Solar PV Simulation Workflow#
This example shows the different results of the different PV simulation workflows
Workflow:
Import required packages
Create locations DataFrame for single location
Run simulation for single location
Plot Capacity factor for single location
Computate single location, only using ERA5 weather data
Computate single location with Tracking
Simulate multiple locations
import reskit as rk
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Single Location based on Sarah and ERA5 Data.#
# 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
| lon | lat | tilt | azimuth | capacity | elev | |
|---|---|---|---|---|---|---|
| 0 | 6.083 | 50.775 | 32 | 180 | 2000 | 250 |
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))
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')
# Plot of the capacity factor time series for the single placement
out.capacity_factor.plot()
[<matplotlib.lines.Line2D at 0x71074aae32c0>]
Simulate single location, but only using ERA-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))
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: solar_azimuth - Dimensions:('time', 'location')
17: apparent_solar_zenith - Dimensions:('time', 'location')
18: direct_normal_irradiance - Dimensions:('time', 'location')
19: extra_terrestrial_irradiance - Dimensions:('time', 'location')
20: air_mass - Dimensions:('time', 'location')
21: diffuse_horizontal_irradiance - Dimensions:('time', 'location')
22: angle_of_incidence - Dimensions:('time', 'location')
23: poa_global - Dimensions:('time', 'location')
24: poa_direct - Dimensions:('time', 'location')
25: poa_diffuse - Dimensions:('time', 'location')
26: poa_sky_diffuse - Dimensions:('time', 'location')
27: poa_ground_diffuse - Dimensions:('time', 'location')
28: cell_temperature - Dimensions:('time', 'location')
29: module_dc_power_at_mpp - Dimensions:('time', 'location')
30: module_dc_voltage_at_mpp - Dimensions:('time', 'location')
31: capacity_factor - Dimensions:('time', 'location')
32: total_system_generation - Dimensions:('time', 'location')
out.capacity_factor.fillna(0).plot()
[<matplotlib.lines.Line2D at 0x71074ac7e8a0>]
Single Location with Tracking#
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.capacity_factor.fillna(0).plot()
[<matplotlib.lines.Line2D at 0x710749194380>]
Simulating multiple locations is the same as simulating a single location#
It is recommended to simulate multiple locations at once!
# 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
| 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 |
# 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
)
# 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()
<matplotlib.collections.QuadMesh at 0x7107491c9d00>
