Merge pull request #835 from openego/features/#817-add-new-industrial…

…-electricity-demand

Add new industrial electricity demands for eGon100RE

CHANGELOG.rst

@@ -383,6 +383,8 @@ Changed
   `#797 <https://github.com/openego/eGon-data/issues/797>`_
 * Add coordinates to non AC buses abroad in eGon100RE
   `#803 <https://github.com/openego/eGon-data/issues/803>`_
+* Integrate additional industrial electricity demands for eGon100RE
+  `#817 <https://github.com/openego/eGon-data/issues/817>`_
 
 Bug Fixes
 ---------

src/egon/data/datasets.yml

@@ -136,6 +136,10 @@ demandregio_cts_ind_demand:
       schema: 'boundaries'
       table: 'vg250_krs'
     new_consumers_2035: 'new_largescale_consumers_nep.csv'
+    new_consumers_2050:
+      "pes-demand-today": "industrial_energy_demand_per_country_today.csv"
+      "pes-production-tomorrow": "industrial_production_per_country_tomorrow_2050.csv"
+      "pes-sector-ratios": "industry_sector_ratios.csv"
     wz_definitions:
       "CTS": 'CTS_WZ_definition.csv'
       "industry": 'ind_WZ_definition.csv'

src/egon/data/datasets/demandregio/__init__.py

@@ -23,7 +23,7 @@
     from disaggregator import data, spatial, config
 
 except ImportError as e:
-   pass
+    pass
 
 # will be later imported from another file ###
 Base = declarative_base()
@@ -33,7 +33,7 @@ class DemandRegio(Dataset):
     def __init__(self, dependencies):
         super().__init__(
             name="DemandRegio",
-            version="0.0.3",
+            version="0.0.4",
             dependencies=dependencies,
             tasks=(
                 clone_and_install,
@@ -124,7 +124,7 @@ def create_tables():
 
 
 def data_in_boundaries(df):
-    """ Select rows with nuts3 code within boundaries, used for testmode
+    """Select rows with nuts3 code within boundaries, used for testmode
 
     Parameters
     ----------
@@ -160,7 +160,7 @@ def data_in_boundaries(df):
 
 
 def insert_cts_ind_wz_definitions():
-    """ Insert demandregio's definitions of CTS and industrial branches
+    """Insert demandregio's definitions of CTS and industrial branches
 
     Returns
     -------
@@ -206,7 +206,7 @@ def insert_cts_ind_wz_definitions():
 
 
 def match_nuts3_bl():
-    """ Function that maps the federal state to each nuts3 region
+    """Function that maps the federal state to each nuts3 region
 
     Returns
     -------
@@ -233,8 +233,129 @@ def match_nuts3_bl():
     return df.set_index("nuts")
 
 
+def adjust_ind_pes(ec_cts_ind):
+    """
+    Adjust electricity demand of industrial consumers due to electrification
+    of process heat based on assumptions of pypsa-eur-sec.
+
+    Parameters
+    ----------
+    ec_cts_ind : pandas.DataFrame
+        Industrial demand without additional electrification
+
+    Returns
+    -------
+    ec_cts_ind : pandas.DataFrame
+        Industrial demand with additional electrification
+
+    """
+
+    pes_path = (
+        Path(".") / "data_bundle_egon_data" / "pypsa_eur_sec" / "resources"
+    )
+
+    sources = egon.data.config.datasets()["demandregio_cts_ind_demand"][
+        "sources"
+    ]["new_consumers_2050"]
+
+    # Extract today's industrial demand from pypsa-eur-sec
+    demand_today = pd.read_csv(
+        pes_path / sources["pes-demand-today"],
+        header=None,
+    ).transpose()
+
+    # Filter data
+    demand_today[1].fillna("carrier", inplace=True)
+    demand_today = demand_today[
+        (demand_today[0] == "DE") | (demand_today[1] == "carrier")
+    ].drop([0, 2], axis="columns")
+
+    demand_today = (
+        demand_today.transpose()
+        .set_index(0)
+        .transpose()
+        .set_index("carrier")
+        .transpose()
+        .loc["electricity"]
+        .astype(float)
+    )
+
+    # Calculate future industrial demand from pypsa-eur-sec
+    # based on production and energy demands per carrier ('sector ratios')
+    prod_tomorrow = pd.read_csv(pes_path / sources["pes-production-tomorrow"])
+
+    prod_tomorrow = prod_tomorrow[prod_tomorrow["kton/a"] == "DE"].set_index(
+        "kton/a"
+    )
+
+    sector_ratio = (
+        pd.read_csv(pes_path / sources["pes-sector-ratios"])
+        .set_index("MWh/tMaterial")
+        .loc["elec"]
+    )
+
+    demand_tomorrow = prod_tomorrow.multiply(
+        sector_ratio.div(1000)
+    ).transpose()["DE"]
+
+    # Calculate changes of electrical demand per sector in pypsa-eur-sec
+    change = pd.DataFrame(
+        (demand_tomorrow / demand_today)
+        / (demand_tomorrow / demand_today).sum()
+    )
+
+    # Drop rows without changes
+    change = change[~change[0].isnull()]
+
+    # Map industrial branches of pypsa-eur-sec to WZ2008 used in demandregio
+    change["wz"] = change.index.map(
+        {
+            "Alumina production": 24,
+            "Aluminium - primary production": 24,
+            "Aluminium - secondary production": 24,
+            "Ammonia": 20,
+            "Basic chemicals (without ammonia)": 20,
+            "Cement": 23,
+            "Ceramics & other NMM": 23,
+            "Electric arc": 24,
+            "Food, beverages and tobacco": 10,
+            "Glass production": 23,
+            "Integrated steelworks": 24,
+            "Machinery Equipment": 28,
+            "Other Industrial Sectors": 32,
+            "Other chemicals": 20,
+            "Other non-ferrous metals": 24,
+            "Paper production": 17,
+            "Pharmaceutical products etc.": 21,
+            "Printing and media reproduction": 18,
+            "Pulp production": 17,
+            "Textiles and leather": 13,
+            "Transport Equipment": 29,
+            "Wood and wood products": 16,
+        }
+    )
+
+    # Group by WZ2008
+    shares_per_wz = change.groupby("wz")[0].sum()
+
+    # Calculate addtional demands needed to meet future demand of pypsa-eur-sec
+    addtional_mwh = shares_per_wz.multiply(
+        demand_tomorrow.sum() * 1000000 - ec_cts_ind.sum().sum()
+    )
+
+    # Calulate overall industrial demand for eGon100RE
+    final_mwh = addtional_mwh + ec_cts_ind[addtional_mwh.index].sum()
+
+    # Linear scale the industrial demands per nuts3 and wz to meet final demand
+    ec_cts_ind[addtional_mwh.index] *= (
+        final_mwh / ec_cts_ind[addtional_mwh.index].sum()
+    )
+
+    return ec_cts_ind
+
+
 def adjust_cts_ind_nep(ec_cts_ind, sector):
-    """ Add electrical demand of new largescale CTS und industrial consumers
+    """Add electrical demand of new largescale CTS und industrial consumers
     according to NEP 2021, scneario C 2035. Values per federal state are
     linear distributed over all CTS branches and nuts3 regions.
 
@@ -351,7 +472,7 @@ def disagg_households_power(
 
 
 def insert_hh_demand(scenario, year, engine):
-    """ Calculates electrical demands of private households using demandregio's
+    """Calculates electrical demands of private households using demandregio's
     disaggregator and insert results into the database.
 
     Parameters
@@ -391,7 +512,7 @@ def insert_hh_demand(scenario, year, engine):
 
 
 def insert_cts_ind(scenario, year, engine, target_values):
-    """ Calculates electrical demands of CTS and industry using demandregio's
+    """Calculates electrical demands of CTS and industry using demandregio's
     disaggregator, adjusts them according to resulting values of NEP 2021 or
     JRC IDEES and insert results into the database.
 
@@ -434,6 +555,9 @@ def insert_cts_ind(scenario, year, engine, target_values):
         # include new largescale consumers according to NEP 2021
         if scenario == "eGon2035":
             ec_cts_ind = adjust_cts_ind_nep(ec_cts_ind, sector)
+        # include new industrial demands due to sector coupling
+        if (scenario == "eGon100RE") & (sector == "industry"):
+            ec_cts_ind = adjust_ind_pes(ec_cts_ind)
 
         # Select demands for nuts3-regions in boundaries (needed for testmode)
         ec_cts_ind = data_in_boundaries(ec_cts_ind)
@@ -455,7 +579,7 @@ def insert_cts_ind(scenario, year, engine, target_values):
 
 
 def insert_household_demand():
-    """ Insert electrical demands for households according to
+    """Insert electrical demands for households according to
     demandregio using its disaggregator-tool in MWh
 
     Returns
@@ -482,7 +606,7 @@ def insert_household_demand():
 
 
 def insert_cts_ind_demands():
-    """ Insert electricity demands per nuts3-region in Germany according to
+    """Insert electricity demands per nuts3-region in Germany according to
     demandregio using its disaggregator-tool in MWh
 
     Returns
@@ -527,7 +651,7 @@ def insert_cts_ind_demands():
 
 
 def insert_society_data():
-    """ Insert population and number of households per nuts3-region in Germany
+    """Insert population and number of households per nuts3-region in Germany
     according to demandregio using its disaggregator-tool
 
     Returns
@@ -578,7 +702,7 @@ def insert_society_data():
 
 
 def insert_timeseries_per_wz(sector, year):
-    """ Insert normalized electrical load time series for the selected sector
+    """Insert normalized electrical load time series for the selected sector
 
     Parameters
     ----------
@@ -644,7 +768,7 @@ def insert_timeseries_per_wz(sector, year):
 
 
 def timeseries_per_wz():
-    """ Calcultae and insert normalized timeseries per wz for cts and industry
+    """Calcultae and insert normalized timeseries per wz for cts and industry
 
     Returns
     -------