平行排列倾斜表面上的全球太阳辐照度预测,包括直接和各向异性漫射遮阳的影响

Energies Pub Date : 2024-07-12 DOI:10.3390/en17143444
S. Pereira, P. Canhoto, Rui Salgado
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引用次数: 0

摘要

太阳能光伏发电厂通常由一排排太阳能电池板组成,对倾斜表面的太阳辐照度的准确估算对发电量有很大影响。现有的做法通常只考虑第一排,而忽略了后面几排的遮挡情况。在这项工作中,根据实验数据对 10 个换位模型进行了评估,并开发和验证了内排的换位模型。所开发的模型纳入了视角因子以及相邻行遮挡的直接辐照度和环太阳辐照度,大大提高了全球倾斜辐照度(GTI)的估算结果。根据 2022 年 4 月 14 日至 6 月 1 日在葡萄牙埃武拉(38.5306,-8.0112)记录的一分钟观测数据对该模型进行了验证,得出的平均偏差误差(MBE)和均方根误差(RMSE)值分别为-12.9 瓦/平方米和 76.8 瓦/平方米,与第一行表现最佳的转置模型相比,全球倾斜辐照度(GTI)估算的平均偏差误差(MBE)提高了 368.3 瓦/平方米。此外,还在业务预报环境中对拟议模型进行了评估,将直接辐照度和漫射辐照度的校正预报(提前 0 到 72 小时)作为输入,结果 MBE 和 RMSE 分别为 -33.6 W/m2 和 169.7 W/m2。这些发现凸显了所开发模型在提高太阳能预测准确性以及运行算法的效率和稳健性方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Global Solar Irradiance on Parallel Rows of Tilted Surfaces Including the Effect of Direct and Anisotropic Diffuse Shading
Solar photovoltaic power plants typically consist of rows of solar panels, where the accurate estimation of solar irradiance on inclined surfaces significantly impacts energy generation. Existing practices often only account for the first row, neglecting shading from subsequent rows. In this work, ten transposition models were assessed against experimental data and a transposition model for inner rows was developed and validated. The developed model incorporates view factors and direct and circumsolar irradiances shading from adjacent rows, significantly improving global tilted irradiance (GTI) estimates. This model was validated against one-minute observations recorded between 14 April and 1 June 2022, at Évora, Portugal (38.5306, −8.0112) resulting in values of mean bias error (MBE) and root-mean-squared error (RMSE) of −12.9 W/m2 and 76.8 W/m2, respectively, which represent an improvement of 368.3 W/m2 in the MBE of GTI estimations compared to the best-performing transposition model for the first row. The proposed model was also evaluated in an operational forecast setting where corrected forecasts of direct and diffuse irradiance (0 to 72 h ahead) were used as inputs, resulting in an MBE and RMSE of −33.6 W/m2 and 169.7 W/m2, respectively. These findings underscore the potential of the developed model to enhance solar energy forecasting accuracy and operational algorithms’ efficiency and robustness.
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