Changes in Cadmium Telluride Photovoltaic System Performance due to Spectrum

Changes in Cadmium Telluride Photovoltaic System Performance due to Spectrum
复制标题

光谱引起的碲化镉光伏系统性能变化

DOI:
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发表时间:
2012
影响因子:
3
通讯作者:
A. Panchula
A. Panchula
中科院分区:
工程技术3区
文献类型:
--
作者:
L. Nelson;Mark Frichtl;A. Panchula

文献摘要

被引文献

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季节性和短期天气相关的太阳光谱的变化可能会导致光伏(PV)系统的性能变化,影响年度能源预测和系统特性。光谱偏移因子,这是一个度量指标,表明光伏系统的性能将有多大变化,由于偏离ASTM G173光谱(空气质量为1.5),预测使用TMY3数据和简单的模型的大气辐射传输阳光(SMARTS)模型,并与碲化镉(CdTe)光伏系统性能在四个不同的气候。CdTe系统的预测光谱偏移因子显示在夏末和初秋的性能提高,在冬季的性能下降。这些年内变化可以高达± 3%,但年光谱移动因子通常在± 1%的范围内。碲化镉系统的光谱位移因子被认为是最敏感的大气中的可降水量。因此,碲化镉光谱偏移因子作为指数函数的可降水量的参数化,来自使用的SMARTS模式在11个位置的输出。这种参数化预测观察到的每月和每日的CdTe光伏性能波动。未来的努力将把这种方法纳入能源预测,以减少不确定性。
Seasonal and short-term weather-related changes in the solar spectrum can induce shifts in the performance of photovoltaic (PV) systems that affect both annual energy predictions and system characterization. The spectral shift factor, which is a metric indicative of how much the performance of a PV system will vary from nameplate due to deviations from the ASTM G173 spectrum (air mass of 1.5), is predicted using TMY3 data and the simple model of the atmospheric radiative transfer for sunshine (SMARTS) model and is correlated with cadmium telluride (CdTe) PV system performance in four different climates. The predicted spectral shift factors for CdTe systems show improved performance in the late summer and early fall and diminished performance in the winter. These intraannual variations can be as large as ±3%, but annual spectral shift factors are typically within ± 1% of nameplate. The spectral shift factor of CdTe systems was found to be most sensitive to the precipitable water content of the atmosphere. Consequently, a parameterization of CdTe spectral shift factor as an exponential function of precipitable water is derived using the outputs of the SMARTS model in 11 locations. This parameterization is shown to predict observed monthly and daily fluctuations in CdTe PV performance. Future efforts will incorporate this methodology into energy predictions that will reduce uncertainty.