data.datasets.DSM_cts_ind

Complexity

Total Complexity

53

Size/Duplication

Total Lines	1789
Duplicated Lines	11.29 %

Importance

Changes

0

19 Functions

Rating	Name	Duplication	Size	Complexity
A	ind_sites_vent_data_import()	35	35	2
A	ind_sites_vent_data_import_individual()	35	35	2
A	delete_dsm_entries()	0	65	1
A	dsm_cts_ind_processing()	0	6	1
A	create_table()	0	11	1
B	data_export()	0	110	1
A	ind_sites_data_import()	0	21	1
C	calculate_potentials()	0	91	9
A	div_list()	0	2	1
A	ind_osm_data_import_individual()	34	34	2
A	cts_data_import()	0	40	2
A	ind_osm_data_import()	32	34	2
B	dsm_cts_ind()	0	296	1
A	relate_to_schmidt_sites()	0	25	1
D	create_dsm_components()	0	169	9
A	calc_ind_site_timeseries()	0	71	2
B	dsm_cts_ind_individual()	0	247	1
C	aggregate_components()	0	88	11
B	add_metadata_individual()	0	186	2

"""
Currently, there are differences in the aggregated and individual DSM time
series. These are caused by the truncation of the values at zero.

The sum of the individual time series is a more accurate value than the
aggregated time series used so far and should replace it in the future. Since
the deviations are relatively small, a tolerance is currently accepted in the
sanity checks. See [#1120](https://github.com/openego/eGon-data/issues/1120)
for updates.
"""
import datetime
import json

from omi.dialects import get_dialect
from sqlalchemy import ARRAY, Column, Float, Integer, String
from sqlalchemy.ext.declarative import declarative_base
import geopandas as gpd
import numpy as np
import pandas as pd

from egon.data import config, db
from egon.data.datasets import Dataset
from egon.data.datasets.electricity_demand.temporal import calc_load_curve
from egon.data.datasets.industry.temporal import identify_bus
from egon.data.metadata import (
    context,
    contributors,
    generate_resource_fields_from_db_table,
    license_odbl,
    meta_metadata,
    oep_metadata_version,
    sources,
)

# CONSTANTS
# TODO: move to datasets.yml
CON = db.engine()

# CTS
CTS_COOL_VENT_AC_SHARE = 0.22

S_FLEX_CTS = 0.5
S_UTIL_CTS = 0.67
S_INC_CTS = 1
S_DEC_CTS = 0
DELTA_T_CTS = 1

# industry
IND_VENT_COOL_SHARE = 0.039
IND_VENT_SHARE = 0.017

# OSM
S_FLEX_OSM = 0.5
S_UTIL_OSM = 0.73
S_INC_OSM = 0.9
S_DEC_OSM = 0.5
DELTA_T_OSM = 1

# paper
S_FLEX_PAPER = 0.15
S_UTIL_PAPER = 0.86
S_INC_PAPER = 0.95
S_DEC_PAPER = 0
DELTA_T_PAPER = 3

# recycled paper
S_FLEX_RECYCLED_PAPER = 0.7
S_UTIL_RECYCLED_PAPER = 0.85
S_INC_RECYCLED_PAPER = 0.95
S_DEC_RECYCLED_PAPER = 0
DELTA_T_RECYCLED_PAPER = 3

# pulp
S_FLEX_PULP = 0.7
S_UTIL_PULP = 0.83
S_INC_PULP = 0.95
S_DEC_PULP = 0
DELTA_T_PULP = 2

# cement
S_FLEX_CEMENT = 0.61
S_UTIL_CEMENT = 0.65
S_INC_CEMENT = 0.95
S_DEC_CEMENT = 0
DELTA_T_CEMENT = 4

# wz 23
WZ = 23

S_FLEX_WZ = 0.5
S_UTIL_WZ = 0.8
S_INC_WZ = 1
S_DEC_WZ = 0.5
DELTA_T_WZ = 1

Base = declarative_base()


class DsmPotential(Dataset):
    def __init__(self, dependencies):
        super().__init__(
            name="DsmPotential",
            version="0.0.5",
            dependencies=dependencies,
            tasks=(dsm_cts_ind_processing,),
        )


# Datasets
class EgonEtragoElectricityCtsDsmTimeseries(Base):

This code seems to be duplicated in your project.

    target = config.datasets()["DSM_CTS_industry"]["targets"][
        "cts_loadcurves_dsm"
    ]

    __tablename__ = target["table"]
    __table_args__ = {"schema": target["schema"]}

    bus = Column(Integer, primary_key=True, index=True)
    scn_name = Column(String, primary_key=True, index=True)
    p_set = Column(ARRAY(Float))
    p_max = Column(ARRAY(Float))
    p_min = Column(ARRAY(Float))
    e_max = Column(ARRAY(Float))
    e_min = Column(ARRAY(Float))


class EgonOsmIndLoadCurvesIndividualDsmTimeseries(Base):

This code seems to be duplicated in your project.

    target = config.datasets()["DSM_CTS_industry"]["targets"][
        "ind_osm_loadcurves_individual_dsm"
    ]

    __tablename__ = target["table"]
    __table_args__ = {"schema": target["schema"]}

    osm_id = Column(Integer, primary_key=True, index=True)
    scn_name = Column(String, primary_key=True, index=True)
    bus = Column(Integer)
    p_set = Column(ARRAY(Float))
    p_max = Column(ARRAY(Float))
    p_min = Column(ARRAY(Float))
    e_max = Column(ARRAY(Float))
    e_min = Column(ARRAY(Float))


class EgonDemandregioSitesIndElectricityDsmTimeseries(Base):

This code seems to be duplicated in your project.

    target = config.datasets()["DSM_CTS_industry"]["targets"][
        "demandregio_ind_sites_dsm"
    ]

    __tablename__ = target["table"]
    __table_args__ = {"schema": target["schema"]}

    industrial_sites_id = Column(Integer, primary_key=True, index=True)
    scn_name = Column(String, primary_key=True, index=True)
    bus = Column(Integer)
    application = Column(String)
    p_set = Column(ARRAY(Float))
    p_max = Column(ARRAY(Float))
    p_min = Column(ARRAY(Float))
    e_max = Column(ARRAY(Float))
    e_min = Column(ARRAY(Float))


class EgonSitesIndLoadCurvesIndividualDsmTimeseries(Base):

This code seems to be duplicated in your project.

    target = config.datasets()["DSM_CTS_industry"]["targets"][
        "ind_sites_loadcurves_individual"
    ]

    __tablename__ = target["table"]
    __table_args__ = {"schema": target["schema"]}

    site_id = Column(Integer, primary_key=True, index=True)
    scn_name = Column(String, primary_key=True, index=True)
    bus = Column(Integer)
    p_set = Column(ARRAY(Float))
    p_max = Column(ARRAY(Float))
    p_min = Column(ARRAY(Float))
    e_max = Column(ARRAY(Float))
    e_min = Column(ARRAY(Float))


def add_metadata_individual():
    targets = config.datasets()["DSM_CTS_industry"]["targets"]

    targets = {
        k: v for k, v in targets.items() if "dsm_timeseries" in v["table"]
    }

    title_dict = {
        "egon_etrago_electricity_cts_dsm_timeseries": (
            "DSM flexibility band time series for CTS"
        ),
        "egon_osm_ind_load_curves_individual_dsm_timeseries": (
            "DSM flexibility band time series for OSM industry sites"
        ),
        "egon_demandregio_sites_ind_electricity_dsm_timeseries": (
            "DSM flexibility band time series for demandregio industry sites"
        ),
        "egon_sites_ind_load_curves_individual_dsm_timeseries": (
            "DSM flexibility band time series for other industry sites"
        ),
    }

    description_dict = {
        "egon_etrago_electricity_cts_dsm_timeseries": (
            "DSM flexibility band time series for CTS in 1 h resolution "
            "including available store capacity and power potential"
        ),
        "egon_osm_ind_load_curves_individual_dsm_timeseries": (
            "DSM flexibility band time series for OSM industry sites in 1 h "
            "resolution including available store capacity and power potential"
        ),
        "egon_demandregio_sites_ind_electricity_dsm_timeseries": (
            "DSM flexibility band time series for demandregio industry sites "
            "in 1 h resolution including available store capacity and power "
            "potential"
        ),
        "egon_sites_ind_load_curves_individual_dsm_timeseries": (
            "DSM flexibility band time series for other industry sites in 1 h "
            "resolution including available store capacity and power potential"
        ),
    }

    keywords_dict = {
        "egon_etrago_electricity_cts_dsm_timeseries": ["cts"],
        "egon_osm_ind_load_curves_individual_dsm_timeseries": [
            "osm",
            "industry",
        ],
        "egon_demandregio_sites_ind_electricity_dsm_timeseries": [
            "demandregio",
            "industry",
        ],
        "egon_sites_ind_load_curves_individual_dsm_timeseries": ["industry"],
    }

    primaryKey_dict = {
        "egon_etrago_electricity_cts_dsm_timeseries": ["bus"],
        "egon_osm_ind_load_curves_individual_dsm_timeseries": ["osm_id"],
        "egon_demandregio_sites_ind_electricity_dsm_timeseries": [
            "industrial_sites_id",
        ],
        "egon_sites_ind_load_curves_individual_dsm_timeseries": ["site_id"],
    }

    sources_dict = {
        "egon_etrago_electricity_cts_dsm_timeseries": [
            sources()["nep2021"],
            sources()["zensus"],
        ],
        "egon_osm_ind_load_curves_individual_dsm_timeseries": [
            sources()["hotmaps_industrial_sites"],
            sources()["schmidt"],
            sources()["seenergies"],
        ],
        "egon_demandregio_sites_ind_electricity_dsm_timeseries": [
            sources()["openstreetmap"],
        ],
        "egon_sites_ind_load_curves_individual_dsm_timeseries": [
            sources()["hotmaps_industrial_sites"],
            sources()["openstreetmap"],
            sources()["schmidt"],
            sources()["seenergies"],
        ],
    }

    contris = contributors(["kh", "kh"])

    contris[0]["date"] = "2023-03-17"

    contris[0]["object"] = "metadata"
    contris[1]["object"] = "dataset"

    contris[0]["comment"] = "Add metadata to dataset."
    contris[1]["comment"] = "Add workflow to generate dataset."

    for t_dict in targets.values():
        schema = t_dict["schema"]
        table = t_dict["table"]
        name = f"{schema}.{table}"

        meta = {
            "name": name,
            "title": title_dict[table],
            "id": "WILL_BE_SET_AT_PUBLICATION",
            "description": description_dict[table],
            "language": "en-US",
            "keywords": ["dsm", "timeseries"] + keywords_dict[table],
            "publicationDate": datetime.date.today().isoformat(),
            "context": context(),
            "spatial": {
                "location": "none",
                "extent": "Germany",
                "resolution": "none",
            },
            "temporal": {
                "referenceDate": "2011-01-01",
                "timeseries": {
                    "start": "2011-01-01",
                    "end": "2011-12-31",
                    "resolution": "1 h",
                    "alignment": "left",
                    "aggregationType": "average",
                },
            },
            "sources": [
                sources()["egon-data"],
                sources()["vg250"],
                sources()["demandregio"],
            ]
            + sources_dict[table],
            "licenses": [license_odbl("© eGon development team")],
            "contributors": contris,
            "resources": [
                {
                    "profile": "tabular-data-resource",
                    "name": name,
                    "path": "None",
                    "format": "PostgreSQL",
                    "encoding": "UTF-8",
                    "schema": {
                        "fields": generate_resource_fields_from_db_table(
                            schema,
                            table,
                        ),
                        "primaryKey": ["scn_name"] + primaryKey_dict[table],
                    },
                    "dialect": {"delimiter": "", "decimalSeparator": ""},
                }
            ],
            "review": {"path": "", "badge": ""},
            "metaMetadata": meta_metadata(),
            "_comment": {
                "metadata": (
                    "Metadata documentation and explanation (https://"
                    "github.com/OpenEnergyPlatform/oemetadata/blob/master/"
                    "metadata/v141/metadata_key_description.md)"
                ),
                "dates": (
                    "Dates and time must follow the ISO8601 including time "
                    "zone (YYYY-MM-DD or YYYY-MM-DDThh:mm:ss±hh)"
                ),
                "units": "Use a space between numbers and units (100 m)",
                "languages": (
                    "Languages must follow the IETF (BCP47) format (en-GB, "
                    "en-US, de-DE)"
                ),
                "licenses": (
                    "License name must follow the SPDX License List "
                    "(https://spdx.org/licenses/)"
                ),
                "review": (
                    "Following the OEP Data Review (https://github.com/"
                    "OpenEnergyPlatform/data-preprocessing/wiki)"
                ),
                "none": "If not applicable use (none)",
            },
        }

        dialect = get_dialect(oep_metadata_version())()

        meta = dialect.compile_and_render(dialect.parse(json.dumps(meta)))

        db.submit_comment(
            f"'{json.dumps(meta)}'",
            schema,
            table,
        )


# Code
def cts_data_import(cts_cool_vent_ac_share):
    """
    Import CTS data necessary to identify DSM-potential.

    ----------
    cts_share: float
        Share of cooling, ventilation and AC in CTS demand
    """

    # import load data

    sources = config.datasets()["DSM_CTS_industry"]["sources"][
        "cts_loadcurves"
    ]

    ts = db.select_dataframe(
        f"""SELECT bus_id, scn_name, p_set FROM
        {sources['schema']}.{sources['table']}"""
    )

    # identify relevant columns and prepare df to be returned

    dsm = pd.DataFrame(index=ts.index)

    dsm["bus"] = ts["bus_id"].copy()
    dsm["scn_name"] = ts["scn_name"].copy()
    dsm["p_set"] = ts["p_set"].copy()

    # calculate share of timeseries for air conditioning, cooling and
    # ventilation out of CTS-data

    timeseries = dsm["p_set"].copy()

    for index, liste in timeseries.items():
        share = [float(item) * cts_cool_vent_ac_share for item in liste]
        timeseries.loc[index] = share

    dsm["p_set"] = timeseries.copy()

    return dsm


def ind_osm_data_import(ind_vent_cool_share):

This code seems to be duplicated in your project.

    """
    Import industry data per osm-area necessary to identify DSM-potential.
        ----------
    ind_share: float
        Share of considered application in industry demand
    """

    # import load data

    sources = config.datasets()["DSM_CTS_industry"]["sources"][
        "ind_osm_loadcurves"
    ]

    dsm = db.select_dataframe(
        f"""
        SELECT bus, scn_name, p_set FROM
        {sources['schema']}.{sources['table']}
        """
    )

    # calculate share of timeseries for cooling and ventilation out of
    # industry-data

    timeseries = dsm["p_set"].copy()

    for index, liste in timeseries.items():
        share = [float(item) * ind_vent_cool_share for item in liste]

        timeseries.loc[index] = share

    dsm["p_set"] = timeseries.copy()

    return dsm


def ind_osm_data_import_individual(ind_vent_cool_share):

This code seems to be duplicated in your project.

    """
    Import industry data per osm-area necessary to identify DSM-potential.
        ----------
    ind_share: float
        Share of considered application in industry demand
    """

    # import load data

    sources = config.datasets()["DSM_CTS_industry"]["sources"][
        "ind_osm_loadcurves_individual"
    ]

    dsm = db.select_dataframe(
        f"""
        SELECT osm_id, bus_id as bus, scn_name, p_set FROM
        {sources['schema']}.{sources['table']}
        """
    )

    # calculate share of timeseries for cooling and ventilation out of
    # industry-data

    timeseries = dsm["p_set"].copy()

    for index, liste in timeseries.items():
        share = [float(item) * ind_vent_cool_share for item in liste]

        timeseries.loc[index] = share

    dsm["p_set"] = timeseries.copy()

    return dsm


def ind_sites_vent_data_import(ind_vent_share, wz):

This code seems to be duplicated in your project.

    """
    Import industry sites necessary to identify DSM-potential.
        ----------
    ind_vent_share: float
        Share of considered application in industry demand
    wz: int
        Wirtschaftszweig to be considered within industry sites
    """

    # import load data

    sources = config.datasets()["DSM_CTS_industry"]["sources"][
        "ind_sites_loadcurves"
    ]

    dsm = db.select_dataframe(
        f"""
        SELECT bus, scn_name, p_set FROM
        {sources['schema']}.{sources['table']}
        WHERE wz = {wz}
        """
    )

    # calculate share of timeseries for ventilation

    timeseries = dsm["p_set"].copy()

    for index, liste in timeseries.items():
        share = [float(item) * ind_vent_share for item in liste]
        timeseries.loc[index] = share

    dsm["p_set"] = timeseries.copy()

    return dsm


def ind_sites_vent_data_import_individual(ind_vent_share, wz):

This code seems to be duplicated in your project.

    """
    Import industry sites necessary to identify DSM-potential.
        ----------
    ind_vent_share: float
        Share of considered application in industry demand
    wz: int
        Wirtschaftszweig to be considered within industry sites
    """

    # import load data

    sources = config.datasets()["DSM_CTS_industry"]["sources"][
        "ind_sites_loadcurves_individual"
    ]

    dsm = db.select_dataframe(
        f"""
        SELECT site_id, bus_id as bus, scn_name, p_set FROM
        {sources['schema']}.{sources['table']}
        WHERE wz = {wz}
        """
    )

    # calculate share of timeseries for ventilation

    timeseries = dsm["p_set"].copy()

    for index, liste in timeseries.items():
        share = [float(item) * ind_vent_share for item in liste]
        timeseries.loc[index] = share

    dsm["p_set"] = timeseries.copy()

    return dsm


def calc_ind_site_timeseries(scenario):
    # calculate timeseries per site
    # -> using code from egon.data.datasets.industry.temporal:
    # calc_load_curves_ind_sites

    # select demands per industrial site including the subsector information
    source1 = config.datasets()["DSM_CTS_industry"]["sources"][
        "demandregio_ind_sites"
    ]

    demands_ind_sites = db.select_dataframe(
        f"""SELECT industrial_sites_id, wz, demand
            FROM {source1['schema']}.{source1['table']}
            WHERE scenario = '{scenario}'
            AND demand > 0
            """
    ).set_index(["industrial_sites_id"])

    # select industrial sites as demand_areas from database
    source2 = config.datasets()["DSM_CTS_industry"]["sources"]["ind_sites"]

    demand_area = db.select_geodataframe(
        f"""SELECT id, geom, subsector FROM
            {source2['schema']}.{source2['table']}""",
        index_col="id",
        geom_col="geom",
        epsg=3035,
    )

    # replace entries to bring it in line with demandregio's subsector
    # definitions
    demands_ind_sites.replace(1718, 17, inplace=True)
    share_wz_sites = demands_ind_sites.copy()

    # create additional df on wz_share per industrial site, which is always set
    # to one as the industrial demand per site is subsector specific
    share_wz_sites.demand = 1
    share_wz_sites.reset_index(inplace=True)

    share_transpose = pd.DataFrame(
        index=share_wz_sites.industrial_sites_id.unique(),
        columns=share_wz_sites.wz.unique(),
    )
    share_transpose.index.rename("industrial_sites_id", inplace=True)
    for wz in share_transpose.columns:
        share_transpose[wz] = (
            share_wz_sites[share_wz_sites.wz == wz]
            .set_index("industrial_sites_id")
            .demand
        )

    # calculate load curves
    load_curves = calc_load_curve(share_transpose, demands_ind_sites["demand"])

    # identify bus per industrial site
    curves_bus = identify_bus(load_curves, demand_area)
    curves_bus.index = curves_bus["id"].astype(int)

    # initialize dataframe to be returned

    ts = pd.DataFrame(
        data=curves_bus["bus_id"], index=curves_bus["id"].astype(int)
    )
    ts["scenario_name"] = scenario
    curves_bus.drop({"id", "bus_id", "geom"}, axis=1, inplace=True)
    ts["p_set"] = curves_bus.values.tolist()

    # add subsector to relate to Schmidt's tables afterwards
    ts["application"] = demand_area["subsector"]

    return ts


def relate_to_schmidt_sites(dsm):
    # import industrial sites by Schmidt

    source = config.datasets()["DSM_CTS_industry"]["sources"][
        "ind_sites_schmidt"
    ]

    schmidt = db.select_dataframe(
        f"""SELECT application, geom FROM
            {source['schema']}.{source['table']}"""
    )

    # relate calculated timeseries (dsm) to Schmidt's industrial sites

    applications = np.unique(schmidt["application"])
    dsm = pd.DataFrame(dsm[dsm["application"].isin(applications)])

    # initialize dataframe to be returned

    dsm.rename(
        columns={"scenario_name": "scn_name", "bus_id": "bus"},
        inplace=True,
    )

    return dsm


def ind_sites_data_import():
    """
    Import industry sites data necessary to identify DSM-potential.
    """
    # calculate timeseries per site

    # scenario eGon2035
    dsm_2035 = calc_ind_site_timeseries("eGon2035")
    dsm_2035.reset_index(inplace=True)
    # scenario eGon100RE
    dsm_100 = calc_ind_site_timeseries("eGon100RE")
    dsm_100.reset_index(inplace=True)
    # bring df for both scenarios together
    dsm_100.index = range(len(dsm_2035), (len(dsm_2035) + len((dsm_100))))
    dsm = dsm_2035.append(dsm_100)

    # relate calculated timeseries to Schmidt's industrial sites

    dsm = relate_to_schmidt_sites(dsm)

    return dsm[["application", "id", "bus", "scn_name", "p_set"]]


def calculate_potentials(s_flex, s_util, s_inc, s_dec, delta_t, dsm):
    """
    Calculate DSM-potential per bus using the methods by Heitkoetter et. al.:
        https://doi.org/10.1016/j.adapen.2020.100001
    Parameters
        ----------
    s_flex: float
        Feasability factor to account for socio-technical restrictions
    s_util: float
        Average annual utilisation rate
    s_inc: float
        Shiftable share of installed capacity up to which load can be
        increased considering technical limitations
    s_dec: float
        Shiftable share of installed capacity up to which load can be
        decreased considering technical limitations
    delta_t: int
        Maximum shift duration in hours
    dsm: DataFrame
        List of existing buses with DSM-potential including timeseries of
        loads
    """

    # copy relevant timeseries
    timeseries = dsm["p_set"].copy()

    # calculate scheduled load L(t)

    scheduled_load = timeseries.copy()

    for index, liste in scheduled_load.items():
        share = [item * s_flex for item in liste]
        scheduled_load.loc[index] = share

    # calculate maximum capacity Lambda

    # calculate energy annual requirement
    energy_annual = pd.Series(index=timeseries.index, dtype=float)
    for index, liste in timeseries.items():
        energy_annual.loc[index] = sum(liste)

    # calculate Lambda
    lam = (energy_annual * s_flex) / (8760 * s_util)

    # calculation of P_max and P_min

    # P_max
    p_max = scheduled_load.copy()
    for index, liste in scheduled_load.items():
        lamb = lam.loc[index]
        p_max.loc[index] = [max(0, lamb * s_inc - item) for item in liste]

    # P_min
    p_min = scheduled_load.copy()
    for index, liste in scheduled_load.items():
        lamb = lam.loc[index]
        p_min.loc[index] = [min(0, -(item - lamb * s_dec)) for item in liste]

    # calculation of E_max and E_min

    e_max = scheduled_load.copy()
    e_min = scheduled_load.copy()

    for index, liste in scheduled_load.items():
        emin = []
        emax = []
        for i in range(len(liste)):
            if i + delta_t > len(liste):
                emax.append(
                    (sum(liste[i:]) + sum(liste[: delta_t - (len(liste) - i)]))
                )
            else:
                emax.append(sum(liste[i : i + delta_t]))
            if i - delta_t < 0:
                emin.append(
                    (
                        -1
                        * (
                            (
                                sum(liste[:i])
                                + sum(liste[len(liste) - delta_t + i :])
                            )
                        )
                    )
                )
            else:
                emin.append(-1 * sum(liste[i - delta_t : i]))
        e_max.loc[index] = emax
        e_min.loc[index] = emin

    return p_max, p_min, e_max, e_min


def create_dsm_components(
    con, p_max, p_min, e_max, e_min, dsm, export_aggregated=True
):
    """
    Create components representing DSM.
    Parameters
        ----------
    con :
        Connection to database
    p_max: DataFrame
        Timeseries identifying maximum load increase
    p_min: DataFrame
        Timeseries identifying maximum load decrease
    e_max: DataFrame
        Timeseries identifying maximum energy amount to be preponed
    e_min: DataFrame
        Timeseries identifying maximum energy amount to be postponed
    dsm: DataFrame
        List of existing buses with DSM-potential including timeseries of loads
    """
    if not export_aggregated:
        # calculate P_nom and P per unit
        p_nom = pd.Series(index=p_max.index, dtype=float)
        for index, row in p_max.items():
            nom = max(max(row), abs(min(p_min.loc[index])))
            p_nom.loc[index] = nom
            new = [element / nom for element in row]
            p_max.loc[index] = new
            new = [element / nom for element in p_min.loc[index]]
            p_min.loc[index] = new

        # calculate E_nom and E per unit
        e_nom = pd.Series(index=p_min.index, dtype=float)
        for index, row in e_max.items():
            nom = max(max(row), abs(min(e_min.loc[index])))
            e_nom.loc[index] = nom
            new = [element / nom for element in row]
            e_max.loc[index] = new
            new = [element / nom for element in e_min.loc[index]]
            e_min.loc[index] = new

    # add DSM-buses to "original" buses
    dsm_buses = gpd.GeoDataFrame(index=dsm.index)
    dsm_buses["original_bus"] = dsm["bus"].copy()
    dsm_buses["scn_name"] = dsm["scn_name"].copy()

    # get original buses and add copy of relevant information
    target1 = config.datasets()["DSM_CTS_industry"]["targets"]["bus"]
    original_buses = db.select_geodataframe(
        f"""SELECT bus_id, v_nom, scn_name, x, y, geom FROM
            {target1['schema']}.{target1['table']}""",
        geom_col="geom",
        epsg=4326,
    )

    # copy relevant information from original buses to DSM-buses
    dsm_buses["index"] = dsm_buses.index
    originals = original_buses[
        original_buses["bus_id"].isin(np.unique(dsm_buses["original_bus"]))
    ]
    dsm_buses = originals.merge(
        dsm_buses,
        left_on=["bus_id", "scn_name"],
        right_on=["original_bus", "scn_name"],
    )
    dsm_buses.index = dsm_buses["index"]
    dsm_buses.drop(["bus_id", "index"], axis=1, inplace=True)

    # new bus_ids for DSM-buses
    max_id = original_buses["bus_id"].max()
    if np.isnan(max_id):
        max_id = 0
    dsm_id = max_id + 1
    bus_id = pd.Series(index=dsm_buses.index, dtype=int)

    # Get number of DSM buses for both scenarios
    rows_per_scenario = (
        dsm_buses.groupby("scn_name").count().original_bus.to_dict()
    )

    # Assignment of DSM ids
    bus_id.iloc[: rows_per_scenario.get("eGon2035", 0)] = range(
        dsm_id, dsm_id + rows_per_scenario.get("eGon2035", 0)
    )

    bus_id.iloc[
        rows_per_scenario.get("eGon2035", 0) : rows_per_scenario.get(
            "eGon2035", 0
        )
        + rows_per_scenario.get("eGon100RE", 0)
    ] = range(dsm_id, dsm_id + rows_per_scenario.get("eGon100RE", 0))

    dsm_buses["bus_id"] = bus_id

    # add links from "orignal" buses to DSM-buses

    dsm_links = pd.DataFrame(index=dsm_buses.index)
    dsm_links["original_bus"] = dsm_buses["original_bus"].copy()
    dsm_links["dsm_bus"] = dsm_buses["bus_id"].copy()
    dsm_links["scn_name"] = dsm_buses["scn_name"].copy()

    # set link_id
    target2 = config.datasets()["DSM_CTS_industry"]["targets"]["link"]
    sql = f"""SELECT link_id FROM {target2['schema']}.{target2['table']}"""
    max_id = pd.read_sql_query(sql, con)
    max_id = max_id["link_id"].max()
    if np.isnan(max_id):
        max_id = 0
    dsm_id = max_id + 1
    link_id = pd.Series(index=dsm_buses.index, dtype=int)

    # Assignment of link ids
    link_id.iloc[: rows_per_scenario.get("eGon2035", 0)] = range(
        dsm_id, dsm_id + rows_per_scenario.get("eGon2035", 0)
    )

    link_id.iloc[
        rows_per_scenario.get("eGon2035", 0) : rows_per_scenario.get(
            "eGon2035", 0
        )
        + rows_per_scenario.get("eGon100RE", 0)
    ] = range(dsm_id, dsm_id + rows_per_scenario.get("eGon100RE", 0))

    dsm_links["link_id"] = link_id

    # add calculated timeseries to df to be returned
    if not export_aggregated:
        dsm_links["p_nom"] = p_nom

The variable p_nom does not seem to be defined in case BooleanNotNode on line 796 is False. Are you sure this can never be the case?

    dsm_links["p_min"] = p_min
    dsm_links["p_max"] = p_max

    # add DSM-stores

    dsm_stores = pd.DataFrame(index=dsm_buses.index)
    dsm_stores["bus"] = dsm_buses["bus_id"].copy()
    dsm_stores["scn_name"] = dsm_buses["scn_name"].copy()
    dsm_stores["original_bus"] = dsm_buses["original_bus"].copy()

    # set store_id
    target3 = config.datasets()["DSM_CTS_industry"]["targets"]["store"]
    sql = f"""SELECT store_id FROM {target3['schema']}.{target3['table']}"""
    max_id = pd.read_sql_query(sql, con)
    max_id = max_id["store_id"].max()
    if np.isnan(max_id):
        max_id = 0
    dsm_id = max_id + 1
    store_id = pd.Series(index=dsm_buses.index, dtype=int)

    # Assignment of store ids
    store_id.iloc[: rows_per_scenario.get("eGon2035", 0)] = range(
        dsm_id, dsm_id + rows_per_scenario.get("eGon2035", 0)
    )

    store_id.iloc[
        rows_per_scenario.get("eGon2035", 0) : rows_per_scenario.get(
            "eGon2035", 0
        )
        + rows_per_scenario.get("eGon100RE", 0)
    ] = range(dsm_id, dsm_id + rows_per_scenario.get("eGon100RE", 0))

    dsm_stores["store_id"] = store_id

    # add calculated timeseries to df to be returned
    if not export_aggregated:
        dsm_stores["e_nom"] = e_nom

The variable e_nom does not seem to be defined in case BooleanNotNode on line 796 is False. Are you sure this can never be the case?

    dsm_stores["e_min"] = e_min
    dsm_stores["e_max"] = e_max

    return dsm_buses, dsm_links, dsm_stores


def aggregate_components(df_dsm_buses, df_dsm_links, df_dsm_stores):
    # aggregate buses

    grouper = [df_dsm_buses.original_bus, df_dsm_buses.scn_name]

    df_dsm_buses = df_dsm_buses.groupby(grouper).first()

    df_dsm_buses.reset_index(inplace=True)
    df_dsm_buses.sort_values("scn_name", inplace=True)

    # aggregate links

    df_dsm_links["p_max"] = df_dsm_links["p_max"].apply(lambda x: np.array(x))
    df_dsm_links["p_min"] = df_dsm_links["p_min"].apply(lambda x: np.array(x))

    grouper = [df_dsm_links.original_bus, df_dsm_links.scn_name]

    p_max = df_dsm_links.groupby(grouper)["p_max"].apply(np.sum)
    p_min = df_dsm_links.groupby(grouper)["p_min"].apply(np.sum)

    df_dsm_links = df_dsm_links.groupby(grouper).first()
    df_dsm_links.p_max = p_max
    df_dsm_links.p_min = p_min

    df_dsm_links.reset_index(inplace=True)
    df_dsm_links.sort_values("scn_name", inplace=True)

    # calculate P_nom and P per unit
    for index, row in df_dsm_links.iterrows():
        nom = max(max(row.p_max), abs(min(row.p_min)))
        df_dsm_links.at[index, "p_nom"] = nom

    df_dsm_links["p_max"] = df_dsm_links["p_max"] / df_dsm_links["p_nom"]
    df_dsm_links["p_min"] = df_dsm_links["p_min"] / df_dsm_links["p_nom"]

    df_dsm_links["p_max"] = df_dsm_links["p_max"].apply(lambda x: list(x))
    df_dsm_links["p_min"] = df_dsm_links["p_min"].apply(lambda x: list(x))

    # aggregate stores
    df_dsm_stores["e_max"] = df_dsm_stores["e_max"].apply(
        lambda x: np.array(x)
    )
    df_dsm_stores["e_min"] = df_dsm_stores["e_min"].apply(
        lambda x: np.array(x)
    )

    grouper = [df_dsm_stores.original_bus, df_dsm_stores.scn_name]

    e_max = df_dsm_stores.groupby(grouper)["e_max"].apply(np.sum)
    e_min = df_dsm_stores.groupby(grouper)["e_min"].apply(np.sum)

    df_dsm_stores = df_dsm_stores.groupby(grouper).first()
    df_dsm_stores.e_max = e_max
    df_dsm_stores.e_min = e_min

    df_dsm_stores.reset_index(inplace=True)
    df_dsm_stores.sort_values("scn_name", inplace=True)

    # calculate E_nom and E per unit
    for index, row in df_dsm_stores.iterrows():
        nom = max(max(row.e_max), abs(min(row.e_min)))
        df_dsm_stores.at[index, "e_nom"] = nom

    df_dsm_stores["e_max"] = df_dsm_stores["e_max"] / df_dsm_stores["e_nom"]
    df_dsm_stores["e_min"] = df_dsm_stores["e_min"] / df_dsm_stores["e_nom"]

    df_dsm_stores["e_max"] = df_dsm_stores["e_max"].apply(lambda x: list(x))
    df_dsm_stores["e_min"] = df_dsm_stores["e_min"].apply(lambda x: list(x))

    # select new bus_ids for aggregated buses and add to links and stores
    bus_id = db.next_etrago_id("Bus") + df_dsm_buses.index

    df_dsm_buses["bus_id"] = bus_id
    df_dsm_links["dsm_bus"] = bus_id
    df_dsm_stores["bus"] = bus_id

    # select new link_ids for aggregated links
    link_id = db.next_etrago_id("Link") + df_dsm_links.index

    df_dsm_links["link_id"] = link_id

    # select new store_ids to aggregated stores

    store_id = db.next_etrago_id("Store") + df_dsm_stores.index

    df_dsm_stores["store_id"] = store_id

    return df_dsm_buses, df_dsm_links, df_dsm_stores


def data_export(dsm_buses, dsm_links, dsm_stores, carrier):
    """
    Export new components to database.

    Parameters
    ----------
    dsm_buses: DataFrame
        Buses representing locations of DSM-potential
    dsm_links: DataFrame
        Links connecting DSM-buses and DSM-stores
    dsm_stores: DataFrame
        Stores representing DSM-potential
    carrier: str
        Remark to be filled in column 'carrier' identifying DSM-potential
    """

    targets = config.datasets()["DSM_CTS_industry"]["targets"]

    # dsm_buses

    insert_buses = gpd.GeoDataFrame(
        index=dsm_buses.index,
        data=dsm_buses["geom"],
        geometry="geom",
        crs=dsm_buses.crs,
    )
    insert_buses["scn_name"] = dsm_buses["scn_name"]
    insert_buses["bus_id"] = dsm_buses["bus_id"]
    insert_buses["v_nom"] = dsm_buses["v_nom"]
    insert_buses["carrier"] = carrier
    insert_buses["x"] = dsm_buses["x"]
    insert_buses["y"] = dsm_buses["y"]

    # insert into database
    insert_buses.to_postgis(
        targets["bus"]["table"],
        con=db.engine(),
        schema=targets["bus"]["schema"],
        if_exists="append",
        index=False,
        dtype={"geom": "geometry"},
    )

    # dsm_links

    insert_links = pd.DataFrame(index=dsm_links.index)
    insert_links["scn_name"] = dsm_links["scn_name"]
    insert_links["link_id"] = dsm_links["link_id"]
    insert_links["bus0"] = dsm_links["original_bus"]
    insert_links["bus1"] = dsm_links["dsm_bus"]
    insert_links["carrier"] = carrier
    insert_links["p_nom"] = dsm_links["p_nom"]

    # insert into database
    insert_links.to_sql(
        targets["link"]["table"],
        con=db.engine(),
        schema=targets["link"]["schema"],
        if_exists="append",
        index=False,
    )

    insert_links_timeseries = pd.DataFrame(index=dsm_links.index)
    insert_links_timeseries["scn_name"] = dsm_links["scn_name"]
    insert_links_timeseries["link_id"] = dsm_links["link_id"]
    insert_links_timeseries["p_min_pu"] = dsm_links["p_min"]
    insert_links_timeseries["p_max_pu"] = dsm_links["p_max"]
    insert_links_timeseries["temp_id"] = 1

    # insert into database
    insert_links_timeseries.to_sql(
        targets["link_timeseries"]["table"],
        con=db.engine(),
        schema=targets["link_timeseries"]["schema"],
        if_exists="append",
        index=False,
    )

    # dsm_stores

    insert_stores = pd.DataFrame(index=dsm_stores.index)
    insert_stores["scn_name"] = dsm_stores["scn_name"]
    insert_stores["store_id"] = dsm_stores["store_id"]
    insert_stores["bus"] = dsm_stores["bus"]
    insert_stores["carrier"] = carrier
    insert_stores["e_nom"] = dsm_stores["e_nom"]

    # insert into database
    insert_stores.to_sql(
        targets["store"]["table"],
        con=db.engine(),
        schema=targets["store"]["schema"],
        if_exists="append",
        index=False,
    )

    insert_stores_timeseries = pd.DataFrame(index=dsm_stores.index)
    insert_stores_timeseries["scn_name"] = dsm_stores["scn_name"]
    insert_stores_timeseries["store_id"] = dsm_stores["store_id"]
    insert_stores_timeseries["e_min_pu"] = dsm_stores["e_min"]
    insert_stores_timeseries["e_max_pu"] = dsm_stores["e_max"]
    insert_stores_timeseries["temp_id"] = 1

    # insert into database
    insert_stores_timeseries.to_sql(
        targets["store_timeseries"]["table"],
        con=db.engine(),
        schema=targets["store_timeseries"]["schema"],
        if_exists="append",
        index=False,
    )


def delete_dsm_entries(carrier):
    """
    Deletes DSM-components from database if they already exist before creating
    new ones.

    Parameters
        ----------
     carrier: str
        Remark in column 'carrier' identifying DSM-potential
    """

    targets = config.datasets()["DSM_CTS_industry"]["targets"]

    # buses

    sql = (
        f"DELETE FROM {targets['bus']['schema']}.{targets['bus']['table']} b "
        f"WHERE (b.carrier LIKE '{carrier}');"
    )
    db.execute_sql(sql)

    # links

    sql = f"""
        DELETE FROM {targets["link_timeseries"]["schema"]}.
        {targets["link_timeseries"]["table"]} t
        WHERE t.link_id IN
        (
            SELECT l.link_id FROM {targets["link"]["schema"]}.
            {targets["link"]["table"]} l
            WHERE l.carrier LIKE '{carrier}'
        );
        """

    db.execute_sql(sql)

    sql = f"""
        DELETE FROM {targets["link"]["schema"]}.
        {targets["link"]["table"]} l
        WHERE (l.carrier LIKE '{carrier}');
        """

    db.execute_sql(sql)

    # stores

    sql = f"""
        DELETE FROM {targets["store_timeseries"]["schema"]}.
        {targets["store_timeseries"]["table"]} t
        WHERE t.store_id IN
        (
            SELECT s.store_id FROM {targets["store"]["schema"]}.
            {targets["store"]["table"]} s
            WHERE s.carrier LIKE '{carrier}'
        );
        """

    db.execute_sql(sql)

    sql = f"""
        DELETE FROM {targets["store"]["schema"]}.{targets["store"]["table"]} s
        WHERE (s.carrier LIKE '{carrier}');
        """

    db.execute_sql(sql)


def dsm_cts_ind(
    con=db.engine(),
    cts_cool_vent_ac_share=0.22,
    ind_vent_cool_share=0.039,
    ind_vent_share=0.017,
):
    """
    Execute methodology to create and implement components for DSM considering
    a) CTS per osm-area: combined potentials of cooling, ventilation and air
      conditioning
    b) Industry per osm-are: combined potentials of cooling and ventilation
    c) Industrial Sites: potentials of ventilation in sites of
      "Wirtschaftszweig" (WZ) 23
    d) Industrial Sites: potentials of sites specified by subsectors
      identified by Schmidt (https://zenodo.org/record/3613767#.YTsGwVtCRhG):
      Paper, Recycled Paper, Pulp, Cement

    Modelled using the methods by Heitkoetter et. al.:
    https://doi.org/10.1016/j.adapen.2020.100001

    Parameters
    ----------
    con :
        Connection to database
    cts_cool_vent_ac_share: float
        Share of cooling, ventilation and AC in CTS demand
    ind_vent_cool_share: float
        Share of cooling and ventilation in industry demand
    ind_vent_share: float
        Share of ventilation in industry demand in sites of WZ 23

    """

    # CTS per osm-area: cooling, ventilation and air conditioning

    print(" ")
    print("CTS per osm-area: cooling, ventilation and air conditioning")
    print(" ")

    dsm = cts_data_import(cts_cool_vent_ac_share)

    # calculate combined potentials of cooling, ventilation and air
    # conditioning in CTS using combined parameters by Heitkoetter et. al.
    p_max, p_min, e_max, e_min = calculate_potentials(
        s_flex=S_FLEX_CTS,
        s_util=S_UTIL_CTS,
        s_inc=S_INC_CTS,
        s_dec=S_DEC_CTS,
        delta_t=DELTA_T_CTS,
        dsm=dsm,
    )

    dsm_buses, dsm_links, dsm_stores = create_dsm_components(
        con, p_max, p_min, e_max, e_min, dsm
    )

    df_dsm_buses = dsm_buses.copy()
    df_dsm_links = dsm_links.copy()
    df_dsm_stores = dsm_stores.copy()

    # industry per osm-area: cooling and ventilation

    print(" ")
    print("industry per osm-area: cooling and ventilation")
    print(" ")

    dsm = ind_osm_data_import(ind_vent_cool_share)

    # calculate combined potentials of cooling and ventilation in industrial
    # sector using combined parameters by Heitkoetter et. al.
    p_max, p_min, e_max, e_min = calculate_potentials(
        s_flex=S_FLEX_OSM,
        s_util=S_UTIL_OSM,
        s_inc=S_INC_OSM,
        s_dec=S_DEC_OSM,
        delta_t=DELTA_T_OSM,
        dsm=dsm,
    )

    dsm_buses, dsm_links, dsm_stores = create_dsm_components(
        con, p_max, p_min, e_max, e_min, dsm
    )

    df_dsm_buses = gpd.GeoDataFrame(
        pd.concat([df_dsm_buses, dsm_buses], ignore_index=True),
        crs="EPSG:4326",
    )
    df_dsm_links = pd.DataFrame(
        pd.concat([df_dsm_links, dsm_links], ignore_index=True)
    )
    df_dsm_stores = pd.DataFrame(
        pd.concat([df_dsm_stores, dsm_stores], ignore_index=True)
    )

    # industry sites

    # industry sites: different applications

    dsm = ind_sites_data_import()

    print(" ")
    print("industry sites: paper")
    print(" ")

    dsm_paper = gpd.GeoDataFrame(
        dsm[
            dsm["application"].isin(
                [
                    "Graphic Paper",
                    "Packing Paper and Board",
                    "Hygiene Paper",
                    "Technical/Special Paper and Board",
                ]
            )
        ]
    )

    # calculate potentials of industrial sites with paper-applications
    # using parameters by Heitkoetter et al.
    p_max, p_min, e_max, e_min = calculate_potentials(
        s_flex=S_FLEX_PAPER,
        s_util=S_UTIL_PAPER,
        s_inc=S_INC_PAPER,
        s_dec=S_DEC_PAPER,
        delta_t=DELTA_T_PAPER,
        dsm=dsm_paper,
    )

    dsm_buses, dsm_links, dsm_stores = create_dsm_components(
        con, p_max, p_min, e_max, e_min, dsm_paper
    )

    df_dsm_buses = gpd.GeoDataFrame(
        pd.concat([df_dsm_buses, dsm_buses], ignore_index=True),
        crs="EPSG:4326",
    )
    df_dsm_links = pd.DataFrame(
        pd.concat([df_dsm_links, dsm_links], ignore_index=True)
    )
    df_dsm_stores = pd.DataFrame(
        pd.concat([df_dsm_stores, dsm_stores], ignore_index=True)
    )

    print(" ")
    print("industry sites: recycled paper")
    print(" ")

    # calculate potentials of industrial sites with recycled paper-applications
    # using parameters by Heitkoetter et. al.
    dsm_recycled_paper = gpd.GeoDataFrame(
        dsm[dsm["application"] == "Recycled Paper"]
    )

    p_max, p_min, e_max, e_min = calculate_potentials(
        s_flex=S_FLEX_RECYCLED_PAPER,
        s_util=S_UTIL_RECYCLED_PAPER,
        s_inc=S_INC_RECYCLED_PAPER,
        s_dec=S_DEC_RECYCLED_PAPER,
        delta_t=DELTA_T_RECYCLED_PAPER,
        dsm=dsm_recycled_paper,
    )

    dsm_buses, dsm_links, dsm_stores = create_dsm_components(
        con, p_max, p_min, e_max, e_min, dsm_recycled_paper
    )

    df_dsm_buses = gpd.GeoDataFrame(
        pd.concat([df_dsm_buses, dsm_buses], ignore_index=True),
        crs="EPSG:4326",
    )
    df_dsm_links = pd.DataFrame(
        pd.concat([df_dsm_links, dsm_links], ignore_index=True)
    )
    df_dsm_stores = pd.DataFrame(
        pd.concat([df_dsm_stores, dsm_stores], ignore_index=True)
    )

    print(" ")
    print("industry sites: pulp")
    print(" ")

    dsm_pulp = gpd.GeoDataFrame(dsm[dsm["application"] == "Mechanical Pulp"])

    # calculate potentials of industrial sites with pulp-applications
    # using parameters by Heitkoetter et al.
    p_max, p_min, e_max, e_min = calculate_potentials(
        s_flex=S_FLEX_PULP,
        s_util=S_UTIL_PULP,
        s_inc=S_INC_PULP,
        s_dec=S_DEC_PULP,
        delta_t=DELTA_T_PULP,
        dsm=dsm_pulp,
    )

    dsm_buses, dsm_links, dsm_stores = create_dsm_components(
        con, p_max, p_min, e_max, e_min, dsm_pulp
    )

    df_dsm_buses = gpd.GeoDataFrame(
        pd.concat([df_dsm_buses, dsm_buses], ignore_index=True),
        crs="EPSG:4326",
    )
    df_dsm_links = pd.DataFrame(
        pd.concat([df_dsm_links, dsm_links], ignore_index=True)
    )
    df_dsm_stores = pd.DataFrame(
        pd.concat([df_dsm_stores, dsm_stores], ignore_index=True)
    )

    # industry sites: cement

    print(" ")
    print("industry sites: cement")
    print(" ")

    dsm_cement = gpd.GeoDataFrame(dsm[dsm["application"] == "Cement Mill"])

    # calculate potentials of industrial sites with cement-applications
    # using parameters by Heitkoetter et al.
    p_max, p_min, e_max, e_min = calculate_potentials(
        s_flex=S_FLEX_CEMENT,
        s_util=S_UTIL_CEMENT,
        s_inc=S_INC_CEMENT,
        s_dec=S_DEC_CEMENT,
        delta_t=DELTA_T_CEMENT,
        dsm=dsm_cement,
    )

    dsm_buses, dsm_links, dsm_stores = create_dsm_components(
        con, p_max, p_min, e_max, e_min, dsm_cement
    )

    df_dsm_buses = gpd.GeoDataFrame(
        pd.concat([df_dsm_buses, dsm_buses], ignore_index=True),
        crs="EPSG:4326",
    )
    df_dsm_links = pd.DataFrame(
        pd.concat([df_dsm_links, dsm_links], ignore_index=True)
    )
    df_dsm_stores = pd.DataFrame(
        pd.concat([df_dsm_stores, dsm_stores], ignore_index=True)
    )

    # industry sites: ventilation in WZ23

    print(" ")
    print("industry sites: ventilation in WZ23")
    print(" ")

    dsm = ind_sites_vent_data_import(ind_vent_share, wz=WZ)

    # drop entries of Cement Mills whose DSM-potentials have already been
    # modelled
    cement = np.unique(dsm_cement["bus"].values)
    index_names = np.array(dsm[dsm["bus"].isin(cement)].index)
    dsm.drop(index_names, inplace=True)

    # calculate potentials of ventialtion in industrial sites of WZ 23
    # using parameters by Heitkoetter et al.
    p_max, p_min, e_max, e_min = calculate_potentials(
        s_flex=S_FLEX_WZ,
        s_util=S_UTIL_WZ,
        s_inc=S_INC_WZ,
        s_dec=S_DEC_WZ,
        delta_t=DELTA_T_WZ,
        dsm=dsm,
    )

    dsm_buses, dsm_links, dsm_stores = create_dsm_components(
        con, p_max, p_min, e_max, e_min, dsm
    )

    df_dsm_buses = gpd.GeoDataFrame(
        pd.concat([df_dsm_buses, dsm_buses], ignore_index=True),
        crs="EPSG:4326",
    )
    df_dsm_links = pd.DataFrame(
        pd.concat([df_dsm_links, dsm_links], ignore_index=True)
    )
    df_dsm_stores = pd.DataFrame(
        pd.concat([df_dsm_stores, dsm_stores], ignore_index=True)
    )

    # aggregate DSM components per substation
    dsm_buses, dsm_links, dsm_stores = aggregate_components(
        df_dsm_buses, df_dsm_links, df_dsm_stores
    )

    # export aggregated DSM components to database

    delete_dsm_entries("dsm-cts")
    delete_dsm_entries("dsm-ind-osm")
    delete_dsm_entries("dsm-ind-sites")
    delete_dsm_entries("dsm")

    data_export(dsm_buses, dsm_links, dsm_stores, carrier="dsm")


def create_table(df, table, engine=CON):
    """Create table"""
    table.__table__.drop(bind=engine, checkfirst=True)
    table.__table__.create(bind=engine, checkfirst=True)

    df.to_sql(
        name=table.__table__.name,
        schema=table.__table__.schema,
        con=engine,
        if_exists="append",
        index=False,
    )


def div_list(lst: list, div: float):
    return [v / div for v in lst]


def dsm_cts_ind_individual(
    cts_cool_vent_ac_share=CTS_COOL_VENT_AC_SHARE,
    ind_vent_cool_share=IND_VENT_COOL_SHARE,
    ind_vent_share=IND_VENT_SHARE,
):
    """
    Execute methodology to create and implement components for DSM considering
    a) CTS per osm-area: combined potentials of cooling, ventilation and air
      conditioning
    b) Industry per osm-are: combined potentials of cooling and ventilation
    c) Industrial Sites: potentials of ventilation in sites of
      "Wirtschaftszweig" (WZ) 23
    d) Industrial Sites: potentials of sites specified by subsectors
      identified by Schmidt (https://zenodo.org/record/3613767#.YTsGwVtCRhG):
      Paper, Recycled Paper, Pulp, Cement

    Modelled using the methods by Heitkoetter et. al.:
    https://doi.org/10.1016/j.adapen.2020.100001

    Parameters
    ----------
    cts_cool_vent_ac_share: float
        Share of cooling, ventilation and AC in CTS demand
    ind_vent_cool_share: float
        Share of cooling and ventilation in industry demand
    ind_vent_share: float
        Share of ventilation in industry demand in sites of WZ 23

    """

    # CTS per osm-area: cooling, ventilation and air conditioning

    print(" ")
    print("CTS per osm-area: cooling, ventilation and air conditioning")
    print(" ")

    dsm = cts_data_import(cts_cool_vent_ac_share)

    # calculate combined potentials of cooling, ventilation and air
    # conditioning in CTS using combined parameters by Heitkoetter et. al.
    vals = calculate_potentials(
        s_flex=S_FLEX_CTS,
        s_util=S_UTIL_CTS,
        s_inc=S_INC_CTS,
        s_dec=S_DEC_CTS,
        delta_t=DELTA_T_CTS,
        dsm=dsm,
    )

    dsm = dsm.assign(
        p_set=dsm.p_set.apply(div_list, div=cts_cool_vent_ac_share)
    )

    base_columns = [
        "bus",
        "scn_name",
        "p_set",
        "p_max",
        "p_min",
        "e_max",
        "e_min",
    ]

    cts_df = pd.concat([dsm, *vals], axis=1, ignore_index=True)
    cts_df.columns = base_columns

    print(" ")
    print("industry per osm-area: cooling and ventilation")
    print(" ")

    dsm = ind_osm_data_import_individual(ind_vent_cool_share)

    # calculate combined potentials of cooling and ventilation in industrial
    # sector using combined parameters by Heitkoetter et al.
    vals = calculate_potentials(
        s_flex=S_FLEX_OSM,
        s_util=S_UTIL_OSM,
        s_inc=S_INC_OSM,
        s_dec=S_DEC_OSM,
        delta_t=DELTA_T_OSM,
        dsm=dsm,
    )

    dsm = dsm.assign(p_set=dsm.p_set.apply(div_list, div=ind_vent_cool_share))

    columns = ["osm_id"] + base_columns

    osm_df = pd.concat([dsm, *vals], axis=1, ignore_index=True)
    osm_df.columns = columns

    # industry sites

    # industry sites: different applications

    dsm = ind_sites_data_import()

    print(" ")
    print("industry sites: paper")
    print(" ")

    dsm_paper = gpd.GeoDataFrame(
        dsm[
            dsm["application"].isin(
                [
                    "Graphic Paper",
                    "Packing Paper and Board",
                    "Hygiene Paper",
                    "Technical/Special Paper and Board",
                ]
            )
        ]
    )

    # calculate potentials of industrial sites with paper-applications
    # using parameters by Heitkoetter et al.
    vals = calculate_potentials(
        s_flex=S_FLEX_PAPER,
        s_util=S_UTIL_PAPER,
        s_inc=S_INC_PAPER,
        s_dec=S_DEC_PAPER,
        delta_t=DELTA_T_PAPER,
        dsm=dsm_paper,
    )

    columns = ["application", "industrial_sites_id"] + base_columns

    paper_df = pd.concat([dsm_paper, *vals], axis=1, ignore_index=True)
    paper_df.columns = columns

    print(" ")
    print("industry sites: recycled paper")
    print(" ")

    # calculate potentials of industrial sites with recycled paper-applications
    # using parameters by Heitkoetter et. al.
    dsm_recycled_paper = gpd.GeoDataFrame(
        dsm[dsm["application"] == "Recycled Paper"]
    )

    vals = calculate_potentials(
        s_flex=S_FLEX_RECYCLED_PAPER,
        s_util=S_UTIL_RECYCLED_PAPER,
        s_inc=S_INC_RECYCLED_PAPER,
        s_dec=S_DEC_RECYCLED_PAPER,
        delta_t=DELTA_T_RECYCLED_PAPER,
        dsm=dsm_recycled_paper,
    )

    recycled_paper_df = pd.concat(
        [dsm_recycled_paper, *vals], axis=1, ignore_index=True
    )
    recycled_paper_df.columns = columns

    print(" ")
    print("industry sites: pulp")
    print(" ")

    dsm_pulp = gpd.GeoDataFrame(dsm[dsm["application"] == "Mechanical Pulp"])

    # calculate potentials of industrial sites with pulp-applications
    # using parameters by Heitkoetter et al.
    vals = calculate_potentials(
        s_flex=S_FLEX_PULP,
        s_util=S_UTIL_PULP,
        s_inc=S_INC_PULP,
        s_dec=S_DEC_PULP,
        delta_t=DELTA_T_PULP,
        dsm=dsm_pulp,
    )

    pulp_df = pd.concat([dsm_pulp, *vals], axis=1, ignore_index=True)
    pulp_df.columns = columns

    # industry sites: cement

    print(" ")
    print("industry sites: cement")
    print(" ")

    dsm_cement = gpd.GeoDataFrame(dsm[dsm["application"] == "Cement Mill"])

    # calculate potentials of industrial sites with cement-applications
    # using parameters by Heitkoetter et al.
    vals = calculate_potentials(
        s_flex=S_FLEX_CEMENT,
        s_util=S_UTIL_CEMENT,
        s_inc=S_INC_CEMENT,
        s_dec=S_DEC_CEMENT,
        delta_t=DELTA_T_CEMENT,
        dsm=dsm_cement,
    )

    cement_df = pd.concat([dsm_cement, *vals], axis=1, ignore_index=True)
    cement_df.columns = columns

    ind_df = pd.concat(
        [paper_df, recycled_paper_df, pulp_df, cement_df], ignore_index=True
    )

    # industry sites: ventilation in WZ23

    print(" ")
    print("industry sites: ventilation in WZ23")
    print(" ")

    dsm = ind_sites_vent_data_import_individual(ind_vent_share, wz=WZ)

    # drop entries of Cement Mills whose DSM-potentials have already been
    # modelled
    cement = np.unique(dsm_cement["bus"].values)
    index_names = np.array(dsm[dsm["bus"].isin(cement)].index)
    dsm.drop(index_names, inplace=True)

    # calculate potentials of ventialtion in industrial sites of WZ 23
    # using parameters by Heitkoetter et al.
    vals = calculate_potentials(
        s_flex=S_FLEX_WZ,
        s_util=S_UTIL_WZ,
        s_inc=S_INC_WZ,
        s_dec=S_DEC_WZ,
        delta_t=DELTA_T_WZ,
        dsm=dsm,
    )

    columns = ["site_id"] + base_columns

    ind_sites_df = pd.concat([dsm, *vals], axis=1, ignore_index=True)
    ind_sites_df.columns = columns

    # create tables
    create_table(
        df=cts_df, table=EgonEtragoElectricityCtsDsmTimeseries, engine=CON
    )
    create_table(
        df=osm_df,
        table=EgonOsmIndLoadCurvesIndividualDsmTimeseries,
        engine=CON,
    )
    create_table(
        df=ind_df,
        table=EgonDemandregioSitesIndElectricityDsmTimeseries,
        engine=CON,
    )
    create_table(
        df=ind_sites_df,
        table=EgonSitesIndLoadCurvesIndividualDsmTimeseries,
        engine=CON,
    )


def dsm_cts_ind_processing():
    dsm_cts_ind()

    dsm_cts_ind_individual()

    add_metadata_individual()