改进的独立于参考条件的方法,用于评估不同运行条件下光伏组件的输出性能

IF 1.9 4区 工程技术 Q4 ENERGY & FUELS Journal of Renewable and Sustainable Energy Pub Date : 2024-03-01 DOI:10.1063/5.0195075
Guorong Li, Yunpeng Zhang, Jiao Ma, Hai Zhou, Ji Wu, Shumin Sun, Daning You, Yuanpeng Zhang
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引用次数: 0

摘要

估算光伏组件输出特性的传统方法深受参考条件和转换方程选择的影响,而参考条件和转换方程决定了实际运行条件下的计算物理参数。在不同工作条件下,光伏电池的载流子传输特性存在差异,如结边少数载流子的数量和速度及其重组速度,这导致输出特性的估算存在较大偏差,尤其是在低辐照度条件下。为了提高性能估计的准确性,我们提出了一种独立于参考条件的改进方法。这种方法消除了参考条件的影响,并改进了所有辐照度水平下的转换方程。根据物理参数对辐照度和温度的依赖关系,建立了单二极管模型在不同辐照度区间的转换方程。特别是在低辐照度范围内,改进的转换方程中的每个物理参数都考虑了辐照度和温度的所有影响。为了优化转换方程中的未知参数,采用了人工蜂鸟算法来拟合实验 I-V 数据。六种不同类型的光伏组件在各种工作条件下的实验结果验证了所提方法的有效性。所提出的方法具有立竿见影的效果,包括不受参考条件的影响,在估算光伏输出属性时,物理参数和环境因素之间的关系更加精确。将结果与 Laudani 的传统方法进行比较,发现拟议方法在估算 I-V 特性方面能力出众,并能在各种工作条件下准确识别最大功率点,对工程应用具有重要价值。
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Improved reference condition independent method for output performance estimation of PV modules under varying operating conditions
Traditional methods for estimating output property of the photovoltaic (PV) modules are strongly influenced by the selection of reference condition and transforming equations, which determine the calculated physical parameters under real operating conditions. The differences in the carrier transport properties of PV cells under varying operating conditions, such as the number and velocity of minority carriers at the junction edge and their recombination speed, lead to large deviations in the estimation of the output characteristics, especially under low irradiance conditions. To enhance the accuracy of performance estimation, we propose an improved method that is independent of reference condition. This method eliminates the impact of reference conditions and improves the transformation equations under all irradiance levels. Transformation equations of single diode model are established in different irradiance intervals based on the dependence of physical parameter on irradiance and temperature. Especially in the low irradiance range, all effects of irradiance and temperature are considered for each physical parameter in improved transformation equations. To optimize the unknown parameters in the transformation equations, the artificial hummingbird algorithm is used to fit experimental I–V data. The experimental results of six different types PV modules under a wide range of operating conditions are used to verify the effectiveness of the proposed method. The proposed method offers immediate benefits, including independence from reference condition and a more precise relationship between physical parameters and environmental factors in the estimation of PV output properties. Comparing the results to the traditional method by Laudani, the proposed method demonstrates superior capability in estimating I–V characteristics and accurately identifies the maximum power point under various operating conditions, which is of significant value for engineering applications.
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来源期刊
Journal of Renewable and Sustainable Energy
Journal of Renewable and Sustainable Energy ENERGY & FUELS-ENERGY & FUELS
CiteScore
4.30
自引率
12.00%
发文量
122
审稿时长
4.2 months
期刊介绍: The Journal of Renewable and Sustainable Energy (JRSE) is an interdisciplinary, peer-reviewed journal covering all areas of renewable and sustainable energy relevant to the physical science and engineering communities. The interdisciplinary approach of the publication ensures that the editors draw from researchers worldwide in a diverse range of fields. Topics covered include: Renewable energy economics and policy Renewable energy resource assessment Solar energy: photovoltaics, solar thermal energy, solar energy for fuels Wind energy: wind farms, rotors and blades, on- and offshore wind conditions, aerodynamics, fluid dynamics Bioenergy: biofuels, biomass conversion, artificial photosynthesis Distributed energy generation: rooftop PV, distributed fuel cells, distributed wind, micro-hydrogen power generation Power distribution & systems modeling: power electronics and controls, smart grid Energy efficient buildings: smart windows, PV, wind, power management Energy conversion: flexoelectric, piezoelectric, thermoelectric, other technologies Energy storage: batteries, supercapacitors, hydrogen storage, other fuels Fuel cells: proton exchange membrane cells, solid oxide cells, hybrid fuel cells, other Marine and hydroelectric energy: dams, tides, waves, other Transportation: alternative vehicle technologies, plug-in technologies, other Geothermal energy
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