I'm trying to use PVLIB to estimate output power for a PV System installed in the west of my country.
As an example I've got 2 days of hourly GHI, 2m Temperature and 10m wind speed from MERRA2 reanalysis.
I want to estimate how much power a fixed PV System or 1 axis tracking system would generate using the forementioned dataset, and ModelChain function from PVLIB. I first estimate DNI and DHI from GHI data using DISC model to obtain DNI and then DHI is the difference between GHI and DNI*cos(Z)
a) First behaviour I am not completely sure if it is Ok. Here is the plot of GHI, DNI , DHI, T2m and Wind Speed. It seems that DNI is shifted with its maximum occurring 1 hour before GHI maximum.
After preparing irradiance data I calculated AC using Model Chain, specifying the fixed PV System and 1 axis single tracking system. The thing is that I don't trust in the AC output for a 1-single axis system. I expected a plateau shape of AC output and i found a kind of weird behaviour.
Here is the otuput values of power generation i expected to see:
And here is the estimated output by PVLIB
I hope someone can help me to find the error on my proccedure.
Here is the code:
# =============================================================================
# Example of using MERRA2 data and PVLIB
# =============================================================================
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import pvlib
from pvlib.pvsystem import PVSystem
from pvlib.location import Location
from pvlib.modelchain import ModelChain
# =============================================================================
# 1) Create small data set extracted from MERRA
# =============================================================================
GHI = np.array([0,0,0,0,0,0,0,0,0,10.8,148.8,361,583,791.5,998.5,1105.5,1146.5,1118.5,1023.5,
860.2,650.2,377.1,165.1,16,0,0,0,0,0,0,0,0,0,11.3,166.2,395.8,624.5,827,986,
1065.5,1079,1025.5,941.5,777,581.5,378.9,156.2,20.6,0,0,0,0])
temp_air = np.array([21.5,20.5,19.7,19.6,18.8,17.9,17.1,16.5,16.2,16.2,17,21.3,24.7,26.9,28.8,30.5,
31.6,32.4,33,33.3,32.9,32,30.6,28.7,25.4,23.9,22.6,21.2,20.3,19.9,19.5,19.1,18.4,
17.7,18.3,23,25.1,27.3,29.5,31.2,32.1,32.6,32.6,32.5,31.8,30.7,29.6,28.1,24.6,22.9,
22.3,23.2])
wind_speed = np.array([3.1,2.7,2.5,2.6,2.8,3,3,3,2.8,2.5,2.1,1,2.2,3.7,4.8,5.6,6.1,6.4,6.5,6.6,6.3,5.8,5.3,
3.7,3.9,4,3.6,3.4,3.4,3,2.6,2.3,2.1,2,2.2,2.7,3.2,4.3,5.1,5.6,5.7,5.8,5.8,5.7,5.4,4.8,
4.4,3.1,2.7,2.3,1.1,0.6])
local_timestamp = pd.DatetimeIndex(start='1979-12-31 21:00', end='1980-01-03 00:00', freq='1h',tz='America/Argentina/Buenos_Aires')
d = {'ghi':GHI,'temp_air':temp_air,'wind_speed':wind_speed}
data = pd.DataFrame(data=d)
data.index = local_timestamp
lat = -31.983
lon = -68.530
location = Location(latitude = lat,
longitude = lon,
tz = 'America/Argentina/Buenos_Aires',
altitude = 601)
# =============================================================================
# 2) SOLAR POSITION AND ATMOSPHERIC MODELING
# =============================================================================
solpos = pvlib.solarposition.get_solarposition(time = local_timestamp,
latitude = lat,
longitude = lon,
altitude = 601)
# DNI and DHI calculation from GHI data
DNI = pvlib.irradiance.disc(ghi = data.ghi,
solar_zenith = solpos.zenith,
datetime_or_doy = local_timestamp)
DHI = data.ghi - DNI.dni*np.cos(np.radians(solpos.zenith.values))
d = {'ghi': data.ghi,'dni': DNI.dni,'dhi': DHI,'temp_air':data.temp_air,'wind_speed':data.wind_speed }
weather = pd.DataFrame(data=d)
plt.plot(weather)
# =============================================================================
# 3) SYSTEM SPECIFICATIONS
# =============================================================================
# load some module and inverter specifications
sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod')
cec_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')
sandia_module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']
cec_inverter = cec_inverters['Power_Electronics__FS2400CU15__645V__645V__CEC_2018_']
# Fixed system with tilt=abs(lat)-10
f_system = PVSystem( surface_tilt = abs(lat)-10,
surface_azimuth = 0,
module = sandia_module,
inverter = cec_inverter,
module_parameters = sandia_module,
inverter_parameters = cec_inverter,
albedo = 0.20,
modules_per_string = 100,
strings_per_inverter = 100)
# 1 axis tracking system
t_system = pvlib.tracking.SingleAxisTracker(axis_tilt = 0, #abs(-33.5)-10
axis_azimuth = 0,
max_angle = 52,
backtrack = True,
module = sandia_module,
inverter = cec_inverter,
module_parameters = sandia_module,
inverter_parameters = cec_inverter,
name = 'tracking',
gcr = .3,
modules_per_string = 100,
strings_per_inverter = 100)
# =============================================================================
# 4) MODEL CHAIN USING ALL THE SPECIFICATIONS for a fixed and 1 axis tracking systems
# =============================================================================
mc_f = ModelChain(f_system, location)
mc_t = ModelChain(t_system, location)
# Next, we run a model with some simple weather data.
mc_f.run_model(times=weather.index, weather=weather)
mc_t.run_model(times=weather.index, weather=weather)
# =============================================================================
# 5) Get only AC output form a fixed and 1 axis tracking systems and assign
# 0 values to each NaN
# =============================================================================
d = {'fixed':mc_f.ac,'tracking':mc_t.ac}
AC = pd.DataFrame(data=d)
i = np.isnan(AC.tracking)
AC.tracking[i] = 0
i = np.isnan(AC.fixed)
AC.fixed[i] = 0
plt.plot(AC)
I hope anyone could help me with the intepretation of the results and debugging of the code.
Thanks a lot!