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solarPOT/EW_scenarios_MMH/compute_PV_output_batch_flat_EW.py
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compute_PV_output_batch_flat_EW.py
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import pandas as pd
import xarray as xr
import numpy as np
from pvlib.tools import cosd
from demand_mapping import get_yearly_sum
import os
import time
import sys
TILTED_IRRAD_E_FP = sys.argv[1] # name of the tilted radiation file (east-facing)
TILTED_IRRAD_W_FP = sys.argv[2] # name of the tilted radiation file (west-facing)
ROOFS_FP = sys.argv[3] # name of the file with roof characteristics
AVAIL_AREA_FP = sys.argv[4] # name of the file with predicted available area
OUTIRRAD_FP = sys.argv[5] # name of the output file (average tilted radiation)
OUTFILE_FP = sys.argv[6] # name of the output file (pv potential)
## ===== CONSTANTS ===== ##
CHUNKSIZE = 1000
EFFICIENCY_FP = '/work/hyenergy/output/solar_potential/technical_potential/pv_efficiencies_proj_roofs.nc'
print('\nTilted irradiance (east) file path: %s' %TILTED_IRRAD_E_FP)
print('Tilted irradiance (west) file path: %s' %TILTED_IRRAD_W_FP)
print('Available area file path: %s' %AVAIL_AREA_FP)
print('Roof surfaces file path: %s' %ROOFS_FP)
print('Efficiency file path: %s' %EFFICIENCY_FP)
print('Output file path (tilted radiation): %s' %OUTFILE_FP)
print('Output file path (PV Potential): %s\n\n' %OUTFILE_FP)
## =========================================================================================== ##
## ===== LOAD RADIATION DATA ===== ##
tt = time.time()
t0 = time.time()
irrad_E = xr.open_mfdataset( TILTED_IRRAD_E_FP, chunks={'DF_UID': CHUNKSIZE},
combine = 'by_coords', lock=False ).sortby('DF_UID')
irrad_W = xr.open_mfdataset( TILTED_IRRAD_W_FP, chunks={'DF_UID': CHUNKSIZE},
combine = 'by_coords', lock=False ).sortby('DF_UID')
irrad = 0.5 * (irrad_E + irrad_W)
irrad.to_netcdf( OUTIRRAD_FP )
print('Saved average tilted radiation in %s (%.3fs)\n' %(OUTIRRAD_FP, time.time()-tt))
## ===== LOAD OTHER DATA ===== ##
av_area = pd.read_csv( AVAIL_AREA_FP, usecols = ['DF_UID', 'tilted_area', 'available_area'])
roofs = pd.read_csv( ROOFS_FP, usecols = ['DF_UID', 'NEIGUNG', 'panel_tilt'])
eff = xr.open_mfdataset( EFFICIENCY_FP, chunks={'DF_UID': CHUNKSIZE},
combine = 'by_coords', lock=False ).sel(DF_UID = irrad.DF_UID.values)
print('Loaded all data in %.3f seconds' %(time.time()-tt))
av_area = av_area[av_area.DF_UID.isin(irrad.DF_UID.values)].merge( roofs[roofs.DF_UID.isin(irrad.DF_UID.values)],
on = 'DF_UID', how = 'left' )
av_area['panelled_area'] = av_area.available_area / cosd( av_area.panel_tilt - av_area.NEIGUNG )
print('Merged roof info with available area')
## ===== PREPARE PV OUTPUT ===== ##
tt = time.time()
print('\nComputing PV potential ...')
# First get all required variables for computation of PV output
pv_output = irrad.tilted_irradiance.to_dataset()
pv_output['panelled_area'] = av_area.set_index('DF_UID').to_xarray().panelled_area
pv_output['tilted_area'] = av_area.set_index('DF_UID').to_xarray().tilted_area
pv_output['available_area'] = av_area.set_index('DF_UID').to_xarray().available_area
pv_output['efficiency'] = eff['module_eff']
pv_output['performance_factor'] = eff['PF_mean']
# Compute total PV output
pv_output['pv_potential'] = ( pv_output.tilted_irradiance
* pv_output.panelled_area
* pv_output.efficiency
* pv_output.performance_factor )
print('Finished in %.3f seconds' %(time.time()-tt))
tt = time.time()
print('\nComputing yearly sum ...')
# Compute yearly sums
pv_output['yearly_PV_kWh'] = get_yearly_sum( pv_output.pv_potential, week_weighting = False ) / 1000
print(pv_output)
print('Finished in %.3f seconds' %(time.time()-tt))
tt = time.time()
print('\nSaving output ...')
pv_save = pv_output[['pv_potential', 'yearly_PV_kWh']]
# reduce storage requirement
pv_save['pv_potential' ] = pv_save['pv_potential' ].astype('float32')
pv_save['yearly_PV_kWh'] = pv_save['yearly_PV_kWh'].astype('float32')
print(pv_save)
pv_save.to_netcdf( OUTFILE_FP )
print('Saved output in %s (%.3fs)' %(OUTFILE_FP, time.time()-tt))
irrad_E.close()
irrad_W.close()
eff.close()
print('\nDONE - total execution time: %.3f seconds' %(time.time()-t0))
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