Cooling and Heating Workflows

Cooling and Heating Workflows#

This example illustrates the cooling and heating workflows currently employed by direct air capture technologies, which may also be utilised by other future RESKit implementations.

import reskit as rk
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

This script illustrates how to run a simulation with ETHOS.RESKit.CoolingHeating.

# Create Placements DataFrame with turbine locations and specifications

placements = pd.DataFrame(
    {
        "lon": [5.5, 5.994685, 6.8],
        "lat": [50.797254, 50.794208, 49.5],
        "capacity": [4000, 4000, 4000],
    }
)
placements
lon lat capacity
0 5.500000 50.797254 4000
1 5.994685 50.794208 4000
2 6.800000 49.500000 4000

Run the simulation workflow for air cooling#

RESKit will run the simulation and create an xarray Dataset with the simulation results for you. Apart from the capacity_factor, RESKit also includes the conversion factors and intermediate data used to determine the capacity factor and conversion factors.

reskit_xr = rk.cooling_heating.air_cooling_wenzel2025(
    placements=placements,
    era5_path=rk.TEST_DATA["era5-like"],
    temperatureCoolant=30,
    designTemperature=5,
)
reskit_xr

<xarray.Dataset> Size: 35kB
Dimensions:                             (location: 3, time: 140)
Coordinates:
  * location                            (location) int64 24B 0 1 2
  * time                                (time) datetime64[ns] 1kB 2014-12-31T...
Data variables: (12/13)
    lon                                 (location) float64 24B 5.5 5.995 6.8
    lat                                 (location) float64 24B 50.8 50.79 49.5
    capacity                            (location) int64 24B 4000 4000 4000
    surface_air_temperature             (time, location) float64 3kB -0.248 ....
    conversion_factor_fan_electricity   (time, location) float64 3kB -0.01136...
    conversion_factor_pump_electricity  (time, location) float64 3kB -0.00271...
    ...                                  ...
    capacity_factor                     (time, location) float64 3kB 1.0 ... 1.0
    conversion_factor_electricity       (time, location) float64 3kB -0.01407...
    cooling_output                      (time, location) float64 3kB 4e+03 .....
    electricity_input                   (time, location) float64 3kB 56.28 .....
    electricity_input_fan               (time, location) float64 3kB 45.45 .....
    electricity_input_pump              (time, location) float64 3kB 10.84 .....
Attributes:
    capacity:                            kW_th
    relative_cost_factor:                -
    capacity_factor:                     -
    conversion_factor_electricity:       kWh_el/kWh_th
    conversion_factor_fan_electricity:   kWh_el/kWh_th
    conversion_factor_pump_electricity:  kWh_el/kWh_th
    electricity_input:                   kWh_el
    electricity_input_fan:               kWh_el
    electricity_input_pump:              kWh_el
    cooling_output:                      kWh_th

Have a look at the previaling temperature:

fig, ax = plt.subplots(nrows=1, ncols=1)
reskit_xr["surface_air_temperature"].isel(time=slice(0, 400)).plot.line(x="time", ax=ax)
ax.axhline(5, c="r")  # design Temperature, at which the system will be able to provide the specified cooling load!

<matplotlib.lines.Line2D at 0x7fdae443f8f0>
../../_images/81c0195472b02c45a955131d422f738733a613c8ffbc1ad4245a50ca7fd552a2.png

RESKit will output the capacity factor of each location for every hour of the simulated year. If the air temperature is above the design temperature, the capacity factor is lower than 1 since the designed pumps/fans would not be able to provide sufficient flows.

reskit_xr["capacity_factor"].isel(time=slice(0, 400)).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x7fda94f9ac60>,
 <matplotlib.lines.Line2D at 0x7fda94f98c50>,
 <matplotlib.lines.Line2D at 0x7fda94fbec90>]
../../_images/df775eaaad49ad292b0c0411baab6229e3483345ce57d9f619d265b808f6a479.png

As well as the conversion factors:

reskit_xr["conversion_factor_electricity"].isel(time=slice(0, 400)).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x7fda94e60f80>,
 <matplotlib.lines.Line2D at 0x7fda94e63590>,
 <matplotlib.lines.Line2D at 0x7fda94e52270>]
../../_images/7da1baf109910262f997753853bdead67db615f96540b7adcc0e2d801d862346.png 
reskit_xr["electricity_input"].isel(time=slice(0, 400)).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x7fda94ef1a30>,
 <matplotlib.lines.Line2D at 0x7fda94ef2fc0>,
 <matplotlib.lines.Line2D at 0x7fda94ef2450>]
../../_images/acfcab6e0e5b49803993f415502bef9675d0336929432512ae6118c39fb40098.png

Run the simulation workflow for an evaporative cooling system to calculate the water losses#

reskit_xr = rk.cooling_heating.evaporative_cooling_wortmann2025(
    placements=placements,
    era5_path=rk.TEST_DATA["era5-like"],
    temperatureCoolant=80,
    heatTransferDelta=10,
    efficiencyCoolingTower=0.65,
)


reskit_xr

<xarray.Dataset> Size: 35kB
Dimensions:                                   (location: 3, time: 140)
Coordinates:
  * location                                  (location) int64 24B 0 1 2
  * time                                      (time) datetime64[ns] 1kB 2014-...
Data variables: (12/13)
    lon                                       (location) float64 24B 5.5 ... 6.8
    lat                                       (location) float64 24B 50.8 ......
    capacity                                  (location) int64 24B 4000 ... 4000
    surface_air_temperature                   (time, location) float64 3kB -0...
    surface_dew_temperature                   (time, location) float64 3kB -0...
    relative_humidity                         (time, location) float64 3kB 97...
    ...                                        ...
    approach_temperature_evaporative_cooling  (time, location) float64 3kB 24...
    specific_mass_evaporation_loss            (time, location) float64 3kB 0....
    specific_mass_drift_loss                  (time, location) float64 3kB 0....
    specific_mass_blowdown_loss               (time, location) float64 3kB 0....
    conversion_factor_water                   (time, location) float64 3kB -1...
    total_water_losses                        (time, location) float64 3kB 4....
Attributes:
    capacity:                                  kW_th
    conversion_factor_water:                   kg_H2O/kWh_th
    wet_bulb_temperature:                      °C
    approach_temperature_evaporative_cooling:  K
    specific_mass_evaporation_loss:            kg_H2O/kWh_th
    specific_mass_drift_loss:                  kg_H2O/kWh_th
    specific_mass_blowdown_loss:               kg_H2O/kWh_th
reskit_xr["conversion_factor_water"].std(dim="time")

<xarray.DataArray 'conversion_factor_water' (location: 3)> Size: 24B
array([0.03379537, 0.03082596, 0.03771017])
Coordinates:
  * location  (location) int64 24B 0 1 2
reskit_xr["conversion_factor_water"].isel(time=slice(0, 400)).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x7fda94fa60f0>,
 <matplotlib.lines.Line2D at 0x7fda94e78260>,
 <matplotlib.lines.Line2D at 0x7fda8c5610a0>]
../../_images/feeb1918777cadaac4c091293f7beef33dbe30136c37b4ba55ac2cc9a56122ef.png

The following plot show the influence of temperature vs. the conversion factor (specific water demand) for location 0:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(nrows=1, ncols=1)
ax.scatter(reskit_xr["surface_air_temperature"], -reskit_xr["conversion_factor_water"])
ax.set_title("Ambient Air Temperature vs. Water Consumption")
ax.set_ylabel("Specific Water Loss [kg$_{H2O}$/kWh$_{th}$]")
ax.set_xlabel("Air Temperature [°C]")

Text(0.5, 0, 'Air Temperature [°C]')
../../_images/77845f448f557670ad7d99717f6ee6ff54cc74600743509c9182748c0e2421ac.png

Run the simulation workflow for air source heat pumps#

RESKit will run the simulation and create an xarray Dataset with the simulation results for you.

reskit_hp = rk.cooling_heating.air_source_heat_pump(placements=placements, era5_path=rk.TEST_DATA["era5-like"])
reskit_hp

<xarray.Dataset> Size: 18kB
Dimensions:                        (location: 3, time: 140)
Coordinates:
  * location                       (location) int64 24B 0 1 2
  * time                           (time) datetime64[ns] 1kB 2014-12-31T23:30...
Data variables:
    lon                            (location) float64 24B 5.5 5.995 6.8
    lat                            (location) float64 24B 50.8 50.79 49.5
    capacity                       (location) int64 24B 4000 4000 4000
    surface_air_temperature        (time, location) float64 3kB -0.248 ... -1...
    COP                            (time, location) float64 3kB 1.861 ... 1.836
    conversion_factor_electricity  (time, location) float64 3kB -0.5373 ... -...
    electricity_input              (time, location) float64 3kB 2.149e+03 ......
    heat_output                    (time, location) float64 3kB 4e+03 ... 4e+03
Attributes:
    capacity:                       kW_th
    conversion_factor_electricity:  kWh_el/kWh_th
    electricity_input:              kWh_el
    heat_output:                    kWh_th
    COP:                            -
reskit_hp["COP"].isel(time=slice(0, 400)).plot.line(x="time")

[<matplotlib.lines.Line2D at 0x7fda8c4f8110>,
 <matplotlib.lines.Line2D at 0x7fdae443e180>,
 <matplotlib.lines.Line2D at 0x7fda8c4fbd10>]
../../_images/cb48f07d81988e2b0557ec96f43fef4130d9ff86f49c33d6416378fcf1106b9e.png