data.datasets.DSM_cts_ind.create_table() - Code Metrics - openego/eGon-data - Measure and Improve Code Quality continuously with Scrutinizer

data.datasets.DSM_cts_ind.create_table() A
last analyzed 2025-06-24 07:50 UTC

↳ Parent: data.datasets.DSM_cts_ind
Complexity

Conditions
Size

Total Lines	11
Code Lines	9
Duplication

Lines	0
Ratio	0 %
Importance

Changes
Metric	Value
eloc	9
dl	0
loc	11
rs	9.95
c	0
b	0
f	0
cc	1
nop	3
"""
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,
    meta_metadata,
    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):
    """
    Calculate Demand-Side Management potentials and transfer to charactersitics of DSM components

    DSM within this work includes the shifting of loads within the sectors of
    industry and CTS. Therefore, the corresponding formerly prepared demand
    time sereies are used. Shiftable potentials are calculated using the
    parametrization elaborated in Heitkoetter et. al (doi:https://doi.org/10.1016/j.adapen.2020.100001).
    DSM is modelled as storage-equivalent operation using the methods by Kleinhans (doi:10.48550/ARXIV.1401.4121).
    The potentials are transferred to characterisitcs of DSM links (minimal and
    maximal shiftable power per time step) and DSM stores (minimum and maximum
    capacity per time step). DSM buses are created to connect DSM components with
    the electrical network. All DSM components are added to the corresponding
    tables for the transmission grid level. For the distribution grids, the
    respective time series are exported to the corresponding tables (for the
    required higher spatial resolution).

    *Dependencies*
      * :py:class:`CtsElectricityDemand <egon.data.datasets.electricity_demand>`
      * :py:class:`IndustrialDemandCurves <from egon.data.datasets.industry>`
      * :py:class:`Osmtgmod <egon.data.datasets.osmtgmod>`

    *Resulting tables*
      * :py:class:`grid.egon_etrago_bus <egon.data.datasets.etrago_setup.EgonPfHvBus>` is extended
      * :py:class:`grid.egon_etrago_link <egon.data.datasets.etrago_setup.EgonPfHvLink>` is extended
      * :py:class:`grid.egon_etrago_link_timeseries <egon.data.datasets.etrago_setup.EgonPfHvLinkTimeseries>` is extended
      * :py:class:`grid.egon_etrago_store <egon.data.datasets.etrago_setup.EgonPfHvStore>` is extended
      * :py:class:`grid.egon_etrago_store_timeseries <egon.data.datasets.etrago_setup.EgonPfHvStoreTimeseries>` is extended
      * :py:class:`demand.egon_etrago_electricity_cts_dsm_timeseries <egon.data.datasets.DsmPotential.EgonEtragoElectricityCtsDsmTimeseries>` is created and filled # noqa: E501
      * :py:class:`demand.egon_osm_ind_load_curves_individual_dsm_timeseries <egon.data.datasets.DsmPotential.EgonOsmIndLoadCurvesIndividualDsmTimeseries>` is created and filled # noqa: E501
      * :py:class:`demand.egon_demandregio_sites_ind_electricity_dsm_timeseries <egon.data.datasets.DsmPotential.EgonDemandregioSitesIndElectricityDsmTimeseries>` is created and filled # noqa: E501
      * :py:class:`demand.egon_sites_ind_load_curves_individual_dsm_timeseries <egon.data.datasets.DsmPotential.EgonSitesIndLoadCurvesIndividualDsmTimeseries>` is created and filled # noqa: E501

    """

    #:
    name: str = "DsmPotential"
    #:
    version: str = "0.0.7"

    def __init__(self, dependencies):
        super().__init__(
            name=self.name,
            version=self.version,
            dependencies=dependencies,
            tasks=(dsm_cts_ind_processing,),
        )


# Datasets
class EgonEtragoElectricityCtsDsmTimeseries(Base):

    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):

    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):

    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):

    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(f"oep-v{meta_metadata()['metadataVersion'][4:7]}")()

        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.

    Parameters
    ----------
    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):

    """
    Import industry data per osm-area necessary to identify DSM-potential.

    Parameters
    ----------
    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):

    """
    Import industry data per osm-area necessary to identify DSM-potential.

    Parameters
    ----------
    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):

    """
    Import industry sites necessary to identify DSM-potential.

    Parameters
    ----------
    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):

    """
    Import industry sites necessary to identify DSM-potential.

    Parameters
    ----------
    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, scenario, 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
    scenarios = config.settings()["egon-data"]["--scenarios"]

    dsm = pd.DataFrame(
        columns=["bus_id", "scenario_name", "p_set", "application", "id"]
    )

    # scenario eGon2035
    if "eGon2035" in scenarios:
        dsm_2035 = calc_ind_site_timeseries("eGon2035").reset_index()
        dsm = pd.concat([dsm, dsm_2035], ignore_index=True)
    # scenario eGon100RE
    if "eGon100RE" in scenarios:
        dsm_100 = calc_ind_site_timeseries("eGon100RE").reset_index()
        dsm = pd.concat([dsm, dsm_100], ignore_index=True)

    dsm.index = range(len(dsm))
    # 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

    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

    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="EPSG:4326",
    )
    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",
        geometry="geom",
    )
    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",
        geometry="geom",
    )
    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",
        geometry="geom",
    )
    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",
        geometry="geom",
    )
    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",
        geometry="geom",
    )
    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",
        geometry="geom",
    )
    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()

openego / eGon-data

data.datasets.DSM_cts_ind.create_table() A last analyzed 2025-06-24 07:50 UTC

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data.datasets.DSM_cts_ind.create_table() A
last analyzed 2025-06-24 07:50 UTC
