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compute_PV_output_batch_SCENARIO.py
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Sun, Jun 15, 01:02

compute_PV_output_batch_SCENARIO.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_FP = sys.argv[1] # name of the tilted radiation file
ROOFS_FP = sys.argv[2] # roof scenario file (incl. 'DF_UID', 'available_area', 'NEIGUNG', 'panel_tilt')
OUTFILE_FP = sys.argv[3] # name of the output file
if len(sys.argv) > 4: # indicate if data is a sample (i.e. few roofs)
if sys.argv[4] == 'sample':
sample = True
else:
sample = False
## ===== CONSTANTS ===== ##
CHUNKSIZE = 1000
EFFICIENCY_FP = '/work/hyenergy/output/solar_potential/technical_potential/pv_efficiencies_proj_roofs.nc'
print('\nTilted irradiance file path: %s' %TILTED_IRRAD_FP)
print('Efficiency file path: %s' %EFFICIENCY_FP)
print('Output file path: %s\n\n' %OUTFILE_FP)
## =========================================================================================== ##
## ===== LOAD DATA ===== ##
tt = time.time()
t0 = time.time()
irrad = xr.open_mfdataset( TILTED_IRRAD_FP, chunks={'DF_UID': CHUNKSIZE},
combine = 'by_coords', lock = None ).sortby('DF_UID')
av_area = pd.read_csv( ROOFS_FP, usecols = ['DF_UID', 'available_area', 'NEIGUNG', 'panel_tilt'])
eff = xr.open_mfdataset( EFFICIENCY_FP, chunks={'DF_UID': CHUNKSIZE},
combine = 'by_coords', lock = None ).sel(DF_UID = irrad.DF_UID.values)
if sample:
eff = eff.compute()
print(eff)
print('Loaded all data in %.3f seconds' %(time.time()-tt))
av_area = av_area[av_area.DF_UID.isin(irrad.DF_UID.values)]
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['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
if 'day' in pv_output.dims:
pv_output['yearly_PV_kWh'] = pv_output.pv_potential.sum(dim = ['timestamp', 'day']) / 1000
else:
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')
if 'time' in pv_save.variables:
pv_save['time'] = irrad.time
print(pv_save)
pv_save.to_netcdf( OUTFILE_FP )
print('Saved output in %s (%.3fs)' %(OUTFILE_FP, time.time()-tt))
print('\nDONE - total execution time: %.3f seconds' %(time.time()-t0))

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