def __init__(
self,
weather_dataframe, # weather data from NSRDB (pd.DataFrame)
weather_meta_data, # meta data for the weather data file above (pd.DataFrame)
system_capacity_dc=1, # DC system capacity (kW or MW)
dc_ac_ratio=1.1, # Set DC/AC ratio (or inverter loading ratio). See https://sam.nrel.gov/sites/default/files/content/virtual_conf_july_2013/07-sam-virtual-conference-2013-woodcock.pdf
tilt=0, # tilt of system in degrees (0 = horizontal)
azimuth=180, # azimuth angle (in degrees) from north (180 = south facing)
inv_eff=96, # inverter efficiency in percent
losses=14.0757, # system losses in percent (soiling, shading, wiring, etc.)
array_type=0, # specify fixed tilt system (0=Fixed, 1=Fixed Roof, 2=1 Axis Tracker, 3=Backtracted, 4=2 Axis Tracker)
gcr=0.4, # ground coverage ratio
adjust_constant=0 # constant loss adjustment
):
# Set up Software Simulation Core (SSC) Object
ssc = sscapi.PySSC()
# Set Up Data Containers
dat = ssc.data_create() # container for all input data
wfd = ssc.data_create() # weather file data container
# Fill wfd container with weather data
ssc.data_set_number(wfd, 'lat', weather_meta_data['Latitude'])
ssc.data_set_number(wfd, 'lon', weather_meta_data['Longitude'])
ssc.data_set_number(wfd, 'tz', weather_meta_data['Local Time Zone'])
ssc.data_set_number(wfd, 'elev', weather_meta_data['Elevation'])
ssc.data_set_array(wfd, 'year', weather_dataframe.index.year)
ssc.data_set_array(wfd, 'month', weather_dataframe.index.month)
ssc.data_set_array(wfd, 'day', weather_dataframe.index.day)
ssc.data_set_array(wfd, 'hour', weather_dataframe.index.hour)
ssc.data_set_array(wfd, 'minute', weather_dataframe.index.minute)
ssc.data_set_array(wfd, 'dn', weather_dataframe['DNI'])
ssc.data_set_array(wfd, 'df', weather_dataframe['DHI'])
ssc.data_set_array(wfd, 'wspd', weather_dataframe['Wind Speed'])
ssc.data_set_array(wfd, 'tdry', weather_dataframe['Temperature'])
# Add wfd container to dat container
ssc.data_set_table(dat, 'solar_resource_data', wfd)
ssc.data_free(wfd)
# Specify the system Configuration
ssc.data_set_number(dat, 'system_capacity', system_capacity_dc)
ssc.data_set_number(dat, 'dc_ac_ratio', dc_ac_ratio)
ssc.data_set_number(dat, 'tilt', tilt)
ssc.data_set_number(dat, 'azimuth', azimuth)
ssc.data_set_number(dat, 'inv_eff', inv_eff)
ssc.data_set_number(dat, 'losses', losses)
ssc.data_set_number(dat, 'array_type', array_type)
ssc.data_set_number(dat, 'gcr', gcr)
ssc.data_set_number(dat, 'adjust:constant', adjust_constant)
# Add the software simulation core object and the input data object as attributes to the PVSystem object
self.ssc = ssc
self.dat = dat
def sam_simulation(meteo_data, verbose=False, **kwargs):
""" SAM performance simulation
Perform a PVWATTS simulation using the SAM python implementation.
Args:
meteo_data (pd.DataFrame): Solar radiation dataframe
kwargs (dictionary): Dictionary containing simulation parameters
Returns:
CP (float): Capacity factor
Generation (float): Generation over the year of simulation
meto_data (pd.DataFrame): Dataframe with hourly generation
"""
params = {
'lat': kwargs['lat'],
'lon': kwargs['lng'],
'timezone': kwargs['timezone'],
'elevation': kwargs['elevation'],
'sys_capacity': kwargs['system_capacity'],
'dc_ac_ratio': kwargs['dc_ac_ratio'],
'inv_eff': kwargs['inv_eff'],
'losses': kwargs['losses'],
'configuration': kwargs['configuration'],
'tilt': kwargs['tilt'],
}
if verbose:
print({key: type(value) for key, value in params.items()})
ssc = sscapi.PySSC()
wfd = ssc.data_create()
ssc.data_set_number(wfd, 'lat', params['lat'])
ssc.data_set_number(wfd, 'lon', params['lon'])
ssc.data_set_number(wfd, 'tz', params['timezone'])
ssc.data_set_number(wfd, 'elev', params['elevation'])
ssc.data_set_array(wfd, 'year', meteo_data.index.year)
ssc.data_set_array(wfd, 'month', meteo_data.index.month)
ssc.data_set_array(wfd, 'day', meteo_data.index.day)
ssc.data_set_array(wfd, 'hour', meteo_data.index.hour)
ssc.data_set_array(wfd, 'minute', meteo_data.index.minute)
ssc.data_set_array(wfd, 'dn', meteo_data['DNI'])
ssc.data_set_array(wfd, 'df', meteo_data['DHI'])
ssc.data_set_array(wfd, 'wspd', meteo_data['Wind Speed'])
ssc.data_set_array(wfd, 'tdry', meteo_data['Temperature'])
# Create SAM compliant object
dat = ssc.data_create()
ssc.data_set_table(dat, 'solar_resource_data', wfd)
ssc.data_free(wfd)
# Set system capacity in MW
ssc.data_set_number(dat, 'system_capacity', params['sys_capacity'])
# Set DC/AC ratio (or power ratio). See https://sam.nrel.gov/sites/default/files/content/virtual_conf_july_2013/07-sam-virtual-conference-2013-woodcock.pdf
ssc.data_set_number(dat, 'dc_ac_ratio', params['dc_ac_ratio'])
# Set tilt of system in degrees
ssc.data_set_number(dat, 'tilt', params['tilt'])
# Set azimuth angle (in degrees) from north (0 degrees)
ssc.data_set_number(dat, 'azimuth', 180)
# Set the inverter efficency
ssc.data_set_number(dat, 'inv_eff', params['inv_eff'])
# Set the system losses, in percent
ssc.data_set_number(dat, 'losses', params['losses'])
# Set ground coverage ratio
ssc.data_set_number(dat, 'gcr', 0.4)
# Set constant loss adjustment
ssc.data_set_number(dat, 'adjust:constant', 0)
system_capacity = params['sys_capacity']
value = params['configuration']
if isinstance(params['configuration'], dict):
d = {}
for key, val in value.iteritems():
ssc.data_set_number(dat, 'array_type', val)
# execute and put generation results back into dataframe
mod = ssc.module_create('pvwattsv5')
ssc.module_exec(mod, dat)
meteo_data['generation'] = np.array(ssc.data_get_array(dat, 'gen'))
CP = meteo_data['generation'].sum() / (525600 / int('60') *
system_capacity)
generation = meteo_data['generation'].sum()
d[key] = CP
d['gen_' + key] = generation
ssc.data_free(dat)
ssc.module_free(mod)
return (d)
else:
ssc.data_set_number(dat, 'array_type', value)
# execute and put generation results back into dataframe
mod = ssc.module_create('pvwattsv5')
ssc.module_exec(mod, dat)
meteo_data['generation'] = np.array(ssc.data_get_array(dat, 'gen'))
meteo_data['cf'] = meteo_data['generation'] / system_capacity
CP = meteo_data['generation'].sum() / (525600 / int('60') *
system_capacity)
generation = meteo_data['generation'].sum()
click.secho('Output data from SAM.', fg='green')
click.secho(f'Capacity Factor: {CP}', fg='blue')
click.secho(f'Annual Generation: {generation}', fg='blue')
ssc.data_free(dat)
ssc.module_free(mod)
meteo_data.to_csv(output_path / 'test.csv')
return (meteo_data, (CP, generation))
return (True)
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 11 17:50:06 2016
@author: Koats
"""
import sscapi
ssc = sscapi.PySSC()
dat = ssc.data_create()
def simparam():
def subaltset():
def subarray(angle):
def loss():
def get_csvs_simulations (mesh, path, year, interval, kw, tiltc):
mesh = pd.read_csv(mesh, header = -1, encoding = "ISO-8859-1")[1:]
for i, val in tqdm (enumerate(mesh[1]), desc = "Counties in Mexico completed for this day/dataframe"):
info = pd.read_csv(str(path + '/' + str(year) + 'meta/' + mesh[1].iloc[i] + '.csv'), nrows = 1)
timezone = info['Local Time Zone']
elevation = info['Elevation']
df = pd.read_csv(str(path + '/' + str(year) + '/' + mesh[1].iloc[i] + '.csv'))
latitude = float(mesh[2].iloc[i])
longitude = float(mesh[3].iloc[i])
kw = int(kw)
year = df['Year'][i]
month = df['Month'][i]
day = df['Day'][i]
hour = df['Hour'][i]
minute = df['Minute'][i]
df = df.set_index(pd.date_range('1/1/{yr} 00:00'.format(yr = year), freq = interval + 'Min', periods = 525600 / int(interval)))
ssc = sscapi.PySSC()
wfd = ssc.data_create()
ssc.data_set_number(wfd, 'lat'.encode('utf-8'), latitude)
ssc.data_set_number(wfd, 'lon'.encode('utf-8'), longitude)
ssc.data_set_number(wfd, 'tz'.encode('utf-8'), timezone)
ssc.data_set_number(wfd, 'elev'.encode('utf-8'), elevation)
ssc.data_set_array(wfd, 'year'.encode('utf-8'), df.index.year)
ssc.data_set_array(wfd, 'month'.encode('utf-8'), df.index.month)
ssc.data_set_array(wfd, 'day'.encode('utf-8'), df.index.day)
ssc.data_set_array(wfd, 'hour'.encode('utf-8'), df.index.hour)
ssc.data_set_array(wfd, 'minute'.encode('utf-8'), df.index.minute)
ssc.data_set_array(wfd, 'dn'.encode('utf-8'), df['DNI'])
ssc.data_set_array(wfd, 'df'.encode('utf-8'), df['DHI'])
ssc.data_set_array(wfd, 'wspd'.encode('utf-8'), df['Wind Speed'])
ssc.data_set_array(wfd, 'tdry'.encode('utf-8'), df['Temperature'])
dat = ssc.data_create()
ssc.data_set_table(dat, 'solar_resource_data'.encode('utf-8'), wfd)
ssc.data_free(wfd)
# System Capacity in KW
system_capacity = kw
ssc.data_set_number(dat, 'system_capacity'.encode('utf-8'), system_capacity)
# Set DC/AC ratio (or power ratio). See https://sam.nrel.gov/sites/default/files/content/virtual_conf_july_2013/07-sam-virtual-conference-2013-woodcock.pdf
dc_ac_ratio = 1.1
ssc.data_set_number(dat, 'dc_ac_ratio'.encode('utf-8'), dc_ac_ratio)
# Set tilt of system in degrees (default: 25)
# Optimum: (latitude * .76) + 3.1
tilt = (float(mesh[2].iloc[i])) if tiltc == "Normal" else ((float(mesh[2].iloc[1])*.76) + 3.1)
ssc.data_set_number(dat, 'tilt'.encode('utf-8'), tilt)
# Set azimuth angle (in degrees) from north (0 degrees)
azimuth = 180
ssc.data_set_number(dat, 'azimuth'.encode('utf-8'), azimuth)
# Set the inverter efficiency
inv_eff = 96
ssc.data_set_number(dat, 'inv_eff'.encode('utf-8'), inv_eff)
# Set the system losses, in percent
losses = 14.0757
ssc.data_set_number(dat, 'losses'.encode('utf-8'), losses)
# Specify fixed tilt system (1=true, 0=false)
array_type = 1
ssc.data_set_number(dat, 'array_type'.encode('utf-8'), array_type)
# Set ground coverage ratio
gcr = 0.4
ssc.data_set_number(dat, 'gcr'.encode('utf-8'), gcr)
# Set constant loss adjustment
loss_adjustment = 0
ssc.data_set_number(dat, 'adjust:constant'.encode('utf-8'), loss_adjustment)
# Execute and put generation results back into dataframe
mod = ssc.module_create('pvwattsv5'.encode('utf-8'))
ssc.module_exec(mod, dat)
df['generation'] = np.array(ssc.data_get_array(dat, 'gen'.encode('utf-8')))
# Free the memory
ssc.data_free(dat)
ssc.module_free(mod)
# Divide sum of generation by the number of periods times the system size
df['generation'].sum() / (525600/int(interval) * system_capacity)
# Total Energy:
df['generation'].sum()
df['latitude'] = latitude
df['longitude'] = longitude
df['timezone'] = np.array(timezone)[0]
df['elevation'] = np.array(elevation)[0]
systemconfig_id = 'sc' + str(system_capacity) + 'dcac' + str(dc_ac_ratio) +\
'tilt' + str(tilt) + 'az' + str(azimuth) + 'eff' + str(inv_eff) +\
'loss' + str(losses) + 'gc' + str(gcr) +\
('adj' + str(loss_adjustment) if loss_adjustment != 0 else '') +\
('fixed' if array_type == 1 else '')
df['ID_scenario'] = systemconfig_id
df.to_csv (str(path + '/' + str(year) + 'SAMgen_' + str(interval) + 'mins_' + str(float(kw)/1000.0) + 'MW_' + tiltc + '/' + mesh[1].iloc[i] + '.csv'), index = False)