Sink Simulation

Convert Sinks

src.Sink.simulation.convert_sinks.convert_sinks(in_var, kb)

Design of technologies to connect DHN to the sinks.

For each sink are designed the conversion technologies needed. When performing the conversion, three design options may occur: Sink that requires HEATING:

1. The grid temperature meets the sink target temperature requirements, thus only a grid-sink HX and correspondent circulation pumping is needed

2. The grid temperature does not meet the sink target temperature requirements, thus adding to the grid-sink HX and correspondent circulation pumping, it is also necessary to add a technology to raise the temperature (chp,solar thermal, heat pump, boiler)

Sink that requires COOLING:

3. It is always necessary to add a cooling technology (thermal_chiller), since we are only designing DHNs

Possible conversions: HX, HX + heating/cooling technology + HX.

Moreover, for the group of sinks capacity, heating grid specific technologies (also known as backup) are designed.

Parameters

• in_var (All necessary data to perform the grid to sinks conversion with the following key:) –

  platform: dict

  Data obtained from the platform

  • grid_supply_temperaturefloat, optional

    Grid supply temperature provided by the user [ºC]

  • grid_return_temperaturefloat, optional

    Grid return temperature provided by the user [ºC]

  • group_of_sinkslist with dict

    List with all sinks to be analyzed; each with the following keys:

    • idint

      Sink ID []

    • locationlist

      [latitude, longitude] [º]

    • fuels_data: dict, optional

      Fuels price and CO2 emission, with the following keys:

      • natural_gas: dict

        Natural gas data

        • co2_emissions: float:

          Fuel CO2 emission [kg CO2/kWh

        • price: float:

          Fuel price [€/kWh

      • fuel_oil

        Same keys as “natural_gas”

      • electricity

        Same keys as “natural_gas”

      • biomass

        Same keys as “natural_gas”

    • streamslist with dict

      Streams to be analyzed. Each stream with the following keys:

      • idint

        Stream ID []

      • namestr

        Stream name []

      • object_typestr

        DEFAULT = “stream” []

      • object_linked_id

        None: DEFAULT=NONE, since no equipment/process is associated

      • stream_typestr

        Stream designation []; inflow, outflow, excess_heat

      • supply_temperaturefloat

        Stream’s supply/initial temperature [ºC]

      • target_temperaturefloat

        Stream’s target/final temperature [ºC]

      • fluidstr

        Stream fluid name

      • flowratefloat

        Stream mass flowrate[kg/h]

      • schedulelist

        Hourly values between 0 and 1, according to the capacity ration on that hour

      • hourly_generation: list

        Stream’s hourly capacity [kWh]

      • capacityfloat

        Stream’s capacity [kW]

      • fuelstr

        Associated equipment fuel name []

      • eff_equipmentfloat

        Associated equipment efficiency []

• kb (dict) – Knowledge Base data

Returns

all_info –

All conversion data

• all_sinks_infolist

  Each sink conversion data

  -sink_group_grid_supply_temperaturefloat

  Grid supply temperature [ºC]

  • sink_group_grid_return_temperaturefloat

    Grid return temperature [ºC]

  • grid_specificlist

    List with Grid Specific technologies, each technology with the following keys:

    • teo_equipment_namestr

      Specific nomenclature for the TEO

    • outputstr

      Specific nomenclature for the TEO

    • input_fuelstr

      Specific nomenclature for the TEO

    • output_fuelstr

      Specific nomenclature for the TEO

    • equipmentlist

      All conversion equipments; list with technologies names; e.g. [‘hx_plate’, ‘heat_pump’,’hx_plate’]

    • max_capacityfloat

      Stream power (sources- excess heat; sinks - grid heat) [kW]

    • turnkey_afloat

      Aggregated turnkey a [€/kW]

    • turnkey_bfloat

      Aggregated turnkey b [€]

    • conversion_efficiency :

      Aggregated conversion_efficiency []

    • electrical_conversion_efficiencyfloat

      ONLY FOR ORC - electrical conversion efficiency []

    • om_fixfloat

      Aggregated om_fix [€/year.kW]

    • om_varfloat

      Aggregated om_var [€/kWh]

    • emissionsfloat

      Aggregated emissions [kg.CO2/kWh]

    • technologieslist with dicts

      Each equipment info in detail (check General/Convert_Equipments/Convert_Options)

  • sinkslist with dict

    List with each sink data, with the following keys:

    • sink_idstr

      Sink ID

    • locationlist

      [latitude, longitude] [º]

    • streamslist with dict

      Each stream of the sink, with the following keys:

      • stream_idstr

        Stream ID

      • demand_fuelstr

        TEO specific data

      • gis_capacityfloat

        Sink nominal capacity [kWh]

      • hourly_stream_capacitylist

        Stream hourly capacity [kWh]

      • teo_demand_factorlist

        Stream’s hourly capacity divided by yearly capacity [kWh]

      • teo_yearly_demandfloat

        Stream yearly demand [kWh]

      • conversion_technologieslist

        List with multiple dictionaries with the solution of technologies possible to implement; same keys as the “grid_specific” technologies

• n_demand_listlist with dicts

  Sinks data for GIS, with the following keys:

  • idstr

    Object ID

  • coordslist

    [latitude, longitude] [º]

  • capfloat

    Object nominal capacity [kW]

• n_grid_specificlist with dicts

  Grid specific data for GIS, with the following keys:

  • idint

    Object ID

  • coordslist

    Same keys as “n_demand_list”

  • capfloat

    Same keys as “n_demand_list”

• n_thermal_storagelist with dicts

  Thermal storage data for GIS; Same keys as “n_grid_specific”

• teo_demand_factor_grouplist

  Every hour of the year with dicts in each hour with TEO Sink ID and corresponding hourly_capacity/yearly_capacity; e.g. [{“sink1”:0.5,”sink2”:0,…},{{“sink1”:0.6,”sink2”:0,…},…]

Return type

dict