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# -*- coding: utf-8 -*- |
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# This script is part of eGon-data. |
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# license text - to be added. |
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""" |
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Central module containing all code creating with district heating areas. |
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This module obtains the information from the census tables and the heat demand |
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densities, demarcates so the current and future district heating areas. In the |
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end it saves them in the database. |
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""" |
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import datetime |
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import json |
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import os |
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# for metadata creation |
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import time |
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from geoalchemy2.types import Geometry |
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from matplotlib import pyplot as plt |
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from shapely.geometry.multipolygon import MultiPolygon |
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from shapely.geometry.polygon import Polygon |
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# packages for ORM class definition |
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from sqlalchemy import Column, Float, ForeignKey, Integer, Sequence, String |
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from sqlalchemy.ext.declarative import declarative_base |
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import geopandas as gpd |
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import pandas as pd |
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from egon.data import config, db |
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from egon.data.datasets import Dataset |
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from egon.data.datasets.district_heating_areas.plot import ( |
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plot_heat_density_sorted, |
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) |
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from egon.data.datasets.scenario_parameters import ( |
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EgonScenario, |
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get_sector_parameters, |
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) |
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from egon.data.metadata import context, license_ccby, meta_metadata, sources |
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# import time |
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# class for airflow task management (and version control) |
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class DistrictHeatingAreas(Dataset): |
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""" |
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Create district heating grids for all scenarios |
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This dataset creates district heating grids for each scenario based on a defined |
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district heating share, annual heat demands calcultaed within |
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:py:class:`HeatDemandImport <egon.data.datasets.heat_demand.HeatDemandImport>` |
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and information on existing heating grids from census :py:class:`ZensusMiscellaneous <egon.data.datasets.zensus.ZensusMiscellaneous>` |
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First the tables are created using :py:func:`create_tables`. Afterwards, the |
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distict heating grids for each scenario are created and inserted into the database |
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by applying the function :py:func:`district_heating_areas` |
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*Dependencies* |
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* :py:class:`HeatDemandImport <egon.data.datasets.heat_demand.HeatDemandImport>` |
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* :py:class:`ZensusMiscellaneous <egon.data.datasets.zensus.ZensusMiscellaneous>` |
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* :py:class:`ScenarioParameters <egon.data.datasets.scenario_parameters.ScenarioParameters>` |
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*Resulting tables* |
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* :py:class:`demand.egon_map_zensus_district_heating_areas <egon.data.datasets.district_heating_areas.MapZensusDistrictHeatingAreas>` |
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is created and filled |
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* :py:class:`demand.egon_district_heating_areas <egon.data.datasets.district_heating_areas.EgonDistrictHeatingAreas>` is created and filled |
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""" |
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#: |
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name: str = "district-heating-areas" |
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#: |
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version: str = "0.0.3" |
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def __init__(self, dependencies): |
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super().__init__( |
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name=self.name, |
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# version=self.target_files + "_0.0", |
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version=self.version, # maybe rethink the naming |
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dependencies=dependencies, |
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tasks=(create_tables, demarcation), |
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) |
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Base = declarative_base() |
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# definition of classes for saving data in the database |
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class MapZensusDistrictHeatingAreas(Base): |
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__tablename__ = "egon_map_zensus_district_heating_areas" |
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__table_args__ = {"schema": "demand"} |
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id = Column( |
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Integer, |
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Sequence("map_zensus_district_heating_areas_seq", schema="demand"), |
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server_default=Sequence( |
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"map_zensus_district_heating_areas_seq", schema="demand" |
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).next_value(), |
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primary_key=True, |
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) |
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area_id = Column(Integer) |
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scenario = Column(String, ForeignKey(EgonScenario.name)) |
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zensus_population_id = Column(Integer) |
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class EgonDistrictHeatingAreas(Base): |
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__tablename__ = "egon_district_heating_areas" |
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__table_args__ = {"schema": "demand"} |
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id = Column( |
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Integer, |
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Sequence("district_heating_areas_seq", schema="demand"), |
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server_default=Sequence( |
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"district_heating_areas_seq", schema="demand" |
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).next_value(), |
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primary_key=True, |
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) |
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area_id = Column(Integer) |
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scenario = Column(String, ForeignKey(EgonScenario.name)) |
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geom_polygon = Column(Geometry("MULTIPOLYGON", 3035)) |
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residential_and_service_demand = Column(Float) |
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def create_tables(): |
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"""Create tables for district heating areas |
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Returns |
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------- |
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None |
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""" |
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# Create schema |
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db.execute_sql("CREATE SCHEMA IF NOT EXISTS demand;") |
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# Drop tables |
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db.execute_sql( |
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"""DROP TABLE IF EXISTS |
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demand.egon_district_heating_areas CASCADE;""" |
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) |
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db.execute_sql( |
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"""DROP TABLE IF EXISTS |
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demand.egon_map_zensus_district_heating_areas CASCADE;""" |
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) |
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db.execute_sql( |
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"""DROP TABLE IF EXISTS |
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demand.district_heating_areas CASCADE;""" |
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) |
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db.execute_sql( |
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"""DROP TABLE IF EXISTS |
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demand.map_zensus_district_heating_areas CASCADE;""" |
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) |
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# Drop sequences |
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db.execute_sql( |
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"""DROP SEQUENCE IF EXISTS |
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demand.district_heating_areas_seq CASCADE;""" |
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) |
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db.execute_sql( |
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"""DROP SEQUENCE IF EXISTS |
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demand.egon_map_zensus_district_heating_areas_seq CASCADE;""" |
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) |
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engine = db.engine() |
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EgonDistrictHeatingAreas.__table__.create(bind=engine, checkfirst=True) |
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MapZensusDistrictHeatingAreas.__table__.create( |
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bind=engine, checkfirst=True |
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) |
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# Methods used are explained here: |
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# https://geopandas.org/docs/user_guide/geometric_manipulations.html |
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def load_census_data(minimum_connection_rate=0.3): |
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""" |
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Load the heating type information from the census database table. |
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The census apartment and the census building table contains information |
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about the heating type. The information are loaded from the apartment |
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table, because they might be more useful when it comes to the estimation of |
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the connection rates. Only cells with a connection rate equal to or larger |
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than 30% (based on the census apartment data) are included in the returned |
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district_heat GeoDataFrame. |
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Parameters |
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---------- |
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None |
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Returns |
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------- |
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district_heat: geopandas.geodataframe.GeoDataFrame |
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polygons (hectare cells) with district heat information |
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heating_type: geopandas.geodataframe.GeoDataFrame |
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polygons (hectare cells) with the number of flats having heating |
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type information |
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Notes |
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----- |
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The census contains only information on residential buildings. |
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Therefore, also connection rate of the residential buildings can be |
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estimated. |
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TODO |
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---- |
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- ONLY load cells with flats.quantity_q <2 |
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- remove heating_type return, if not needed |
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- store less columns in the district_heat (pop.geom_point), |
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drop characteristics_text after use |
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""" |
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# only census cells where egon-data has a heat demand are considered |
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district_heat = db.select_geodataframe( |
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"""SELECT flats.zensus_population_id, flats.characteristics_text, |
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flats.quantity, flats.quantity_q, pop.geom_point, |
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pop.geom AS geom_polygon |
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FROM society.egon_destatis_zensus_apartment_per_ha AS flats |
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JOIN society.destatis_zensus_population_per_ha AS pop |
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ON flats.zensus_population_id = pop.id |
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AND flats.characteristics_text = 'Fernheizung (Fernwärme)' |
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AND flats.zensus_population_id IN |
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(SELECT zensus_population_id FROM demand.egon_peta_heat);""", |
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index_col="zensus_population_id", |
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geom_col="geom_polygon", |
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) |
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heating_type = db.select_geodataframe( |
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"""SELECT flats.zensus_population_id, |
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SUM(flats.quantity) AS quantity, pop.geom AS geom_polygon |
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FROM society.egon_destatis_zensus_apartment_per_ha AS flats |
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JOIN society.destatis_zensus_population_per_ha AS pop |
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ON flats.zensus_population_id = pop.id |
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AND flats.attribute = 'HEIZTYP' |
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AND flats.zensus_population_id IN |
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(SELECT zensus_population_id FROM demand.egon_peta_heat) |
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GROUP BY flats.zensus_population_id, pop.geom;""", |
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index_col="zensus_population_id", |
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geom_col="geom_polygon", |
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) |
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# district_heat.to_file(results_path+"dh.shp") |
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# heating_type.to_file(results_path+"heating.shp") |
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# calculate the connection rate for all census cells with DH |
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# adding it to the district_heat geodataframe |
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district_heat["connection_rate"] = district_heat["quantity"].div( |
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heating_type["quantity"] |
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)[district_heat.index] |
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# district_heat.head |
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# district_heat['connection_rate'].describe() |
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district_heat = district_heat[ |
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district_heat["connection_rate"] >= minimum_connection_rate |
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] |
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# district_heat.columns |
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return district_heat, heating_type |
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def load_heat_demands(scenario_name): |
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""" |
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Load scenario specific heat demand data from the local database. |
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Parameters |
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---------- |
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scenario_name: str |
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name of the scenario studied |
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Returns |
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------- |
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heat_demand: geopandas.geodataframe.GeoDataFrame |
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polygons (hectare cells) with heat demand data |
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""" |
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# load the total heat demand (residential plus service sector) |
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heat_demand = db.select_geodataframe( |
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f"""SELECT demand.zensus_population_id, |
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SUM(demand.demand) AS residential_and_service_demand, |
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pop.geom AS geom_polygon |
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FROM demand.egon_peta_heat AS demand |
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JOIN society.destatis_zensus_population_per_ha AS pop |
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ON demand.zensus_population_id = pop.id |
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AND demand.scenario = '{scenario_name}' |
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GROUP BY demand.zensus_population_id, pop.geom;""", |
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index_col="zensus_population_id", |
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geom_col="geom_polygon", |
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) |
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return heat_demand |
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def select_high_heat_demands(heat_demand): |
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""" |
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Take heat demand cells and select cells with higher heat demand. |
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Those can be used to identify prospective district heating supply areas. |
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Parameters |
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---------- |
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heat_demand: geopandas.geodataframe.GeoDataFrame |
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dataset of heat demand cells. |
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Returns |
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------- |
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high_heat_demand: geopandas.geodataframe.GeoDataFrame |
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polygons (hectare cells) with heat demands high enough to be |
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potentially high enough to be in a district heating area |
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""" |
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318
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|
|
# starting point are 100 or 200 GJ/ (ha a), converted into MWh |
|
319
|
|
|
minimum_demand = 100 / 3.6 |
|
320
|
|
|
|
|
321
|
|
|
high_heat_demand = heat_demand[ |
|
322
|
|
|
heat_demand["residential_and_service_demand"] > minimum_demand |
|
323
|
|
|
] |
|
324
|
|
|
# high_heat_demand.head() |
|
325
|
|
|
# print(high_heat_demand.area) # all cells are 10,000 m² |
|
326
|
|
|
|
|
327
|
|
|
return high_heat_demand |
|
328
|
|
|
|
|
329
|
|
|
|
|
330
|
|
|
def area_grouping( |
|
331
|
|
|
raw_polygons, |
|
332
|
|
|
distance=200, |
|
333
|
|
|
minimum_total_demand=None, |
|
334
|
|
|
maximum_total_demand=None, |
|
335
|
|
|
): |
|
336
|
|
|
""" |
|
337
|
|
|
Group polygons which are close to each other. |
|
338
|
|
|
|
|
339
|
|
|
This function creates buffers around the given cell polygons (called |
|
340
|
|
|
"raw_polygons") and unions the intersecting buffer polygons. Afterwards, it |
|
341
|
|
|
unions the cell polygons which are within one unified buffer polygon. |
|
342
|
|
|
If requested, the cells being in areas fulfilling the minimum heat demand |
|
343
|
|
|
criterium are selected. |
|
344
|
|
|
|
|
345
|
|
|
Parameters |
|
346
|
|
|
---------- |
|
347
|
|
|
raw_polygons: geopandas.geodataframe.GeoDataFrame |
|
348
|
|
|
polygons to be grouped. |
|
349
|
|
|
|
|
350
|
|
|
distance: integer |
|
351
|
|
|
distance for buffering |
|
352
|
|
|
|
|
353
|
|
|
minimum_total_demand: integer |
|
354
|
|
|
optional minimum total heat demand to achieve a minimum size of areas |
|
355
|
|
|
|
|
356
|
|
|
maximal_total_demand: integer |
|
357
|
|
|
optional maximal total heat demand per area, if demand is higher the |
|
358
|
|
|
area is cut at nuts3 borders |
|
359
|
|
|
|
|
360
|
|
|
|
|
361
|
|
|
Returns |
|
362
|
|
|
------- |
|
363
|
|
|
join: geopandas.geodataframe.GeoDataFrame |
|
364
|
|
|
cell polygons with area id |
|
365
|
|
|
|
|
366
|
|
|
""" |
|
367
|
|
|
|
|
368
|
|
|
buffer_distance = distance + 1 |
|
369
|
|
|
cell_buffers = raw_polygons.copy() |
|
370
|
|
|
cell_buffers["geom_polygon"] = cell_buffers["geom_polygon"].buffer( |
|
371
|
|
|
buffer_distance |
|
372
|
|
|
) |
|
373
|
|
|
# print(cell_buffers.area) |
|
374
|
|
|
|
|
375
|
|
|
# create a shapely Multipolygon which is split into a list |
|
376
|
|
|
buffer_polygons = list(cell_buffers["geom_polygon"].unary_union.geoms) |
|
377
|
|
|
|
|
378
|
|
|
# change the data type into geopandas geodataframe |
|
379
|
|
|
buffer_polygons_gdf = gpd.GeoDataFrame(geometry=buffer_polygons, crs=3035) |
|
380
|
|
|
# buffer_polygons_gdf.plot() |
|
381
|
|
|
|
|
382
|
|
|
# Join studied cells with buffer polygons |
|
383
|
|
|
columnname = "area_id" |
|
384
|
|
|
join = gpd.sjoin( |
|
385
|
|
|
raw_polygons, buffer_polygons_gdf, how="inner", predicate="intersects" |
|
386
|
|
|
) |
|
387
|
|
|
|
|
388
|
|
|
join = join.rename({"index_right": columnname}, axis=1) |
|
389
|
|
|
# join.plot(column=columnname) |
|
390
|
|
|
|
|
391
|
|
|
# minimum total heat demand for the areas with minimum criterium |
|
392
|
|
|
if ( |
|
393
|
|
|
minimum_total_demand is not None |
|
394
|
|
|
and "residential_and_service_demand" in raw_polygons.columns |
|
395
|
|
|
): |
|
396
|
|
|
# total_heat_demand = join.dissolve('area_id', aggfunc='sum') |
|
397
|
|
|
# type(large_areas) |
|
398
|
|
|
# filtered = join.groupby(['area_id'])[ |
|
399
|
|
|
# 'residential_and_service_demand'].agg('sum') > 0.7 |
|
400
|
|
|
large_areas = gpd.GeoDataFrame( |
|
401
|
|
|
join.groupby(["area_id"])["residential_and_service_demand"].agg( |
|
402
|
|
|
"sum" |
|
403
|
|
|
) |
|
404
|
|
|
) |
|
405
|
|
|
# large_areas = large_areas[large_areas[ |
|
406
|
|
|
# 'residential_and_service_demand'] > minimum_total_demand] |
|
407
|
|
|
large_areas = ( |
|
408
|
|
|
large_areas["residential_and_service_demand"] |
|
409
|
|
|
> minimum_total_demand |
|
410
|
|
|
) |
|
411
|
|
|
join = join[join.area_id.isin(large_areas[large_areas].index)] |
|
412
|
|
|
|
|
413
|
|
|
elif ( |
|
414
|
|
|
minimum_total_demand is not None |
|
415
|
|
|
and "residential_and_service_demand" not in raw_polygons.columns |
|
416
|
|
|
): |
|
417
|
|
|
print( |
|
418
|
|
|
"""The minimum total heat demand criterium can only be applied |
|
419
|
|
|
on geodataframe having a column named |
|
420
|
|
|
'residential_and_service_demand' """ |
|
421
|
|
|
) |
|
422
|
|
|
|
|
423
|
|
|
if ( |
|
424
|
|
|
maximum_total_demand |
|
425
|
|
|
and "residential_and_service_demand" in join.columns |
|
426
|
|
|
): |
|
427
|
|
|
|
|
428
|
|
|
huge_areas_index = ( |
|
429
|
|
|
join.groupby("area_id").residential_and_service_demand.sum() |
|
430
|
|
|
> maximum_total_demand |
|
431
|
|
|
) |
|
432
|
|
|
|
|
433
|
|
|
cells_in_huge_areas = join[ |
|
434
|
|
|
join.area_id.isin(huge_areas_index[huge_areas_index].index) |
|
435
|
|
|
] |
|
436
|
|
|
|
|
437
|
|
|
nuts3_boundaries = db.select_geodataframe( |
|
438
|
|
|
""" |
|
439
|
|
|
SELECT gen, geometry as geom FROM boundaries.vg250_krs |
|
440
|
|
|
""" |
|
441
|
|
|
) |
|
442
|
|
|
join_2 = gpd.sjoin( |
|
443
|
|
|
cells_in_huge_areas, |
|
444
|
|
|
nuts3_boundaries, |
|
445
|
|
|
how="inner", |
|
446
|
|
|
predicate="intersects", |
|
447
|
|
|
) |
|
448
|
|
|
|
|
449
|
|
|
join = join.drop(cells_in_huge_areas.index) |
|
450
|
|
|
|
|
451
|
|
|
max_area_id = join.area_id.max() |
|
452
|
|
|
|
|
453
|
|
|
join_2["area_id"] = join_2.index_right + max_area_id + 1 |
|
454
|
|
|
|
|
455
|
|
|
join = pd.concat( |
|
456
|
|
|
[ |
|
457
|
|
|
join, |
|
458
|
|
|
join_2[ |
|
459
|
|
|
[ |
|
460
|
|
|
"zensus_population_id", |
|
461
|
|
|
"residential_and_service_demand", |
|
462
|
|
|
"geom_polygon", |
|
463
|
|
|
"area_id", |
|
464
|
|
|
] |
|
465
|
|
|
], |
|
466
|
|
|
], |
|
467
|
|
|
ignore_index=True, |
|
468
|
|
|
) |
|
469
|
|
|
|
|
470
|
|
|
return join |
|
471
|
|
|
|
|
472
|
|
|
|
|
473
|
|
|
def district_heating_areas(scenario_name, plotting=False): |
|
474
|
|
|
""" |
|
475
|
|
|
Create scenario specific district heating areas considering on census data. |
|
476
|
|
|
|
|
477
|
|
|
This function loads the district heating share from the scenario table and |
|
478
|
|
|
demarcate the scenario specific district heating areas. To do so it |
|
479
|
|
|
uses the census data on flats currently supplied with district heat, which |
|
480
|
|
|
are supplied selected first, if the estimated connection rate >= 30%. |
|
481
|
|
|
|
|
482
|
|
|
All scenarios use the Prospective Supply Districts (PSDs) made for the |
|
483
|
|
|
eGon2035 scenario to identify the areas where additional district heating |
|
484
|
|
|
supply is feasible. One PSD dataset is to defined which is constant over |
|
485
|
|
|
the years to allow comparisons. Moreover, it is |
|
486
|
|
|
assumed that the eGon2035 PSD dataset is suitable, even though the heat |
|
487
|
|
|
demands will continue to decrease from 2035 to 2050, because district |
|
488
|
|
|
heating systems will be to planned and built before 2050, to exist in 2050. |
|
489
|
|
|
|
|
490
|
|
|
It is assumed that the connection rate in cells with district heating will |
|
491
|
|
|
be a 100%. That is because later in project the number of buildings per |
|
492
|
|
|
cell will be used and connection rates not being 0 or 100% will create |
|
493
|
|
|
buildings which are not fully supplied by one technology. |
|
494
|
|
|
|
|
495
|
|
|
The cell polygons which carry information (like heat demand etc.) are |
|
496
|
|
|
grouped into areas which are close to each other. |
|
497
|
|
|
Only cells with a minimum heat demand density (e.g. >100 GJ/(ha a)) are |
|
498
|
|
|
considered when creating PSDs. Therefore, the select_high_heat_demands() |
|
499
|
|
|
function is used. There is minimum heat demand per PSDs to achieve a |
|
500
|
|
|
certain size. |
|
501
|
|
|
While the grouping buffer for the creation of Prospective Supply Districts |
|
502
|
|
|
(PSDs) is 200m as in the sEEnergies project, the buffer for grouping census |
|
503
|
|
|
data cell with an estimated connection rate >= 30% is 500m. |
|
504
|
|
|
The 500m buffer is also used when the resulting district heating areas are |
|
505
|
|
|
grouped, because they are built upon the existing district heating systems. |
|
506
|
|
|
|
|
507
|
|
|
To reduce the final number of district heating areas having the size of |
|
508
|
|
|
only one hectare, the minimum heat demand critrium is also applied when |
|
509
|
|
|
grouping the cells with census data on district heat. |
|
510
|
|
|
|
|
511
|
|
|
To avoid huge district heating areas, as they appear in the Ruhr area, |
|
512
|
|
|
district heating areas with an annual demand > 4,000,000 MWh are split |
|
513
|
|
|
by nuts3 boundaries. This as set as maximum_total_demand of the |
|
514
|
|
|
area_grouping function. |
|
515
|
|
|
|
|
516
|
|
|
|
|
517
|
|
|
Parameters |
|
518
|
|
|
---------- |
|
519
|
|
|
scenario_name: str |
|
520
|
|
|
name of scenario to be studies |
|
521
|
|
|
|
|
522
|
|
|
plotting: boolean |
|
523
|
|
|
if True, figure showing the heat demand density curve will be created |
|
524
|
|
|
|
|
525
|
|
|
|
|
526
|
|
|
Returns |
|
527
|
|
|
------- |
|
528
|
|
|
None |
|
529
|
|
|
|
|
530
|
|
|
Notes |
|
531
|
|
|
----- |
|
532
|
|
|
None |
|
533
|
|
|
|
|
534
|
|
|
TODO |
|
535
|
|
|
---- |
|
536
|
|
|
Do "area_grouping(load_census_data()[0])" only once, not for all |
|
537
|
|
|
scenarios. |
|
538
|
|
|
|
|
539
|
|
|
Check the applied buffer distances, find a justification for the |
|
540
|
|
|
documentation |
|
541
|
|
|
|
|
542
|
|
|
""" |
|
543
|
|
|
|
|
544
|
|
|
# Load district heating shares from the scenario table |
|
545
|
|
|
if scenario_name == "eGon2015": |
|
546
|
|
|
district_heating_share = 0.08 |
|
547
|
|
|
else: |
|
548
|
|
|
heat_parameters = get_sector_parameters("heat", scenario=scenario_name) |
|
549
|
|
|
|
|
550
|
|
|
district_heating_share = heat_parameters["DE_district_heating_share"] |
|
551
|
|
|
|
|
552
|
|
|
minimum_connection_rate = 0.3 |
|
553
|
|
|
|
|
554
|
|
|
# Adjust minimum connection rate for status2019, and other statusquo scn |
|
555
|
|
|
# otherwise the existing district heating grids would have too much demand |
|
556
|
|
|
# if scenario_name == "status2019": |
|
557
|
|
|
if "status" in scenario_name: |
|
558
|
|
|
minimum_connection_rate = 0.6 |
|
559
|
|
|
|
|
560
|
|
|
# heat_demand is scenario specific |
|
561
|
|
|
heat_demand_cells = load_heat_demands(scenario_name) |
|
562
|
|
|
|
|
563
|
|
|
# Firstly, supply the cells which already have district heating according |
|
564
|
|
|
# to 2011 Census data and which are within likely dh areas (created |
|
565
|
|
|
# by the area grouping function), load only the first returned result: [0] |
|
566
|
|
|
min_hd_census = 10000 / 3.6 # in MWh |
|
567
|
|
|
|
|
568
|
|
|
census_plus_heat_demand = load_census_data( |
|
569
|
|
|
minimum_connection_rate=minimum_connection_rate |
|
570
|
|
|
)[0].copy() |
|
571
|
|
|
census_plus_heat_demand["residential_and_service_demand"] = ( |
|
572
|
|
|
heat_demand_cells.loc[ |
|
573
|
|
|
census_plus_heat_demand.index.values, |
|
574
|
|
|
"residential_and_service_demand", |
|
575
|
|
|
] |
|
576
|
|
|
) |
|
577
|
|
|
|
|
578
|
|
|
cells = area_grouping( |
|
579
|
|
|
census_plus_heat_demand, |
|
580
|
|
|
distance=500, |
|
581
|
|
|
minimum_total_demand=min_hd_census, |
|
582
|
|
|
) |
|
583
|
|
|
# cells.groupby("area_id").size().sort_values() |
|
584
|
|
|
|
|
585
|
|
|
total_district_heat = ( |
|
586
|
|
|
heat_demand_cells["residential_and_service_demand"].sum() |
|
587
|
|
|
* district_heating_share |
|
588
|
|
|
) |
|
589
|
|
|
|
|
590
|
|
|
diff = total_district_heat - cells["residential_and_service_demand"].sum() |
|
591
|
|
|
|
|
592
|
|
|
assert ( |
|
593
|
|
|
diff > 0 |
|
594
|
|
|
), """The chosen district heating share in combination with the heat |
|
595
|
|
|
demand reduction leads to an amount of district heat which is |
|
596
|
|
|
lower than the current one. This case is not implemented yet.""" |
|
597
|
|
|
|
|
598
|
|
|
# Secondly, supply the cells with the highest heat demand not having |
|
599
|
|
|
# district heating yet |
|
600
|
|
|
# ASSUMPTION HERE: 2035 HD defined the PSDs |
|
601
|
|
|
min_hd = 10000 / 3.6 |
|
602
|
|
|
PSDs = area_grouping( |
|
603
|
|
|
select_high_heat_demands(load_heat_demands(scenario_name)), |
|
604
|
|
|
distance=200, |
|
605
|
|
|
minimum_total_demand=min_hd, |
|
606
|
|
|
) |
|
607
|
|
|
|
|
608
|
|
|
# PSDs.groupby("area_id").size().sort_values() |
|
609
|
|
|
|
|
610
|
|
|
# select all cells not already suppied with district heat |
|
611
|
|
|
new_areas = heat_demand_cells[~heat_demand_cells.index.isin(cells.index)] |
|
612
|
|
|
# sort by heat demand density |
|
613
|
|
|
new_areas = new_areas[new_areas.index.isin(PSDs.index)].sort_values( |
|
614
|
|
|
"residential_and_service_demand", ascending=False |
|
615
|
|
|
) |
|
616
|
|
|
new_areas["Cumulative_Sum"] = ( |
|
617
|
|
|
new_areas.residential_and_service_demand.cumsum() |
|
618
|
|
|
) |
|
619
|
|
|
# select cells to be supplied with district heating until district |
|
620
|
|
|
# heating share is reached |
|
621
|
|
|
new_areas = new_areas[new_areas["Cumulative_Sum"] <= diff] |
|
622
|
|
|
|
|
623
|
|
|
print( |
|
624
|
|
|
f"""Minimum heat demand density for cells with new district heat |
|
625
|
|
|
supply in scenario {scenario_name} is |
|
626
|
|
|
{new_areas.residential_and_service_demand.tail(1).values[0]} |
|
627
|
|
|
MWh / (ha a).""" |
|
628
|
|
|
) |
|
629
|
|
|
print( |
|
630
|
|
|
f"""Number of cells with new district heat supply in scenario |
|
631
|
|
|
{scenario_name} is {len(new_areas)}.""" |
|
632
|
|
|
) |
|
633
|
|
|
|
|
634
|
|
|
# check = gpd.GeoDataFrame( |
|
635
|
|
|
# cells[['residential_and_service_demand', 'geom_polygon']].append( |
|
636
|
|
|
# new_areas[['residential_and_service_demand', 'geom_polygon']]), |
|
637
|
|
|
# geometry='geom_polygon') |
|
638
|
|
|
|
|
639
|
|
|
# group the resulting scenario specific district heating areas |
|
640
|
|
|
scenario_dh_area = area_grouping( |
|
641
|
|
|
pd.concat( |
|
642
|
|
|
[ |
|
643
|
|
|
cells[["residential_and_service_demand", "geom_polygon"]], |
|
644
|
|
|
new_areas[["residential_and_service_demand", "geom_polygon"]], |
|
645
|
|
|
] |
|
646
|
|
|
).reset_index(), |
|
647
|
|
|
distance=500, |
|
648
|
|
|
maximum_total_demand=4e6, |
|
649
|
|
|
) |
|
650
|
|
|
scenario_dh_area.loc[:, "zensus_population_id"] = scenario_dh_area.loc[ |
|
651
|
|
|
:, "zensus_population_id" |
|
652
|
|
|
].astype(int) |
|
653
|
|
|
# scenario_dh_area.plot(column = "area_id") |
|
654
|
|
|
|
|
655
|
|
|
scenario_dh_area.groupby("area_id").size().sort_values() |
|
656
|
|
|
scenario_dh_area.residential_and_service_demand.sum() |
|
657
|
|
|
# scenario_dh_area.sort_index() |
|
658
|
|
|
# cells[cells.index==1416974] |
|
659
|
|
|
|
|
660
|
|
|
# store the results in the database |
|
661
|
|
|
scenario_dh_area["scenario"] = scenario_name |
|
662
|
|
|
|
|
663
|
|
|
db.execute_sql( |
|
664
|
|
|
f"""DELETE FROM demand.egon_map_zensus_district_heating_areas |
|
665
|
|
|
WHERE scenario = '{scenario_name}'""" |
|
666
|
|
|
) |
|
667
|
|
|
scenario_dh_area[["scenario", "area_id", "zensus_population_id"]].to_sql( |
|
668
|
|
|
"egon_map_zensus_district_heating_areas", |
|
669
|
|
|
schema="demand", |
|
670
|
|
|
con=db.engine(), |
|
671
|
|
|
if_exists="append", |
|
672
|
|
|
index=False, |
|
673
|
|
|
) |
|
674
|
|
|
|
|
675
|
|
|
# Create polygons around the grouped cells and store them in the database |
|
676
|
|
|
# join.dissolve(columnname).convex_hull.plot() # without holes, too big |
|
677
|
|
|
areas_dissolved = scenario_dh_area.dissolve("area_id", aggfunc="sum") |
|
678
|
|
|
areas_dissolved["scenario"] = scenario_name |
|
679
|
|
|
|
|
680
|
|
|
areas_dissolved["geom_polygon"] = [ |
|
681
|
|
|
MultiPolygon([feature]) if type(feature) == Polygon else feature |
|
682
|
|
|
for feature in areas_dissolved["geom_polygon"] |
|
683
|
|
|
] |
|
684
|
|
|
# type(areas_dissolved["geom"][0]) |
|
685
|
|
|
# print(type(areas_dissolved)) |
|
686
|
|
|
# print(areas_dissolved.head()) |
|
687
|
|
|
|
|
688
|
|
|
if len(areas_dissolved[areas_dissolved.area == 100 * 100]) > 0: |
|
689
|
|
|
print( |
|
690
|
|
|
f"""Number of district heating areas of single zensus cells: |
|
691
|
|
|
{len(areas_dissolved[areas_dissolved.area == 100*100]) |
|
692
|
|
|
}""" |
|
693
|
|
|
) |
|
694
|
|
|
# print(f"""District heating areas ids of single zensus cells in |
|
695
|
|
|
# district heating areas: |
|
696
|
|
|
# {areas_dissolved[areas_dissolved.area == 100*100].index.values |
|
697
|
|
|
# }""") |
|
698
|
|
|
# print(f"""Zensus_population_ids of single zensus cells |
|
699
|
|
|
# in district heating areas: |
|
700
|
|
|
# {scenario_dh_area[scenario_dh_area.area_id.isin( |
|
701
|
|
|
# areas_dissolved[areas_dissolved.area == 100*100].index.values |
|
702
|
|
|
# )].index.values}""") |
|
703
|
|
|
|
|
704
|
|
|
db.execute_sql( |
|
705
|
|
|
f"""DELETE FROM demand.egon_district_heating_areas |
|
706
|
|
|
WHERE scenario = '{scenario_name}'""" |
|
707
|
|
|
) |
|
708
|
|
|
areas_dissolved.reset_index().drop( |
|
709
|
|
|
"zensus_population_id", axis="columns" |
|
710
|
|
|
).to_postgis( |
|
711
|
|
|
"egon_district_heating_areas", |
|
712
|
|
|
schema="demand", |
|
713
|
|
|
con=db.engine(), |
|
714
|
|
|
if_exists="append", |
|
715
|
|
|
) |
|
716
|
|
|
# Alternative: |
|
717
|
|
|
# join.groupby("columnname").demand.sum() |
|
718
|
|
|
# add the sorted heat demand density curve |
|
719
|
|
|
no_district_heating = heat_demand_cells[ |
|
720
|
|
|
~heat_demand_cells.index.isin(scenario_dh_area.index) |
|
721
|
|
|
] |
|
722
|
|
|
collection = pd.concat( |
|
723
|
|
|
[ |
|
724
|
|
|
cells.sort_values( |
|
725
|
|
|
"residential_and_service_demand", ascending=False |
|
726
|
|
|
), |
|
727
|
|
|
new_areas.sort_values( |
|
728
|
|
|
"residential_and_service_demand", ascending=False |
|
729
|
|
|
), |
|
730
|
|
|
no_district_heating.sort_values( |
|
731
|
|
|
"residential_and_service_demand", ascending=False |
|
732
|
|
|
), |
|
733
|
|
|
], |
|
734
|
|
|
ignore_index=True, |
|
735
|
|
|
) |
|
736
|
|
|
collection["Cumulative_Sum"] = ( |
|
737
|
|
|
collection.residential_and_service_demand.cumsum() |
|
738
|
|
|
) / 1000000 |
|
739
|
|
|
if plotting: |
|
740
|
|
|
plot_heat_density_sorted({scenario_name: collection}, scenario_name) |
|
741
|
|
|
|
|
742
|
|
|
return collection |
|
743
|
|
|
|
|
744
|
|
|
|
|
745
|
|
|
def add_metadata(): |
|
746
|
|
|
""" |
|
747
|
|
|
Writes metadata JSON string into table comment. |
|
748
|
|
|
|
|
749
|
|
|
TODO |
|
750
|
|
|
---- |
|
751
|
|
|
|
|
752
|
|
|
Meta data must be check and adjusted to the egon_data standard: |
|
753
|
|
|
- Add context |
|
754
|
|
|
- authors and institutions |
|
755
|
|
|
|
|
756
|
|
|
""" |
|
757
|
|
|
|
|
758
|
|
|
# Prepare variables |
|
759
|
|
|
license_district_heating_areas = [ |
|
760
|
|
|
license_ccby("© Europa-Universität Flensburg") |
|
761
|
|
|
] |
|
762
|
|
|
|
|
763
|
|
|
# Metadata creation for district heating areas (polygons) |
|
764
|
|
|
meta = { |
|
765
|
|
|
"name": "district_heating_areas_metadata", |
|
766
|
|
|
"title": "eGo^n scenario-specific future district heating areas", |
|
767
|
|
|
"description": "Modelled future district heating areas for " |
|
768
|
|
|
"the supply of residential and service-sector heat demands", |
|
769
|
|
|
"language": ["EN"], |
|
770
|
|
|
"publicationDate": datetime.date.today().isoformat(), |
|
771
|
|
|
"context": context(), |
|
772
|
|
|
"spatial": {"location": "", "extent": "Germany", "resolution": ""}, |
|
773
|
|
|
"sources": [ |
|
774
|
|
|
sources()["peta"], |
|
775
|
|
|
sources()["egon-data"], |
|
776
|
|
|
sources()["zensus"], |
|
777
|
|
|
sources()["vg250"], |
|
778
|
|
|
], |
|
779
|
|
|
"resources": [ |
|
780
|
|
|
{ |
|
781
|
|
|
"profile": "tabular-data-resource", |
|
782
|
|
|
"name": "egon_district_heating_areas", |
|
783
|
|
|
"path": "", |
|
784
|
|
|
"format": "PostgreSQL", |
|
785
|
|
|
"encoding": "UTF-8", |
|
786
|
|
|
"schema": { |
|
787
|
|
|
"fields": [ |
|
788
|
|
|
{ |
|
789
|
|
|
"name": "id", |
|
790
|
|
|
"description": "Unique identifier", |
|
791
|
|
|
"type": "serial", |
|
792
|
|
|
"unit": "none", |
|
793
|
|
|
}, |
|
794
|
|
|
{ |
|
795
|
|
|
"name": "area_id", |
|
796
|
|
|
"description": "District heating area id", |
|
797
|
|
|
"type": "integer", |
|
798
|
|
|
"unit": "none", |
|
799
|
|
|
}, |
|
800
|
|
|
{ |
|
801
|
|
|
"name": "scenario", |
|
802
|
|
|
"description": "scenario name", |
|
803
|
|
|
"type": "text", |
|
804
|
|
|
"unit": "none", |
|
805
|
|
|
}, |
|
806
|
|
|
{ |
|
807
|
|
|
"name": "residential_and_service_demand", |
|
808
|
|
|
"description": "annual heat demand", |
|
809
|
|
|
"type": "double precision", |
|
810
|
|
|
"unit": "MWh", |
|
811
|
|
|
}, |
|
812
|
|
|
{ |
|
813
|
|
|
"name": "geom_polygon", |
|
814
|
|
|
"description": "geo information of multipolygons", |
|
815
|
|
|
"type": "geometry(MULTIPOLYGON, 3035)", |
|
816
|
|
|
"unit": "none", |
|
817
|
|
|
}, |
|
818
|
|
|
], |
|
819
|
|
|
"primaryKey": ["id"], |
|
820
|
|
|
"foreignKeys": [ |
|
821
|
|
|
{ |
|
822
|
|
|
"fields": ["scenario"], |
|
823
|
|
|
"reference": { |
|
824
|
|
|
"resource": "scenario.egon_scenario_parameters", |
|
825
|
|
|
"fields": ["name"], |
|
826
|
|
|
}, |
|
827
|
|
|
} |
|
828
|
|
|
], |
|
829
|
|
|
}, |
|
830
|
|
|
"dialect": {"delimiter": "none", "decimalSeparator": "."}, |
|
831
|
|
|
} |
|
832
|
|
|
], |
|
833
|
|
|
"licenses": license_district_heating_areas, |
|
834
|
|
|
"contributors": [ |
|
835
|
|
|
{ |
|
836
|
|
|
"title": "EvaWie", |
|
837
|
|
|
"email": "http://github.com/EvaWie", |
|
838
|
|
|
"date": time.strftime("%Y-%m-%d"), |
|
839
|
|
|
"object": None, |
|
840
|
|
|
"comment": "Imported data", |
|
841
|
|
|
}, |
|
842
|
|
|
{ |
|
843
|
|
|
"title": "Clara Büttner", |
|
844
|
|
|
"email": "http://github.com/ClaraBuettner", |
|
845
|
|
|
"date": time.strftime("%Y-%m-%d"), |
|
846
|
|
|
"object": None, |
|
847
|
|
|
"comment": "Updated metadata", |
|
848
|
|
|
}, |
|
849
|
|
|
], |
|
850
|
|
|
"metaMetadata": meta_metadata(), |
|
851
|
|
|
} |
|
852
|
|
|
meta_json = "'" + json.dumps(meta) + "'" |
|
853
|
|
|
|
|
854
|
|
|
db.submit_comment(meta_json, "demand", "egon_district_heating_areas") |
|
855
|
|
|
|
|
856
|
|
|
# Metadata creation for "id mapping" table |
|
857
|
|
|
meta = { |
|
858
|
|
|
"name": "map_zensus_district_heating_areas_metadata", |
|
859
|
|
|
"title": "district heating area ids assigned to zensus_population_ids", |
|
860
|
|
|
"description": "Ids of scenario specific future district heating areas" |
|
861
|
|
|
" for supply of residential and service-sector heat demands" |
|
862
|
|
|
" assigned to zensus_population_ids", |
|
863
|
|
|
"language": ["EN"], |
|
864
|
|
|
"publicationDate": datetime.date.today().isoformat(), |
|
865
|
|
|
"context": context(), |
|
866
|
|
|
"spatial": {"location": "", "extent": "Germany", "resolution": ""}, |
|
867
|
|
|
"sources": [ |
|
868
|
|
|
sources()["peta"], |
|
869
|
|
|
sources()["egon-data"], |
|
870
|
|
|
sources()["zensus"], |
|
871
|
|
|
sources()["vg250"], |
|
872
|
|
|
], |
|
873
|
|
|
# Add the license for the map table |
|
874
|
|
|
"resources": [ |
|
875
|
|
|
{ |
|
876
|
|
|
"profile": "tabular-data-resource", |
|
877
|
|
|
"name": "egon_map_zensus_district_heating_areas", |
|
878
|
|
|
"path": "", |
|
879
|
|
|
"format": "PostgreSQL", |
|
880
|
|
|
"encoding": "UTF-8", |
|
881
|
|
|
"schema": { |
|
882
|
|
|
"fields": [ |
|
883
|
|
|
{ |
|
884
|
|
|
"name": "id", |
|
885
|
|
|
"description": "Unique identifier", |
|
886
|
|
|
"type": "serial", |
|
887
|
|
|
"unit": "none", |
|
888
|
|
|
}, |
|
889
|
|
|
{ |
|
890
|
|
|
"name": "area_id", |
|
891
|
|
|
"description": "district heating area id", |
|
892
|
|
|
"type": "integer", |
|
893
|
|
|
"unit": "none", |
|
894
|
|
|
}, |
|
895
|
|
|
{ |
|
896
|
|
|
"name": "scenario", |
|
897
|
|
|
"description": "scenario name", |
|
898
|
|
|
"type": "text", |
|
899
|
|
|
"unit": "none", |
|
900
|
|
|
}, |
|
901
|
|
|
], |
|
902
|
|
|
"primaryKey": ["id"], |
|
903
|
|
|
"foreignKeys": [ |
|
904
|
|
|
{ |
|
905
|
|
|
"fields": ["zensus_population_id"], |
|
906
|
|
|
"reference": { |
|
907
|
|
|
"resource": "society.destatis_zensus_population_per_ha", |
|
908
|
|
|
"fields": ["id"], |
|
909
|
|
|
}, |
|
910
|
|
|
}, |
|
911
|
|
|
{ |
|
912
|
|
|
"fields": ["scenario"], |
|
913
|
|
|
"reference": { |
|
914
|
|
|
"resource": "scenario.egon_scenario_parameters", |
|
915
|
|
|
"fields": ["name"], |
|
916
|
|
|
}, |
|
917
|
|
|
}, |
|
918
|
|
|
], |
|
919
|
|
|
}, |
|
920
|
|
|
"dialect": {"delimiter": "none", "decimalSeparator": "."}, |
|
921
|
|
|
} |
|
922
|
|
|
], |
|
923
|
|
|
"licenses": license_district_heating_areas, |
|
924
|
|
|
"contributors": [ |
|
925
|
|
|
{ |
|
926
|
|
|
"title": "EvaWie", |
|
927
|
|
|
"email": "http://github.com/EvaWie", |
|
928
|
|
|
"date": time.strftime("%Y-%m-%d"), |
|
929
|
|
|
"object": None, |
|
930
|
|
|
"comment": "Imported data", |
|
931
|
|
|
}, |
|
932
|
|
|
{ |
|
933
|
|
|
"title": "Clara Büttner", |
|
934
|
|
|
"email": "http://github.com/ClaraBuettner", |
|
935
|
|
|
"date": time.strftime("%Y-%m-%d"), |
|
936
|
|
|
"object": None, |
|
937
|
|
|
"comment": "Updated metadata", |
|
938
|
|
|
}, |
|
939
|
|
|
], |
|
940
|
|
|
"metaMetadata": meta_metadata(), |
|
941
|
|
|
} |
|
942
|
|
|
meta_json = "'" + json.dumps(meta) + "'" |
|
943
|
|
|
|
|
944
|
|
|
db.submit_comment( |
|
945
|
|
|
meta_json, "demand", "egon_map_zensus_district_heating_areas" |
|
946
|
|
|
) |
|
947
|
|
|
|
|
948
|
|
|
return None |
|
949
|
|
|
|
|
950
|
|
|
|
|
951
|
|
|
def study_prospective_district_heating_areas(): |
|
952
|
|
|
""" |
|
953
|
|
|
Get information about Prospective Supply Districts for district heating. |
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954
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955
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This optional function executes the functions so that you can study the |
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956
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heat demand density data of different scenarios and compare them and the |
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957
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resulting Prospective Supply Districts (PSDs) for district heating. This |
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958
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functions saves local shapefiles, because these data are not written into |
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959
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database. Moreover, heat density curves are drawn. |
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960
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This function is tailor-made and includes the scenarios eGon2035 and |
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961
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eGon100RE. |
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962
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963
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Parameters |
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964
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---------- |
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965
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None |
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966
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967
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Returns |
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968
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------- |
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969
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None |
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970
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971
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Notes |
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972
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----- |
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973
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None |
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974
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975
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TODO |
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976
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---- |
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977
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PSD statistics (average PSD connection rate, total HD per PSD) could |
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978
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be studied |
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979
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""" |
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980
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981
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# create directory to store files |
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982
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results_path = "district_heating_areas/" |
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983
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984
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if not os.path.exists(results_path): |
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985
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os.mkdir(results_path) |
|
986
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|
987
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# load the total heat demand by census cell (residential plus service) |
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988
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# HD_2015 = load_heat_demands('eGon2015') |
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989
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# status quo heat demand data are part of the regluar database content |
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990
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# to get them, line 463 ("if not '2015' in source.stem:") has to be |
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991
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# deleted from |
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992
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# importing/heat_demand_data/__init__.py |
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993
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# and an empty row has to be added to scenario table: |
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994
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# INSERT INTO scenario.egon_scenario_parameters (name) |
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995
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# VALUES ('eGon2015'); |
|
996
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# because egon2015 is not part of the regular EgonScenario table! |
|
997
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HD_2035 = load_heat_demands("eGon2035") |
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998
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HD_2050 = load_heat_demands("eGon100RE") |
|
999
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1000
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# select only cells with heat demands > 100 GJ / (ha a) |
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1001
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# HD_2015_above_100GJ = select_high_heat_demands(HD_2015) |
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1002
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HD_2035_above_100GJ = select_high_heat_demands(HD_2035) |
|
1003
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HD_2050_above_100GJ = select_high_heat_demands(HD_2050) |
|
1004
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1005
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# PSDs |
|
1006
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# grouping cells applying the 201m distance buffer, including heat demand |
|
1007
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# aggregation |
|
1008
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# after decision for one year/scenario (here 2035), in the pipeline PSDs |
|
1009
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# are only calculeated for the one selected year/scenario; |
|
1010
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# here you can see all years/scenarios: |
|
1011
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# PSD_2015_201m = area_grouping(HD_2015_above_100GJ, distance=200, |
|
1012
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# minimum_total_demand=(10000/3.6) |
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1013
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# ).dissolve('area_id', aggfunc='sum') |
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1014
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# PSD_2015_201m.to_file(results_path+"PSDs_2015based.shp") |
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1015
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PSD_2035_201m = area_grouping( |
|
1016
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HD_2035_above_100GJ, distance=200, minimum_total_demand=(10000 / 3.6) |
|
1017
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).dissolve("area_id", aggfunc="sum") |
|
1018
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# HD_2035.to_file(results_path+"HD_2035.shp") |
|
1019
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# HD_2035_above_100GJ.to_file(results_path+"HD_2035_above_100GJ.shp") |
|
1020
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PSD_2035_201m.to_file(results_path + "PSDs_2035based.shp") |
|
1021
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PSD_2050_201m = area_grouping( |
|
1022
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HD_2050_above_100GJ, distance=200, minimum_total_demand=(10000 / 3.6) |
|
1023
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).dissolve("area_id", aggfunc="sum") |
|
1024
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PSD_2050_201m.to_file(results_path + "PSDs_2050based.shp") |
|
1025
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|
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|
1026
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# plotting all cells - not considering census data |
|
1027
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# https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html |
|
1028
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# https://www.earthdatascience.org/courses/scientists-guide-to-plotting-data-in-python/plot-with-matplotlib/introduction-to-matplotlib-plots/customize-plot-colors-labels-matplotlib/ |
|
1029
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fig, ax = plt.subplots(1, 1) |
|
1030
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# add the sorted heat demand densities |
|
1031
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# HD_2015 = HD_2015.sort_values('residential_and_service_demand', |
|
1032
|
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# ascending=False).reset_index() |
|
1033
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|
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# HD_2015["Cumulative_Sum"] = (HD_2015.residential_and_service_demand. |
|
1034
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|
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# cumsum()) / 1000000 |
|
1035
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# ax.plot(HD_2015.Cumulative_Sum, |
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1036
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# HD_2015.residential_and_service_demand, label='eGon2015') |
|
1037
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|
1038
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HD_2035 = HD_2035.sort_values( |
|
1039
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"residential_and_service_demand", ascending=False |
|
1040
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).reset_index() |
|
1041
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HD_2035["Cumulative_Sum"] = ( |
|
1042
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HD_2035.residential_and_service_demand.cumsum() |
|
1043
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) / 1000000 |
|
1044
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ax.plot( |
|
1045
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HD_2035.Cumulative_Sum, |
|
1046
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HD_2035.residential_and_service_demand, |
|
1047
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label="eGon2035", |
|
1048
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) |
|
1049
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|
1050
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HD_2050 = HD_2050.sort_values( |
|
1051
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"residential_and_service_demand", ascending=False |
|
1052
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).reset_index() |
|
1053
|
|
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HD_2050["Cumulative_Sum"] = ( |
|
1054
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|
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HD_2050.residential_and_service_demand.cumsum() |
|
1055
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) / 1000000 |
|
1056
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ax.plot( |
|
1057
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HD_2050.Cumulative_Sum, |
|
1058
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HD_2050.residential_and_service_demand, |
|
1059
|
|
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label="eGon100RE", |
|
1060
|
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) |
|
1061
|
|
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|
|
1062
|
|
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# add the district heating shares |
|
1063
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|
|
1064
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heat_parameters = get_sector_parameters("heat", "eGon2035") |
|
1065
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district_heating_share_2035 = heat_parameters["DE_district_heating_share"] |
|
1066
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plt.axvline( |
|
1067
|
|
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x=HD_2035.residential_and_service_demand.sum() |
|
1068
|
|
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/ 1000000 |
|
1069
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* district_heating_share_2035, |
|
1070
|
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ls=":", |
|
1071
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lw=0.5, |
|
1072
|
|
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label="72TWh DH in 2035 in Germany => 14% DH", |
|
1073
|
|
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color="black", |
|
1074
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) |
|
1075
|
|
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heat_parameters = get_sector_parameters("heat", "eGon100RE") |
|
1076
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district_heating_share_100RE = heat_parameters["DE_district_heating_share"] |
|
1077
|
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plt.axvline( |
|
1078
|
|
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x=HD_2050.residential_and_service_demand.sum() |
|
1079
|
|
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/ 1000000 |
|
1080
|
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* district_heating_share_100RE, |
|
1081
|
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ls="-.", |
|
1082
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lw=0.5, |
|
1083
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label="75TWh DH in 100RE in Germany => 19% DH", |
|
1084
|
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color="black", |
|
1085
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) |
|
1086
|
|
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|
|
1087
|
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# axes meet in (0/0) |
|
1088
|
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ax.margins(x=0, y=0) # default is 0.05 |
|
1089
|
|
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# axis style |
|
1090
|
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|
# https://matplotlib.org/stable/gallery/ticks_and_spines/centered_spines_with_arrows.html |
|
1091
|
|
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# Hide the right and top spines |
|
1092
|
|
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ax.spines["right"].set_visible(False) |
|
1093
|
|
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ax.spines["top"].set_visible(False) |
|
1094
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ax.plot(1, 0, ">k", transform=ax.get_yaxis_transform(), clip_on=False) |
|
1095
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ax.plot(0, 1, "^k", transform=ax.get_xaxis_transform(), clip_on=False) |
|
1096
|
|
|
|
|
1097
|
|
|
ax.set(title="Heat Demand in eGo^n") |
|
1098
|
|
|
ax.set_xlabel("Cumulative Heat Demand [TWh / a]") |
|
1099
|
|
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ax.set_ylabel("Heat Demand Densities [MWh / (ha a)]") |
|
1100
|
|
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|
|
1101
|
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plt.legend() |
|
1102
|
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plt.savefig(results_path + "Complete_HeatDemandDensities_Curves.png") |
|
1103
|
|
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|
|
1104
|
|
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return None |
|
1105
|
|
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|
|
1106
|
|
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|
1107
|
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def demarcation(plotting=True): |
|
1108
|
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|
""" |
|
1109
|
|
|
Load scenario specific district heating areas with metadata into database. |
|
1110
|
|
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|
|
1111
|
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This function executes the functions that identifies the areas which will |
|
1112
|
|
|
be supplied with district heat in the two eGo^n scenarios. The creation of |
|
1113
|
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heat demand density curve figures is optional. So is also the export of |
|
1114
|
|
|
scenario specific Prospective Supply Districts for district heating (PSDs) |
|
1115
|
|
|
as shapefiles including the creation of a figure showing the comparison |
|
1116
|
|
|
of sorted heat demand densities. |
|
1117
|
|
|
|
|
1118
|
|
|
The method was executed for 2015, 2035 and 2050 to find out which |
|
1119
|
|
|
scenario year defines the PSDs. The year 2035 was selected and |
|
1120
|
|
|
the function was adjusted accordingly. |
|
1121
|
|
|
If you need the 2015 scenario heat demand data, please have a look at |
|
1122
|
|
|
the heat demand script commit 270bea50332016447e869f69d51e96113073b8a0, |
|
1123
|
|
|
where the 2015 scenario was deactivated. You can study the 2015 PSDs in |
|
1124
|
|
|
the study_prospective_district_heating_areas function after |
|
1125
|
|
|
un-commenting some lines. |
|
1126
|
|
|
|
|
1127
|
|
|
Parameters |
|
1128
|
|
|
---------- |
|
1129
|
|
|
plotting: boolean |
|
1130
|
|
|
if True, figure showing the heat demand density curve will be created |
|
1131
|
|
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|
|
1132
|
|
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Returns |
|
1133
|
|
|
------- |
|
1134
|
|
|
None |
|
1135
|
|
|
|
|
1136
|
|
|
Notes |
|
1137
|
|
|
----- |
|
1138
|
|
|
None |
|
1139
|
|
|
|
|
1140
|
|
|
TODO |
|
1141
|
|
|
---- |
|
1142
|
|
|
Create diagrams/curves, make better curves with matplotlib |
|
1143
|
|
|
|
|
1144
|
|
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Make PSD and DH system statistics |
|
1145
|
|
|
Check if you need the current / future number of DH |
|
1146
|
|
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supplied flats and the total number of flats to calculate the |
|
1147
|
|
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connection rate |
|
1148
|
|
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|
|
1149
|
|
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Add datasets to datasets configuration |
|
1150
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|
1151
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""" |
|
1152
|
|
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|
|
1153
|
|
|
# load the census district heat data on apartments, and group them |
|
1154
|
|
|
# This is currently done in the grouping function: |
|
1155
|
|
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# district_heat_zensus, heating_type_zensus = load_census_data() |
|
1156
|
|
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# Zenus_DH_areas_201m = area_grouping(district_heat_zensus) |
|
1157
|
|
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|
|
1158
|
|
|
heat_density_per_scenario = {} |
|
1159
|
|
|
# scenario specific district heating areas |
|
1160
|
|
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|
|
1161
|
|
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for scenario in config.settings()["egon-data"]["--scenarios"]: |
|
1162
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heat_density_per_scenario[scenario] = district_heating_areas( |
|
1163
|
|
|
scenario, plotting |
|
1164
|
|
|
) |
|
1165
|
|
|
|
|
1166
|
|
|
if plotting: |
|
1167
|
|
|
plot_heat_density_sorted(heat_density_per_scenario) |
|
1168
|
|
|
# if you want to study/export the Prospective Supply Districts (PSDs) |
|
1169
|
|
|
# for all scenarios |
|
1170
|
|
|
# study_prospective_district_heating_areas() |
|
1171
|
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|
|
1172
|
|
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add_metadata() |
|
1173
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|
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|
1174
|
|
|
return None |
|
1175
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openego /
eGon-data
