new script to interpolate industry sector ratios today to tomorrow

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 in the
industry_sector_ratios.csv.

This is done on a per-country basis. The ratio of today to tomorrow's
energy consumption is set with the
config["industry"]["sector_ratios_fraction_future"] parameter.

config/config.default.yaml

@@ -636,6 +636,14 @@ 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

rules/build_sector.smk

@@ -433,6 +433,30 @@ 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"],
@@ -535,7 +559,7 @@ 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

scripts/build_industrial_energy_demand_per_node.py

@@ -19,23 +19,28 @@ 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_industry_sector_ratios_intermediate.py

@@ -0,0 +1,77 @@
+# -*- 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
+    production = production.unstack().swaplevel()
+
+    # in MWh/t
+    future_sector_ratios = pd.read_csv(snakemake.input.industry_sector_ratios,
+                                       index_col=0)
+
+    production.index.names = [None,None]
+
+    today_sector_ratios = demand.div(production, axis=1)
+
+    today_sector_ratios.drop(columns=today_sector_ratios.columns[today_sector_ratios.isna().all()],
+                             inplace=True)
+
+    rename = pd.Series(today_sector_ratios.index,
+                       today_sector_ratios.index)
+    rename["waste"] = "biomass"
+    rename["electricity"] = "elec"
+    rename["solid"] = "coke"
+    rename["gas"] = "methane"
+    rename["other"] = "biomass"
+    rename["liquid"] = "naphtha"
+
+    today_sector_ratios.rename(rename,
+                               inplace=True)
+
+
+    fraction_future = get(params["sector_ratios_fraction_future"], year)
+
+    intermediate_sector_ratios = {}
+
+    for ct in today_sector_ratios.columns.unique(level=0):
+
+        intermediate_sector_ratio = future_sector_ratios.copy()
+
+        intermediate_sector_ratio.loc[today_sector_ratios[ct].index,today_sector_ratios[ct].columns] = (fraction_future*intermediate_sector_ratio.loc[today_sector_ratios[ct].index,today_sector_ratios[ct].columns]
+                                                                                                        + (1 - fraction_future)*today_sector_ratios[ct])
+        intermediate_sector_ratios[ct] = intermediate_sector_ratio
+
+    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")
+
+    year = int(snakemake.wildcards.planning_horizons[-4:])
+
+    params = snakemake.params.industry
+
+    build_industry_sector_ratios_intermediate()