen": production["Ammonia"] * params["MWh_H2_per_tNH3_electrolysis"],
+            "electricity": production["Ammonia"]
+            * params["MWh_elec_per_tNH3_electrolysis"],
         }
-
-        return pd.Series({k: x * v for k, v in fuels.items()})
-
-    ammonia_by_fuel = ammonia.apply(get_ammonia_by_fuel).T
-    ammonia_by_fuel = ammonia_by_fuel.unstack().reindex(
-        index=demand.index, fill_value=0.0
-    )
-
-    ammonia = pd.DataFrame({"ammonia": ammonia * params["MWh_NH3_per_tNH3"]}).T
+    ).T
+    chlorine = pd.DataFrame(
+        {
+            "hydrogen": production["Chlorine"] * params["MWh_H2_per_tCl"],
+            "electricity": production["Chlorine"] * params["MWh_elec_per_tCl"],
+        }
+    ).T
+    methanol = pd.DataFrame(
+        {
+            "gas": production["Methanol"] * params["MWh_CH4_per_tMeOH"],
+            "electricity": production["Methanol"] * params["MWh_elec_per_tMeOH"],
+        }
+    ).T
 
     demand["Ammonia"] = ammonia.unstack().reindex(index=demand.index, fill_value=0.0)
+    demand["Chlorine"] = chlorine.unstack().reindex(index=demand.index, fill_value=0.0)
+    demand["Methanol"] = methanol.unstack().reindex(index=demand.index, fill_value=0.0)
 
-    demand["Basic chemicals (without ammonia)"] = (
-        demand["Basic chemicals"] - ammonia_by_fuel
+    demand["HVC"] = (
+        demand["Basic chemicals"]
+        - demand["Ammonia"]
+        - demand["Methanol"]
+        - demand["Chlorine"]
     )
 
-    demand["Basic chemicals (without ammonia)"].clip(lower=0, inplace=True)
-
     demand.drop(columns="Basic chemicals", inplace=True)
 
+    demand["HVC"].clip(lower=0, inplace=True)
+
     return demand
 
 
-def add_non_eu28_industrial_energy_demand(countries, demand):
+def add_non_eu28_industrial_energy_demand(countries, demand, production):
     non_eu28 = countries.difference(eu28)
     if non_eu28.empty:
         return demand
-    # output in MtMaterial/a
-    fn = snakemake.input.industrial_production_per_country
-    production = pd.read_csv(fn, index_col=0) / 1e3
-
-    # recombine HVC, Chlorine and Methanol to Basic chemicals (without ammonia)
-    chemicals = ["HVC", "Chlorine", "Methanol"]
-    production["Basic chemicals (without ammonia)"] = production[chemicals].sum(axis=1)
-    production.drop(columns=chemicals, inplace=True)
 
     eu28_production = production.loc[countries.intersection(eu28)].sum()
     eu28_energy = demand.groupby(level=1).sum()
@@ -182,9 +181,15 @@ if __name__ == "__main__":
 
     demand = industrial_energy_demand(countries.intersection(eu28), year)
 
-    demand = add_ammonia_energy_demand(demand)
+    # output in MtMaterial/a
+    production = (
+        pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
+        / 1e3
+    )
 
-    demand = add_non_eu28_industrial_energy_demand(countries, demand)
+    demand = separate_basic_chemicals(demand, production)
+
+    demand = add_non_eu28_industrial_energy_demand(countries, demand, production)
 
     # for format compatibility
     demand = demand.stack(dropna=False).unstack(level=[0, 2])

scripts/build_industrial_energy_demand_per_node.py

@@ -19,23 +19,31 @@ if __name__ == "__main__":
             planning_horizons=2030,
         )
 
-    # import EU ratios df as csv
+    # import ratios
     fn = snakemake.input.industry_sector_ratios
-    industry_sector_ratios = pd.read_csv(fn, index_col=0)
+    sector_ratios = pd.read_csv(fn, header=[0, 1], index_col=0)
 
-    # material demand per node and industry (kton/a)
+    # material demand per node and industry (Mton/a)
     fn = snakemake.input.industrial_production_per_node
-    nodal_production = pd.read_csv(fn, index_col=0)
+    nodal_production = pd.read_csv(fn, index_col=0) / 1e3
 
     # energy demand today to get current electricity
     fn = snakemake.input.industrial_energy_demand_per_node_today
     nodal_today = pd.read_csv(fn, index_col=0)
 
-    # final energy consumption per node and industry (TWh/a)
-    nodal_df = nodal_production.dot(industry_sector_ratios.T)
+    nodal_sector_ratios = pd.concat(
+        {node: sector_ratios[node[:2]] for node in nodal_production.index}, axis=1
+    )
 
-    # convert GWh to TWh and ktCO2 to MtCO2
-    nodal_df *= 0.001
+    nodal_production_stacked = nodal_production.stack()
+    nodal_production_stacked.index.names = [None, None]
+
+    # final energy consumption per node and industry (TWh/a)
+    nodal_df = (
+        (nodal_sector_ratios.multiply(nodal_production_stacked))
+        .T.groupby(level=0)
+        .sum()
+    )
 
     rename_sectors = {
         "elec": "electricity",

scripts/build_industrial_production_per_country.py

@@ -261,7 +261,11 @@ def separate_basic_chemicals(demand, year):
     demand["Basic chemicals"].clip(lower=0.0, inplace=True)
 
     # assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
-    distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
+    distribution_key = (
+        demand["Basic chemicals"]
+        / params["basic_chemicals_without_NH3_production_today"]
+        / 1e3
+    )
     demand["HVC"] = params["HVC_production_today"] * 1e3 * distribution_key
     demand["Chlorine"] = params["chlorine_production_today"] * 1e3 * distribution_key
     demand["Methanol"] = params["methanol_production_today"] * 1e3 * distribution_key

scripts/build_industry_sector_ratios_intermediate.py

@@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
+#
+# SPDX-License-Identifier: MIT
+"""
+Build specific energy consumption by carrier and industries and by country,
+that interpolates between the current average energy consumption (from
+2015-2020) and the ideal future best-in-class consumption.
+"""
+
+import pandas as pd
+from prepare_sector_network import get
+
+
+def build_industry_sector_ratios_intermediate():
+
+    # in TWh/a
+    demand = pd.read_csv(
+        snakemake.input.industrial_energy_demand_per_country_today,
+        header=[0, 1],
+        index_col=0,
+    )
+
+    # in Mt/a
+    production = (
+        pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
+        / 1e3
+    ).stack()
+    production.index.names = [None, None]
+
+    # in MWh/t
+    future_sector_ratios = pd.read_csv(
+        snakemake.input.industry_sector_ratios, index_col=0
+    )
+
+    today_sector_ratios = demand.div(production, axis=1)
+
+    today_sector_ratios.dropna(how="all", axis=1, inplace=True)
+
+    rename = {
+        "waste": "biomass",
+        "electricity": "elec",
+        "solid": "coke",
+        "gas": "methane",
+        "other": "biomass",
+        "liquid": "naphtha",
+    }
+    today_sector_ratios = today_sector_ratios.rename(rename).groupby(level=0).sum()
+
+    fraction_future = get(params["sector_ratios_fraction_future"], year)
+
+    intermediate_sector_ratios = {}
+    for ct, group in today_sector_ratios.T.groupby(level=0):
+        today_sector_ratios_ct = (
+            group.droplevel(0)
+            .T.reindex_like(future_sector_ratios)
+            .fillna(future_sector_ratios)
+        )
+        intermediate_sector_ratios[ct] = (
+            today_sector_ratios_ct * (1 - fraction_future)
+            + future_sector_ratios * fraction_future
+        )
+    intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1)
+
+    intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios)
+
+
+if __name__ == "__main__":
+    if "snakemake" not in globals():
+        from _helpers import mock_snakemake
+
+        snakemake = mock_snakemake(
+            "build_industry_sector_ratios_intermediate",
+            planning_horizons="2030",
+        )
+
+    year = int(snakemake.wildcards.planning_horizons[-4:])
+
+    params = snakemake.params.industry
+
+    build_industry_sector_ratios_intermediate()