Overview: PV Workflows

Overview: PV Workflows#

This example provides an overview of the PV workflows implemented in RESKit.

Workflow:

  1. Import required packages

  2. Calculate capacity factors based on MERRA data

  3. Calculate capacity factors based on Sarah and ERA5 data

  4. Calculate capacity factors based on ERA5 data

import reskit as rk
import pandas as pd

Openfield PV system based on MERRA data

xds = rk.solar.openfield_pv_merra_ryberg2019(
    placements=pd.read_csv(rk.TEST_DATA["module_placements.csv"]),
    merra_path=rk.TEST_DATA["merra-like"],
    global_solar_atlas_ghi_path=rk.TEST_DATA["gsa-ghi-like.tif"],
)

# Mean value and standard deviation of the capacity factor across all placements
xds["capacity_factor"].values.mean(), xds["capacity_factor"].values.std()

(0.04185117246354239, 0.09839841335211245)

Openfield PV system based on Sarah and ERA5 data

xds = rk.solar.openfield_pv_sarah_unvalidated(
    placements=pd.read_csv(rk.TEST_DATA["module_placements.csv"]),
    sarah_path=rk.TEST_DATA["sarah-like"],
    era5_path=rk.TEST_DATA["era5-like"],
)

# Mean value and standard deviation of the capacity factor across all placements
xds["capacity_factor"].values.mean(), xds["capacity_factor"].values.std()

(0.14925646752714336, 0.2520615327406497)

Openfield PV system based ond ERA5 data

xds = rk.solar.openfield_pv_era5(
    placements=pd.read_csv(rk.TEST_DATA["module_placements.csv"]),
    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"],
)

# Mean value and standard deviation of the capacity factor across all placements
xds["capacity_factor"].values.mean(), xds["capacity_factor"].values.std()

(0.0799396380771903, 0.16248583153700052)