我正在尝试使用PVLIB估算安装在我国西部地区的PV系统的输出功率。
作为一个例子，通过MERRA2重新分析，我得到了2天的每小时GHI，2m的温度和10m的风速。
我想使用上述数据集和PVLIB的ModelChain函数来估算固定PV系统或1轴跟踪系统将产生多少功率。我首先使用DISC模型从GHI数据估计DNI和DHI，以获得DNI，然后DHI是GHI和DNI * cos（Z）之差
a）第一个行为我不确定是否可以。这是GHI，DNI，DHI，T2m和风速的图。似乎DNI的最大值发生在GHI最大值之前1小时。
准备辐照度数据后，我使用模型链计算了AC，并指定了固定PV系统和1轴单跟踪系统。 事实是，我不相信单轴系统的交流输出。我预计交流输出将达到平稳状态，并且发现一种奇怪的行为。
这是我期望看到的发电量的最大输出值：
这是PVLIB的估计输出
我希望有人能帮助我在我的程序中找到错误。
这是代码：
# =============================================================================
# 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)
我希望任何人都可以帮助我解释结果并调试代码。
非常感谢！
答案 0 :(得分：2)
我怀疑您的问题是由于处理每小时数据的方式引起的。确保您与时间间隔标签（开始/结束）以及瞬时数据与平均数据的处理保持一致。一种可能的原因是使用每小时平均GHI数据得出DNI数据。 pvlib.solarposition.get_solarposition返回传递给它的时间瞬间的太阳位置。因此，当您使用pvlib.irradiance.disc计算DNI以及计算DHI时，您会将每小时平均GHI值与瞬时太阳位置值混合在一起。将时间索引偏移30分钟会减少但不会消除错误。另一种方法是将输入数据重新采样为1-5分钟的分辨率。