from sscapi import PySSC import matplotlib.pyplot as plot # setup data structures ssc = PySSC() dat = ssc.data_create() # required inputs ssc.data_set_string(dat, "file_name", "daggett.tm2") ssc.data_set_number(dat, "system_size", 4) ssc.data_set_number(dat, "derate", 0.77) ssc.data_set_number(dat, "track_mode", 2) ssc.data_set_number(dat, "azimuth", 180) ssc.data_set_number(dat, "tilt_eq_lat", 1) # default inputs exposed ssc.data_set_number(dat, 'rotlim', 45.0) ssc.data_set_number(dat, 't_noct', 45.0) ssc.data_set_number(dat, 't_ref', 25.0) ssc.data_set_number(dat, 'gamma', -0.5) ssc.data_set_number(dat, 'inv_eff', 0.92) ssc.data_set_number(dat, 'fd', 1.0) ssc.data_set_number(dat, 'i_ref', 1000) ssc.data_set_number(dat, 'poa_cutin', 0) ssc.data_set_number(dat, 'w_stow', 0) # complicated optional inputs # ssc.data_set_array(dat, 'shading_hourly', ...) Hourly beam shading factors # ssc.data_set_matrix(dat, 'shading_mxh', ...) Month x Hour beam shading factors # ssc.data_set_matrix(dat, 'shading_azal', ...) Azimuth x altitude beam shading factors # ssc.data_set_number(dat, 'shading_diff', ...) Diffuse shading factor # ssc.data_set_number(dat, 'enable_user_poa', ...) Enable user-defined POA irradiance input = 0 or 1 # ssc.data_set_array(dat, 'user_poa', ...) User-defined POA irradiance in W/m2
from sscapi import PySSC
# setup inputs
ssc = PySSC()
dat = ssc.data_create()
ssc.data_set_string(dat, "file_name", "daggett.tm2")
ssc.data_set_number(dat, "system_size", 4)
ssc.data_set_number(dat, "derate", 0.77)
ssc.data_set_number(dat, "track_mode", 0)
ssc.data_set_number(dat, "azimuth", 180)
ssc.data_set_number(dat, "tilt_eq_lat", 1)
ssc.data_set_matrix(dat, "shading_mxh",
[[
0, 0, 0, 0, 0, 0, 0, 0, 0.475, 0.95, 1, 1, 0.7875,
0.2375, 0.25, 0.3625, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0.4875, 1, 1, 1, 0.925,
0.6375, 0.6625, 0.225, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0.15, 0.925, 1, 1, 1, 1, 1, 0.75,
0.2, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0.45, 0.9125, 1, 1, 1, 1, 1,
0.625, 0.375, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0.075, 0.05, 0.7875, 1, 1, 1, 1, 1, 1,
0.625, 0.4875, 0.025, 0, 0, 0, 0, 0, 0, 0
from sscapi import PySSC from matplotlib import pyplot as pyp # setup data structures. ssc = PySSC() dat = ssc.data_create() # required inputs. ssc.data_set_string(dat, "file_name", "daggett.tm2") # todo: take hot water array input from GUI. hotWaterArray = 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05,7.26,7.52,8.84,11.6,13.7,13.8,12.5,11,8.65,5.85,2.7,1.27,0.951,1.11,2.31,7.74,17.8,19.9,18.1,15.4,12.8,10.7,9.1,8.05,7.26,7.52,8.84,11.6,13.7,13.8,12.5,11,8.65,5.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,1.27,0.951,1.11,2.31,7.74,17.8,19.9,18.1,15.4,12.8,10.7,9.1,8.05,7.26,7.52,8.84,11.6,13.7,13.8,12.5,11,8.65,5.85,2.7,1.27,0.951,1.11,2.31,7.74,17.8,19.9,18.1,15.4,12.8,10.7,9.1,8.05,7.26,7.52,8.84,11.6,13.7,13.8,12.5,11,8.65,5.85,2.7,1.27,0.951,1.11,2.31,7.74,17.8,19.9,18.1,15.4,12.8,10.7,9.1,8.05,7.26,7.52,8.84,11.6,13.7,13.8,12.5,11,8.65,5.85,2.7,1.27,0.951,1.11,2.31,7.74,17.8,19.9,18.1,15.4,12.8,10.7,9.1,8.05,7.26,7.52,8.84,11.6,13.7,13.8,12.5,11,8.65,5.85,2.97,1.4,1.05,1.22,2.54,8.54,19.7,22,20,16.9,14.1,11.8,10,8.88,8,8.29,9.74,12.8,15.1,15.2,13.8,12.1,9.53,6.45,2.97,1.4,1.05,1.22,2.54,8.54,19.7,22,20,16.9,14.1,11.8,10,8.88,8,8.29,9.74,12.8,15.1,15.2,13.8,12.1,9.53,6.45] ssc.data_set_array(dat,"scaled_draw", hotWaterArray) #Hot water draw kg/hr LENGTH=8760 ssc.data_set_number(dat, "tilt", 30) #Collector tilt deg MIN=0,MAX=90 ssc.data_set_number(dat, "azimuth", 0) #Collector azimuth deg 90=E,180=S MIN=0,MAX=360 ssc.data_set_number(dat, "albedo", 0.15) #Ground reflectance factor 0..1 FACTOR ssc.data_set_number(dat, "irrad_mode", 0) #Irradiance input mode 0/1 Beam+Diff,Global+Beam ?=0 INTEGER,MIN=0,MAX=1 ssc.data_set_number(dat, "sky_model", 2) #Tilted surface irradiance model 0/1/2 Isotropic,HDKR,Perez ?=2 INTEGER,MIN=0,MAX=2 ssc.data_set_number(dat, "mdot", 0.0617) #Total system mass flow rate kg/s POSITIVE ssc.data_set_number(dat, "ncoll", 4) #Number of collectors Unitless POSITIVE,INTEGER ssc.data_set_number(dat, "fluid", 0) #Working fluid in system Water,Glycol (INTEGER,MIN=0,MAX=1) ssc.data_set_number(dat, "area_coll", 11.2) #Single collector area m2 POSITIVE ssc.data_set_number(dat, "test_fluid", 0) #Fluid used in collector test Water,Glycol (INTEGER,MIN=0,MAX=1) ssc.data_set_number(dat, "test_flow", 0.0417) #Flow rate used in collector test kg/s POSITIVE ssc.data_set_number(dat, "pipe_length", 20) #Length of piping in system m POSITIVE ssc.data_set_number(dat, "pipe_diam", 0.07) #Pipe diameter m POSITIVE ssc.data_set_number(dat, "pipe_k", 0.02) #Pipe insulation conductivity W/m2.C POSITIVE ssc.data_set_number(dat, "pipe_insul", 0.1) #Pipe insulation thickness m POSITIVE ssc.data_set_number(dat, "tank_h2d_ratio", 3) #Solar tank height to diameter ratio Unitless POSITIVE ssc.data_set_number(dat, "U_tank", 3.553) #Solar tank heat loss coefficient W/m2K POSITIVE ssc.data_set_number(dat, "V_tank", 1) #Solar tank volume m3 POSITIVE ssc.data_set_number(dat, "hx_eff", 0.5) #Heat exchanger effectiveness 0..1 POSITIVE ssc.data_set_number(dat, "T_room", 18.3) #Ambient temperature in mech room C POSITIVE
print num_series print num_inverters # set the weather file. the web service should take a # lat-long and use the perez satellite data or tmy2/3 data # in the same way that the PVWatts service specifies the weather data # --> essentially, this service and PVWatts should use exactly the same # method to get weather data for a location request ssc = PySSC() data = ssc.data_create() ssc.data_set_string(data, 'weather_file', 'daggett.tm2' ) ssc.data_set_number(data, 'ac_derate', ac_derate ) ssc.data_set_number(data, 'modules_per_string', num_series ) ssc.data_set_number(data, 'strings_in_parallel', num_parallel ) ssc.data_set_number(data, 'inverter_count', num_inverters ) ssc.data_set_number(data, 'subarray1_tilt', tilt ) ssc.data_set_number(data, 'subarray1_azimuth', azimuth ) ssc.data_set_number(data, 'subarray1_track_mode', track_mode ) ssc.data_set_matrix(data, 'subarray1_shading_mxh', shading_mxh ) ssc.data_set_array(data, 'subarray1_soiling', [soiling, soiling, soiling, soiling, soiling, soiling, soiling, soiling, soiling, soiling, soiling, soiling ] ) ssc.data_set_number(data, 'subarray1_derate', dc_derate ) # set up values for other sub arrays - not used (currently) ssc.data_set_number(data, 'subarray2_tilt', 0 ) ssc.data_set_number(data, 'subarray3_tilt', 0 ) ssc.data_set_number(data, 'subarray4_tilt', 0 )
from sscapi import PySSC
''' This does not work because of API failures as of 3/22/2013 '''
ssc = PySSC()
data = ssc.data_create()
# Test windwatts with skystream 2.4/3.7 hybrid
ssc.data_set_string(data, 'file_name', 'WY Southern-Flat Lands.srw')
ssc.data_set_number(data, 'ctl_mode', 2)
ssc.data_set_number(data, 'cutin', 4)
ssc.data_set_number(data, 'hub_ht', 50)
ssc.data_set_number(data, 'lossc', 0)
ssc.data_set_number(data, 'lossp', 0)
pc_wind = [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39
]
ssc.data_set_array(data, 'pc_wind', pc_wind)
pc_power = [
0, 0, 0, 0, 0.08, 0.02, 0.35, 0.6, 1, 1.6, 2, 2.25, 2.35, 2.4, 2.4, 2.37,
2.3, 2.09, 2, 2, 2, 2, 2, 1.98, 1.95, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0
]
ssc.data_set_array(data, 'pc_power', pc_power)
ssc.data_set_number(data, 'rotor_di', 3.7)
ssc.data_set_number(data, 'shear', 0.14)
ssc.data_set_number(data, 'turbul', 0.1)
ssc.data_set_array(data, 'wt_x', [0])
ssc.data_set_array(data, 'wt_y', [0])
ssc.data_set_number(data, 'wake_model', 0)
ssc.data_set_number(data, 'model_choice', 0)
from sscapi import PySSC import matplotlib.pyplot as plot # setup data structures ssc = PySSC() dat = ssc.data_create() # required inputs ssc.data_set_string(dat, "file_name", "daggett.tm2") ssc.data_set_number(dat, "system_size", 4) ssc.data_set_number(dat, "derate", 0.77) ssc.data_set_number(dat, "track_mode", 2) ssc.data_set_number(dat, "azimuth", 180) ssc.data_set_number(dat, "tilt_eq_lat", 1) # default inputs exposed ssc.data_set_number(dat, 'rotlim', 45.0) ssc.data_set_number(dat, 't_noct', 45.0) ssc.data_set_number(dat, 't_ref', 25.0) ssc.data_set_number(dat, 'gamma', -0.5) ssc.data_set_number(dat, 'inv_eff', 0.92) ssc.data_set_number(dat, 'fd', 1.0) ssc.data_set_number(dat, 'i_ref', 1000) ssc.data_set_number(dat, 'poa_cutin', 0) ssc.data_set_number(dat, 'w_stow', 0) # complicated optional inputs # ssc.data_set_array(dat, 'shading_hourly', ...) Hourly beam shading factors # ssc.data_set_matrix(dat, 'shading_mxh', ...) Month x Hour beam shading factors # ssc.data_set_matrix(dat, 'shading_azal', ...) Azimuth x altitude beam shading factors # ssc.data_set_number(dat, 'shading_diff', ...) Diffuse shading factor # ssc.data_set_number(dat, 'enable_user_poa', ...) Enable user-defined POA irradiance input = 0 or 1 # ssc.data_set_array(dat, 'user_poa', ...) User-defined POA irradiance in W/m2
from sscapi import PySSC
# setup inputs
ssc = PySSC()
dat = ssc.data_create()
ssc.data_set_string(dat, "file_name", "daggett.tm2")
ssc.data_set_number(dat, "system_size", 4)
ssc.data_set_number(dat, "derate", 0.77)
ssc.data_set_number(dat, "track_mode", 0)
ssc.data_set_number(dat, "azimuth", 180)
ssc.data_set_number(dat, "tilt_eq_lat", 1)
ssc.data_set_array(dat, "shading_hourly", [0.9 for x in range(0,8760)])
# run PV system simulation
mod = ssc.module_create("pvwattsv1")
ssc.module_exec(mod, dat)
# print results
ann = 0
ac = ssc.data_get_array(dat, "ac")
for i in range(len(ac)):
ac[i] = ac[i]/1000
ann += ac[i]
print "PVWatts V1 Simulation ok, e_net (annual kW)=", ann
print num_parallel
print num_series
print num_inverters
# set the weather file. the web service should take a
# lat-long and use the perez satellite data or tmy2/3 data
# in the same way that the PVWatts service specifies the weather data
# --> essentially, this service and PVWatts should use exactly the same
# method to get weather data for a location request
ssc = PySSC()
data = ssc.data_create()
ssc.data_set_string(data, 'weather_file', 'daggett.tm2')
ssc.data_set_number(data, 'ac_derate', ac_derate)
ssc.data_set_number(data, 'modules_per_string', num_series)
ssc.data_set_number(data, 'strings_in_parallel', num_parallel)
ssc.data_set_number(data, 'inverter_count', num_inverters)
ssc.data_set_number(data, 'subarray1_tilt', tilt)
ssc.data_set_number(data, 'subarray1_azimuth', azimuth)
ssc.data_set_number(data, 'subarray1_track_mode', track_mode)
ssc.data_set_matrix(data, 'subarray1_shading_mxh', shading_mxh)
ssc.data_set_array(data, 'subarray1_soiling', [
soiling, soiling, soiling, soiling, soiling, soiling, soiling, soiling,
soiling, soiling, soiling, soiling
])
ssc.data_set_number(data, 'subarray1_derate', dc_derate)
# set up values for other sub arrays - not used (currently)
ssc.data_set_number(data, 'subarray2_tilt', 0)
def sam_simulation(nsrdb_data, meta=None, verbose=False, **kwargs):
"""SAM solar PV simulation
Perform a PVWATTS5 simulation using some input information about the
solar plant.
Parameters
----------
weather (pd.DataFrame): Solar radiation dataframe
meta (pd.DataFrame): NSRDB metadata
kwargs (dictionary): Dictionary containing simulation parameters
Returns
----------
CF (float): Capacity factor
Generation (float): Generation over the year of simulation
meto_data (pd.DataFrame): Dataframe with hourly generation
"""
params = {
"lat": kwargs["lat"],
"lng": kwargs["lon"],
"system_capacity": kwargs["system_capacity"],
"dc_ac_ratio": kwargs["dc_ac_ratio"],
"inv_eff": kwargs["inv_eff"],
"losses": kwargs["losses"],
"tilt": kwargs["tilt"],
"gcr": kwargs["gcr"],
"azimuth": kwargs["azimuth"],
"interval": kwargs["interval"],
}
if verbose:
print({key: value for key, value in params.items()})
# Start sscapi module
ssc = PySSC()
weather_data = ssc.data_create()
valid_keys = [
"lat",
"lon",
"tz",
"elev",
]
for key, value in kwargs.items():
bytestr = key.encode() # Convert string to byte to read it on C
if key in valid_keys:
ssc.data_set_number(weather_data, bytestr, value)
# Set tilt of system in degrees
ssc.data_set_array(weather_data, b"year", nsrdb_data.index.year)
ssc.data_set_array(weather_data, b"month", nsrdb_data.index.month)
ssc.data_set_array(weather_data, b"day", nsrdb_data.index.day)
ssc.data_set_array(weather_data, b"hour", nsrdb_data.index.hour)
ssc.data_set_array(weather_data, b"minute", nsrdb_data.index.minute)
ssc.data_set_array(weather_data, b"dn", nsrdb_data["DNI"])
ssc.data_set_array(weather_data, b"df", nsrdb_data["DHI"])
ssc.data_set_array(weather_data, b"wspd", nsrdb_data["Wind Speed"])
ssc.data_set_array(weather_data, b"tdry", nsrdb_data["Temperature"])
# Create SAM compliant object
sam_data = ssc.data_create()
ssc.data_set_table(sam_data, b"solar_resource_data", weather_data)
ssc.data_free(weather_data)
valid_keys = [
"system_capacity", # kW
"dc_ac_ratio",
"array_type",
"inv_eff",
"losses",
"gcr",
"tilt",
"azimuth",
"interval",
"adjust:constant",
]
for key, value in kwargs.items():
bytestr = key.encode() # Convert string to byte to read it on C
if key in valid_keys:
ssc.data_set_number(sam_data, bytestr, value)
if kwargs["model"] == "pvwattsv7" and "module_type" in kwargs:
ssc.data_set_number(sam_data, b"module_type",
kwargs.get("module_type", 0))
mod = ssc.module_create(kwargs.get("model").encode())
ssc.module_exec(mod, sam_data)
nsrdb_data["ac_generation_W"] = np.array(
ssc.data_get_array(sam_data, b"ac"))
nsrdb_data["dc_generation_W"] = np.array(
ssc.data_get_array(sam_data, b"dc"))
nsrdb_data["dc_capacity_factor"] = (nsrdb_data["dc_generation_W"] *
1e3) / kwargs["system_capacity"]
nsrdb_data["POA"] = np.array(ssc.data_get_array(sam_data, b"poa"))
# Module temperature in ºC
nsrdb_data["TCell"] = np.array(ssc.data_get_array(sam_data, b"tcell"))
# free the memory
ssc.data_free(sam_data)
ssc.module_free(mod)
return nsrdb_data
from sscapi import PySSC ''' Runs but returns nonsense data. ''' # setup inputs ssc = PySSC() dat = ssc.data_create() ssc.data_set_string(dat, "file_name", "daggett.tm2") ssc.data_set_number(dat, "resource_potential", 910) #Resource Potential MW ssc.data_set_number(dat, "resource_type", 0) #Type of Resource INTEGER ssc.data_set_number(dat, "resource_temp", 200) #Resource Temperature C ssc.data_set_number(dat, "resource_depth", 2000) #Resource Depth m ssc.data_set_number(dat, "analysis_period", 25) #Analysis Lifetime years INTEGER ssc.data_set_number(dat, "model_choice", 0) #Which model to run (0,1,2) INTEGER ssc.data_set_number(dat, "nameplate", 15000) #Desired plant output kW ssc.data_set_number(dat, "analysis_type", 0) #Analysis Type INTEGER ssc.data_set_number(dat, "num_wells", 3) #Number of Wells ssc.data_set_number(dat, "num_wells_getem", 3) #Number of Wells GETEM calc'd ssc.data_set_number(dat, "conversion_type", 0) #Conversion Type INTEGER ssc.data_set_number(dat, "plant_efficiency_input", 11.28) #Plant efficiency ssc.data_set_number(dat, "conversion_subtype", 0) #Conversion Subtype INTEGER ssc.data_set_number(dat, "decline_type", 0) #Temp decline Type INTEGER ssc.data_set_number(dat, "temp_decline_rate", 0.03) #Temperature decline rate %/yr ssc.data_set_number(dat, "temp_decline_max", 30) #Maximum temperature decline C ssc.data_set_number(dat, "wet_bulb_temp", 15) #Wet Bulb Temperature C ssc.data_set_number(dat, "ambient_pressure", 14.7) #Ambient pressure psi ssc.data_set_number(dat, "well_flow_rate", 70) #Production flow rate per well kg/s ssc.data_set_number(dat, "pump_efficiency", 0.60) #Pump efficiency % ssc.data_set_number(dat, "delta_pressure_equip", 25) #Delta pressure across surface equipment psi ssc.data_set_number(dat, "excess_pressure_pump", 50.76) #Excess pressure @ pump suction psi ssc.data_set_number(dat, "well_diameter", 10) #Production well diameter in
from sscapi import PySSC
''' Runs but returns nonsense data. '''
# setup inputs
ssc = PySSC()
dat = ssc.data_create()
ssc.data_set_string(dat, "file_name", "daggett.tm2")
ssc.data_set_number(dat, "resource_potential", 910) #Resource Potential MW
ssc.data_set_number(dat, "resource_type", 0) #Type of Resource INTEGER
ssc.data_set_number(dat, "resource_temp", 200) #Resource Temperature C
ssc.data_set_number(dat, "resource_depth", 2000) #Resource Depth m
ssc.data_set_number(dat, "analysis_period",
25) #Analysis Lifetime years INTEGER
ssc.data_set_number(dat, "model_choice",
0) #Which model to run (0,1,2) INTEGER
ssc.data_set_number(dat, "nameplate", 15000) #Desired plant output kW
ssc.data_set_number(dat, "analysis_type", 0) #Analysis Type INTEGER
ssc.data_set_number(dat, "num_wells", 3) #Number of Wells
ssc.data_set_number(dat, "num_wells_getem", 3) #Number of Wells GETEM calc'd
ssc.data_set_number(dat, "conversion_type", 0) #Conversion Type INTEGER
ssc.data_set_number(dat, "plant_efficiency_input", 11.28) #Plant efficiency
ssc.data_set_number(dat, "conversion_subtype",
0) #Conversion Subtype INTEGER
ssc.data_set_number(dat, "decline_type", 0) #Temp decline Type INTEGER
ssc.data_set_number(dat, "temp_decline_rate",
0.03) #Temperature decline rate %/yr
ssc.data_set_number(dat, "temp_decline_max",
30) #Maximum temperature decline C
ssc.data_set_number(dat, "wet_bulb_temp", 15) #Wet Bulb Temperature C
ssc.data_set_number(dat, "ambient_pressure", 14.7) #Ambient pressure psi
ssc.data_set_number(dat, "well_flow_rate",