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我正在使用开源 pvlib 软件（和 CEC 模块）构建一个模型来估计每年的光伏发电量。我在模型中遇到了一些不一致的地方，如果社区可以提供任何故障排除，我将不胜感激。

我的主要问题是：该模型告诉我，用于能源生产的理想北半球表面方位角（即具有最高能量输出的表面方位角）约为 76°（就在正东以北），而用于能源生产的最差表面方位角约为270°（正西）。但是，我知道北半球理想的表面方位角应该是大约 180°（正南），最差的表面方位角在 0°（正北）。

我已经包含了这个图表，以帮助可视化基于 surface_azimuth 的能量产生的变化。 这也是在附加代码的末尾生成的。

谁能帮我纠正这个问题或纠正我的理解？

复制下面的代码以供参考

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"])]
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1 回答 1

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感谢 kevinanderso@gmail.com 在 pvlib-python Google Group 中帮助我。他指出我的“TIMES 变量不支持时区，因此太阳位置计算假设为 UTC”。

为了解决这个问题，他建议我用 tz='Etc/GMT+8' 初始化 TIMES（即美国的 PST）。

用他的话说，“最初发布的代码 [I] 是对一个假设系统进行建模，其中太阳位置和辐照度相对于彼此发生时移。这与现实有很大的不同，因此现实生活中的期望不适用于您的模型”。

感谢凯文，希望这可以帮助其他有类似问题的人。

于 2020-12-23T22:37:36.990 回答