为什么在北半球，pvlib产生能量的最佳地表方位角不在180°左右？

Question

我正在使用开源pvlib软件（和CEC模块）构建一个模型，以估算每年的光伏发电量。我在模型中遇到了一些不一致的地方，如果社区能够提供任何疑难解答，我将不胜感激

我的主要问题是：模型告诉我，能源生产的理想北半球地表方位角（即能量输出最高的地表方位角）约为76°（正东以北），而能源生产的最差地表方位角约为270°（正西）。然而，据我所知，北半球理想的地表方位角应为180°（正南），最差的地表方位角为0°（正北）

I've included this graph to help visualize the variation in energy production based on surface_azimuth 这也是在所附代码的末尾生成的

有人能帮我纠正这个问题或纠正我的理解吗

代码复制如下以供参考

import os
import pandas as pd
import numpy as np
import os
import os.path
import matplotlib.pyplot as plt
import pvlib
from geopy.exc import GeocoderTimedOut 
from geopy.geocoders import Nominatim 
from IPython.display import Image


## GET THE LATITUDE & LONGITUDE OF A GIVEN CITY
geolocator = Nominatim(user_agent="____'s app") 
geo = geolocator.geocode("Berkeley") 
## CHECK THAT CITY IS CORRECT (by Country, State, etc.)
print(geo.address)
# CHECK THE LAT, LON order
print(geo.latitude, geo.longitude)


## SELECT THE YEAR & TIMES YOU'D LIKE TO MODEL OFF
YEAR = 2019
STARTDATE = '%d-01-01T00:00:00' % YEAR
ENDDATE = '%d-12-31T23:59:59' % YEAR
TIMES = pd.date_range(start=STARTDATE, end=ENDDATE, freq='H')

## ACCESS THE NREL API TO EXTRACT WEATHER DATA
NREL_API_KEY = os.getenv('NREL_API_KEY', 'DEMO_KEY')
## FILL IN THE BLANK WITH YOUR EMAIL ADRRESS
EMAIL = os.getenv('EMAIL', '_______.com')

##NEED TO COMMENT OUT THIS LINE BELOW -- if you call it too many times within an hour, it will break your code
header, data = pvlib.iotools.get_psm3(LATITUDE, LONGITUDE, NREL_API_KEY, EMAIL)


## SELECT THE PVLIB PANEL & INTERVTER YOU'D LIKE TO USE
## CAN ALSO CHOOSE FROM SANDIA LABS' DATASET OF PANELS & INVERTERS (check out the function)
## WE CHOSE THE CECMods because they highlighted the ones that were BIPV
INVERTERS = pvlib.pvsystem.retrieve_sam('CECInverter')
INVERTER_10K = INVERTERS['SMA_America__SB10000TL_US__240V_']
CECMODS = pvlib.pvsystem.retrieve_sam('CECMod')

## SELECT THE PANEL YOU'D LIKE TO USE (NOTE: THE PEVAFERSA MODEL IS A BIPV PANEL)
CECMOD_MONO = CECMODS['Pevafersa_America_IP_235_GG']


## CREATING AN ARRAY TO ITERATE THROUGH IN ORDER TO TEST DIFFERENT SURFACE_AZIMUTHS
heading_array = np.arange(0, 361, 2)
heading_array

heading_DF = pd.DataFrame(heading_array).rename(columns = {0: "Heading"})
heading_DF.head()


# geo IS AN OBJECT (the given city) CREATED ABOVE
LATITUDE, LONGITUDE = geo.latitude, geo.longitude

# data IS AN OBJECT (the weather patterns) CREATED ABOVE
# TIMES IS ALSO CREATED ABOVE, AND REPRESENTS TIME
data.index = TIMES
dni = data.DNI.values
ghi = data.GHI.values
dhi = data.DHI.values
surface_albedo = data['Surface Albedo'].values
temp_air = data.Temperature.values
dni_extra = pvlib.irradiance.get_extra_radiation(TIMES).values

# GET SOLAR POSITION
sp = pvlib.solarposition.get_solarposition(TIMES, LATITUDE, LONGITUDE)
solar_zenith = sp.apparent_zenith.values
solar_azimuth = sp.azimuth.values


# CREATING THE ARRY TO STORE THE DAILY ENERGY OUTPUT BY SOLAR AZIMUTH
e_by_az = []


# IDEAL surface_tilt ANGLE IN NORTHERN HEMISPHERE IS ~25
surface_tilt = 25

# ITERATING THROUGH DIFFERENT SURFACE_AZIMUTH VALUES
for heading in heading_DF["Heading"]:
    
 
    surface_azimuth = heading

    poa_sky_diffuse = pvlib.irradiance.get_sky_diffuse(
        surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
        dni, ghi, dhi, dni_extra=dni_extra, model='haydavies')

    # calculate the angle of incidence using the surface_azimuth and (hardcoded) surface_tilt
    aoi = pvlib.irradiance.aoi(
        surface_tilt, surface_azimuth, solar_zenith, solar_azimuth)
    # https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.irradiance.aoi.html
    # https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.PVSystem.html

    poa_ground_diffuse = pvlib.irradiance.get_ground_diffuse(
        surface_tilt, ghi, albedo=surface_albedo)
    poa = pvlib.irradiance.poa_components(
        aoi, dni, poa_sky_diffuse, poa_ground_diffuse)
    poa_direct = poa['poa_direct']
    poa_diffuse = poa['poa_diffuse']
    poa_global = poa['poa_global']
    iam = pvlib.iam.ashrae(aoi)
    effective_irradiance = poa_direct*iam + poa_diffuse
    temp_cell = pvlib.temperature.pvsyst_cell(poa_global, temp_air)

    # THIS IS THE MAGIC
    cecparams = pvlib.pvsystem.calcparams_cec(
        effective_irradiance, temp_cell,
        CECMOD_MONO.alpha_sc, CECMOD_MONO.a_ref,
        CECMOD_MONO.I_L_ref, CECMOD_MONO.I_o_ref,
        CECMOD_MONO.R_sh_ref, CECMOD_MONO.R_s, CECMOD_MONO.Adjust)
    # mpp is the list of energy output by hour for the whole year using a single panel
    mpp = pvlib.pvsystem.max_power_point(*cecparams, method='newton')
    mpp = pd.DataFrame(mpp, index=TIMES)
    first48 = mpp[:48]
    Edaily = mpp.p_mp.resample('D').sum()
    # Edaily is the list of energy output by day for the whole year using a single panel

    Eyearly = sum(Edaily)
    
    e_by_az.append(Eyearly)


## LINKING THE Heading (ie. surface_azimuth) AND THE Eyearly (ie. yearly energy output) IN A DF
heading_DF["Eyearly"] = e_by_az
heading_DF.head()


## VISUALIZE ENERGY OUTPUT BY SURFACE_AZIMUTH
plt.plot(heading_DF["Heading"], heading_DF["Eyearly"])
plt.xlabel("Surface_Azimuth Angle")
plt.ylabel("Yearly Energy Output with tilt @ " + str(surface_tilt))
plt.title("Yearly Energy Output by Solar_Azimuth Angle using surface_tilt = " + str(surface_tilt) + " in Berkeley, CA");

# FIND SURFACE_AZIMUTH THAT YIELDS THE MAX ENERGY OUTPUT
heading_DF[heading_DF["Eyearly"] == max(heading_DF["Eyearly"])]

# FIND SURFACE_AZIMUTH THAT YIELDS THE MIN ENERGY OUTPUT
heading_DF[heading_DF["Eyearly"] == min(heading_DF["Eyearly"])]

Answer 1

多亏了kevinanderso@gmail.com感谢你在pvlib python Google小组中帮助我。他指出，my“TIMES变量不知道时区，因此太阳位置计算假定UTC”

为了解决这个问题，他建议我使用tz='Etc/GMT+8'初始化时间（即美国的PST）

用他的话来说，“最初发布的代码[I]是对一个假设系统进行建模，在这个系统中，太阳位置和辐照度相互之间是时移的。这与现实大相径庭，因此现实生活中的预期不适用于你的模型”

感谢凯文，希望这能帮助其他有类似问题的人