Solar PV Simulation Workflow¶

This example shows the different results of the different PV simulation workflows

Workflow:

1. Import required packages
2. Create locations DataFrame for single location
3. Run simulation for single location
4. Plot Capacity factor for single location
5. Computate single location, only using ERA5 weather data
6. Computate single location with Tracking
7. Simulate multiple locations

In [1]:

import reskit as rk
import pandas as pd

import matplotlib.pyplot as plt

%matplotlib inline

import reskit as rk import pandas as pd import matplotlib.pyplot as plt %matplotlib inline

---

Single Location based on Sarah and ERA5 Data.¶

In [2]:

# Make a dataframe for a single placement
placements = pd.DataFrame()
placements["lon"] = [
    6.083,
]  # Longitude
placements["lat"] = [
    50.775,
]  # Latitude
placements["tilt"] = [
    32,
]  # System tilt in degrees
placements["azimuth"] = [
    180,
]  # System azimuth in degrees
placements["capacity"] = [
    2000,
]  # Total system capacity in kW
placements["elev"] = [
    250,
]  # Altitude in meters

placements

# Make a dataframe for a single placement placements = pd.DataFrame() placements["lon"] = [ 6.083, ] # Longitude placements["lat"] = [ 50.775, ] # Latitude placements["tilt"] = [ 32, ] # System tilt in degrees placements["azimuth"] = [ 180, ] # System azimuth in degrees placements["capacity"] = [ 2000, ] # Total system capacity in kW placements["elev"] = [ 250, ] # Altitude in meters placements

Out[2]:

	lon	lat	tilt	azimuth	capacity	elev
0	6.083	50.775	32	180	2000	250

In [3]:

out = rk.solar.openfield_pv_sarah_unvalidated(
    placements=placements,
    sarah_path=rk.TEST_DATA["sarah-like"],
    era5_path=rk.TEST_DATA["era5-like"],
    module="WINAICO WSx-240P6",  # PV module to use for simulation
)

print("Output is of type:", type(out))
print("Variables in output:")
for i, var in enumerate(list(out.variables)):
    print(" {i:2d}: {name:30s} - Dimensions:{dim}".format(i=i, name=var, dim=out[var].dims))

out = rk.solar.openfield_pv_sarah_unvalidated( placements=placements, sarah_path=rk.TEST_DATA["sarah-like"], era5_path=rk.TEST_DATA["era5-like"], module="WINAICO WSx-240P6", # PV module to use for simulation ) print("Output is of type:", type(out)) print("Variables in output:") for i, var in enumerate(list(out.variables)): print(" {i:2d}: {name:30s} - Dimensions:{dim}".format(i=i, name=var, dim=out[var].dims))

FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/solar/workflows/solar_workflow_manager.py:970
Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/weather/NCSource.py:258
ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Output is of type: <class 'xarray.core.dataset.Dataset'>
Variables in output:
  0: location                       - Dimensions:('location',)
  1: lon                            - Dimensions:('location',)
  2: lat                            - Dimensions:('location',)
  3: tilt                           - Dimensions:('location',)
  4: azimuth                        - Dimensions:('location',)
  5: capacity                       - Dimensions:('location',)
  6: elev                           - Dimensions:('location',)
  7: time                           - Dimensions:('time',)
  8: direct_normal_irradiance       - Dimensions:('time', 'location')
  9: global_horizontal_irradiance   - Dimensions:('time', 'location')
 10: surface_wind_speed             - Dimensions:('time', 'location')
 11: surface_pressure               - Dimensions:('time', 'location')
 12: surface_air_temperature        - Dimensions:('time', 'location')
 13: surface_dew_temperature        - Dimensions:('time', 'location')
 14: solar_azimuth                  - Dimensions:('time', 'location')
 15: apparent_solar_zenith          - Dimensions:('time', 'location')
 16: extra_terrestrial_irradiance   - Dimensions:('time', 'location')
 17: air_mass                       - Dimensions:('time', 'location')
 18: diffuse_horizontal_irradiance  - Dimensions:('time', 'location')
 19: angle_of_incidence             - Dimensions:('time', 'location')
 20: poa_global                     - Dimensions:('time', 'location')
 21: poa_direct                     - Dimensions:('time', 'location')
 22: poa_diffuse                    - Dimensions:('time', 'location')
 23: poa_sky_diffuse                - Dimensions:('time', 'location')
 24: poa_ground_diffuse             - Dimensions:('time', 'location')
 25: cell_temperature               - Dimensions:('time', 'location')
 26: module_dc_power_at_mpp         - Dimensions:('time', 'location')
 27: module_dc_voltage_at_mpp       - Dimensions:('time', 'location')
 28: capacity_factor                - Dimensions:('time', 'location')
 29: total_system_generation        - Dimensions:('time', 'location')

FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/solar/workflows/solar_workflow_manager.py:970
Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.

In [4]:

# Plot of the capacity factor time series for the single placement
out.capacity_factor.plot()

# Plot of the capacity factor time series for the single placement out.capacity_factor.plot()

Out[4]:

[<matplotlib.lines.Line2D at 0x79d6a8fd0500>]

---

Simulate single location, but only using ERA-5¶

In [5]:

out = rk.solar.openfield_pv_era5(
    placements=placements,
    era5_path=rk.TEST_DATA["era5-like"],
    global_solar_atlas_ghi_path=rk.TEST_DATA["gsa-ghi-like.tif"],
    global_solar_atlas_dni_path=rk.TEST_DATA["gsa-dni-like.tif"],
    module="WINAICO WSx-240P6",  # PV module to use for simulation
)

print("Output is of type:", type(out))
print("Variables in output:")
for i, var in enumerate(list(out.variables)):
    print(" {i:2d}: {name:30s} - Dimensions:{dim}".format(i=i, name=var, dim=out[var].dims))

out = rk.solar.openfield_pv_era5( placements=placements, era5_path=rk.TEST_DATA["era5-like"], global_solar_atlas_ghi_path=rk.TEST_DATA["gsa-ghi-like.tif"], global_solar_atlas_dni_path=rk.TEST_DATA["gsa-dni-like.tif"], module="WINAICO WSx-240P6", # PV module to use for simulation ) print("Output is of type:", type(out)) print("Variables in output:") for i, var in enumerate(list(out.variables)): print(" {i:2d}: {name:30s} - Dimensions:{dim}".format(i=i, name=var, dim=out[var].dims))

FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/solar/workflows/solar_workflow_manager.py:970
Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/weather/NCSource.py:258
ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Output is of type: <class 'xarray.core.dataset.Dataset'>
Variables in output:
  0: location                       - Dimensions:('location',)
  1: lon                            - Dimensions:('location',)
  2: lat                            - Dimensions:('location',)
  3: tilt                           - Dimensions:('location',)
  4: azimuth                        - Dimensions:('location',)
  5: capacity                       - Dimensions:('location',)
  6: elev                           - Dimensions:('location',)
  7: LRA_factor_global_horizontal_irradiance - Dimensions:('location',)
  8: LRA_factor_direct_normal_irradiance - Dimensions:('location',)
  9: time                           - Dimensions:('time',)
 10: global_horizontal_irradiance   - Dimensions:('time', 'location')
 11: direct_horizontal_irradiance   - Dimensions:('time', 'location')
 12: surface_wind_speed             - Dimensions:('time', 'location')
 13: surface_pressure               - Dimensions:('time', 'location')
 14: surface_air_temperature        - Dimensions:('time', 'location')
 15: surface_dew_temperature        - Dimensions:('time', 'location')
 16: snow_albedo                    - Dimensions:('time', 'location')
 17: snow_density                   - Dimensions:('time', 'location')
 18: snow_depth_water_equivalent    - Dimensions:('time', 'location')
 19: snowfall_water_equivalent      - Dimensions:('time', 'location')
 20: solar_azimuth                  - Dimensions:('time', 'location')
 21: apparent_solar_zenith          - Dimensions:('time', 'location')
 22: direct_normal_irradiance       - Dimensions:('time', 'location')
 23: extra_terrestrial_irradiance   - Dimensions:('time', 'location')
 24: air_mass                       - Dimensions:('time', 'location')
 25: diffuse_horizontal_irradiance  - Dimensions:('time', 'location')
 26: angle_of_incidence             - Dimensions:('time', 'location')
 27: poa_global                     - Dimensions:('time', 'location')
 28: poa_direct                     - Dimensions:('time', 'location')
 29: poa_diffuse                    - Dimensions:('time', 'location')
 30: poa_sky_diffuse                - Dimensions:('time', 'location')
 31: poa_ground_diffuse             - Dimensions:('time', 'location')
 32: cell_temperature               - Dimensions:('time', 'location')
 33: module_dc_power_at_mpp         - Dimensions:('time', 'location')
 34: module_dc_voltage_at_mpp       - Dimensions:('time', 'location')
 35: capacity_factor                - Dimensions:('time', 'location')
 36: total_system_generation        - Dimensions:('time', 'location')

FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/solar/workflows/solar_workflow_manager.py:970
Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.

In [6]:

out.capacity_factor.fillna(0).plot()

out.capacity_factor.fillna(0).plot()

Out[6]:

[<matplotlib.lines.Line2D at 0x79d6a8ca0170>]

---

Single Location with Tracking¶

In [7]:

out = rk.solar.openfield_pv_era5(
    placements=placements,
    era5_path=rk.TEST_DATA["era5-like"],
    global_solar_atlas_ghi_path=rk.TEST_DATA["gsa-ghi-like.tif"],
    global_solar_atlas_dni_path=rk.TEST_DATA["gsa-dni-like.tif"],
    module="WINAICO WSx-240P6",  # Module to use for simulation
    tracking="single_axis",
)

out = rk.solar.openfield_pv_era5( placements=placements, era5_path=rk.TEST_DATA["era5-like"], global_solar_atlas_ghi_path=rk.TEST_DATA["gsa-ghi-like.tif"], global_solar_atlas_dni_path=rk.TEST_DATA["gsa-dni-like.tif"], module="WINAICO WSx-240P6", # Module to use for simulation tracking="single_axis", )

FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/solar/workflows/solar_workflow_manager.py:970
Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/weather/NCSource.py:258
ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/solar/workflows/solar_workflow_manager.py:970
Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.

In [8]:

out.capacity_factor.fillna(0).plot()

out.capacity_factor.fillna(0).plot()

Out[8]:

[<matplotlib.lines.Line2D at 0x79d6a8d3e930>]

---

Simulating multiple locations is the same as simulating a single location¶

• It is recommended to simulate multiple locations at once!

In [9]:

# Make a placements dataframe with multiple locations
placements = pd.DataFrame()
placements["lon"] = [
    6.083,
    6.090,
    6.095,
    6.100,
    6.105,
]  # Longitude
placements["lat"] = [
    50.775,
    50.780,
    50.785,
    50.790,
    50.795,
]  # Latitude
placements["tilt"] = [
    32,
    25,
    20,
    35,
    40,
]  # System tilt in degrees
placements["azimuth"] = [
    180,
    180,
    180,
    180,
    180,
]  # System azimuth in degrees
placements["capacity"] = [
    2000,
    2000,
    2000,
    2000,
    2000,
]  # Total system capacity in kW
placements["elev"] = [
    250,
    300,
    350,
    1000,
    -100,
]  # Altitude in meters

placements

# Make a placements dataframe with multiple locations placements = pd.DataFrame() placements["lon"] = [ 6.083, 6.090, 6.095, 6.100, 6.105, ] # Longitude placements["lat"] = [ 50.775, 50.780, 50.785, 50.790, 50.795, ] # Latitude placements["tilt"] = [ 32, 25, 20, 35, 40, ] # System tilt in degrees placements["azimuth"] = [ 180, 180, 180, 180, 180, ] # System azimuth in degrees placements["capacity"] = [ 2000, 2000, 2000, 2000, 2000, ] # Total system capacity in kW placements["elev"] = [ 250, 300, 350, 1000, -100, ] # Altitude in meters placements

Out[9]:

	lon	lat	tilt	azimuth	capacity	elev
0	6.083	50.775	32	180	2000	250
1	6.090	50.780	25	180	2000	300
2	6.095	50.785	20	180	2000	350
3	6.100	50.790	35	180	2000	1000
4	6.105	50.795	40	180	2000	-100

In [10]:

# Run simulation with ERA5 workflow
out = rk.solar.openfield_pv_era5(
    placements=placements,
    era5_path=rk.TEST_DATA["era5-like"],
    global_solar_atlas_ghi_path=rk.TEST_DATA["gsa-ghi-like.tif"],
    global_solar_atlas_dni_path=rk.TEST_DATA["gsa-dni-like.tif"],
    module="WINAICO WSx-240P6",  # Module to use for simulation
)

# Run simulation with ERA5 workflow out = rk.solar.openfield_pv_era5( placements=placements, era5_path=rk.TEST_DATA["era5-like"], global_solar_atlas_ghi_path=rk.TEST_DATA["gsa-ghi-like.tif"], global_solar_atlas_dni_path=rk.TEST_DATA["gsa-dni-like.tif"], module="WINAICO WSx-240P6", # Module to use for simulation )

FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/solar/workflows/solar_workflow_manager.py:970
Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/weather/NCSource.py:258
ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

FutureWarning: /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/align-docs-branding/reskit/solar/workflows/solar_workflow_manager.py:970
Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'N' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.

In [11]:

# Same plot as in single examples, but now for multiple placements
plt.figure(figsize=(6, 4))
for i in range(placements.shape[0]):
    out.capacity_factor[:, i].fillna(0).plot(label=i)

plt.legend(ncol=3, title="Placement ID")
plt.title("Capacity Factor Generation")
plt.show()

# Same Plot function as in single examples
out.capacity_factor.fillna(0).plot()

# Same plot as in single examples, but now for multiple placements plt.figure(figsize=(6, 4)) for i in range(placements.shape[0]): out.capacity_factor[:, i].fillna(0).plot(label=i) plt.legend(ncol=3, title="Placement ID") plt.title("Capacity Factor Generation") plt.show() # Same Plot function as in single examples out.capacity_factor.fillna(0).plot()

Out[11]:

<matplotlib.collections.QuadMesh at 0x79d6a8caee40>