DSM_cts_ind

class DsmPotential(dependencies)[source]

Bases: egon.data.datasets.Dataset

class EgonDemandregioSitesIndElectricityDsmTimeseries(**kwargs)[source]

Bases: sqlalchemy.ext.declarative.api.Base

application
bus
e_max_pu
e_min_pu
e_nom
industrial_sites_id
p_max_pu
p_min_pu
p_nom
p_set
scn_name
target = {'schema': 'demand', 'table': 'egon_demandregio_sites_ind_electricity_dsm_timeseries'}
class EgonEtragoElectricityCtsDsmTimeseries(**kwargs)[source]

Bases: sqlalchemy.ext.declarative.api.Base

bus
e_max_pu
e_min_pu
e_nom
p_max_pu
p_min_pu
p_nom
p_set
scn_name
target = {'schema': 'demand', 'table': 'egon_etrago_electricity_cts_dsm_timeseries'}
class EgonOsmIndLoadCurvesIndividualDsmTimeseries(**kwargs)[source]

Bases: sqlalchemy.ext.declarative.api.Base

bus
e_max_pu
e_min_pu
e_nom
osm_id
p_max_pu
p_min_pu
p_nom
p_set
scn_name
target = {'schema': 'demand', 'table': 'egon_osm_ind_load_curves_individual_dsm_timeseries'}
class EgonSitesIndLoadCurvesIndividualDsmTimeseries(**kwargs)[source]

Bases: sqlalchemy.ext.declarative.api.Base

bus
e_max_pu
e_min_pu
e_nom
p_max_pu
p_min_pu
p_nom
p_set
scn_name
site_id
target = {'schema': 'demand', 'table': 'egon_sites_ind_load_curves_individual_dsm_timeseries'}
aggregate_components(df_dsm_buses, df_dsm_links, df_dsm_stores)[source]
calc_ind_site_timeseries(scenario)[source]
calc_per_unit(df)[source]
calculate_potentials(s_flex, s_util, s_inc, s_dec, delta_t, dsm)[source]
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
create_dsm_components(con, p_max, p_min, e_max, e_min, dsm, export_aggregated=True)[source]

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
create_table(df, table, engine=Engine(postgresql+psycopg2://egon:***@127.0.0.1:59734/egon-data))[source]

Create table

cts_data_import(cts_cool_vent_ac_share)[source]

Import CTS data necessary to identify DSM-potential.

cts_share: float
Share of cooling, ventilation and AC in CTS demand
data_export(dsm_buses, dsm_links, dsm_stores, carrier)[source]

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
delete_dsm_entries(carrier)[source]

Deletes DSM-components from database if they already exist before creating new ones.

Parameters
carrier: str
Remark in column ‘carrier’ identifying DSM-potential
dsm_cts_ind(con=Engine(postgresql+psycopg2://egon:***@127.0.0.1:59734/egon-data), cts_cool_vent_ac_share=0.22, ind_vent_cool_share=0.039, ind_vent_share=0.017)[source]

Execute methodology to create and implement components for DSM considering a) CTS per osm-area: combined potentials of cooling, ventilation and air

conditioning
  1. Industry per osm-are: combined potentials of cooling and ventilation
  2. Industrial Sites: potentials of ventilation in sites of
“Wirtschaftszweig” (WZ) 23
  1. 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
dsm_cts_ind_individual(cts_cool_vent_ac_share=0.22, ind_vent_cool_share=0.039, ind_vent_share=0.017)[source]

Execute methodology to create and implement components for DSM considering a) CTS per osm-area: combined potentials of cooling, ventilation and air

conditioning
  1. Industry per osm-are: combined potentials of cooling and ventilation
  2. Industrial Sites: potentials of ventilation in sites of
“Wirtschaftszweig” (WZ) 23
  1. 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
dsm_cts_ind_processing()[source]
get_p_nom_e_nom(df: pandas.core.frame.DataFrame)[source]
ind_osm_data_import(ind_vent_cool_share)[source]
Import industry data per osm-area necessary to identify DSM-potential.
ind_share: float
Share of considered application in industry demand
ind_osm_data_import_individual(ind_vent_cool_share)[source]
Import industry data per osm-area necessary to identify DSM-potential.
ind_share: float
Share of considered application in industry demand
ind_sites_data_import()[source]

Import industry sites data necessary to identify DSM-potential.

ind_sites_vent_data_import(ind_vent_share, wz)[source]
Import industry sites necessary to identify DSM-potential.
ind_vent_share: float
Share of considered application in industry demand
wz: int
Wirtschaftszweig to be considered within industry sites
ind_sites_vent_data_import_individual(ind_vent_share, wz)[source]
Import industry sites necessary to identify DSM-potential.
ind_vent_share: float
Share of considered application in industry demand
wz: int
Wirtschaftszweig to be considered within industry sites
relate_to_schmidt_sites(dsm)[source]