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Global analysis of optimal cleaning cycle and profit of soiling affected solar panels
Applied Energy ( IF 11.2 ) Pub Date : 2021-01-12 , DOI: 10.1016/j.apenergy.2021.116436
Md. Mahamudul Hasan Mithhu , Tahmina Ahmed Rima , M. Ryyan Khan

The photovoltaics (PV) industry is poised to capture most of the energy sector within the next few decades. As the installed PV capacity increases, even the smallest improvements on the system and operations of the solar farms can accumulate to a significant gain in revenue. One such scope is efficient mitigation of dust accumulation on panels or soiling. While installed capacities are two of the highest in Asia and around US deserts, these locations are also dust prone. The Middle East and North Africa (MENA) regions having good insolation are also highly potential candidates for PV farms with the added complexity of soiling losses. Therefore, while soiling may not be an issue in many locations over the globe, it is in fact relevant to the majority of the PV installation sites. The analysis of the effects of soiling losses on energy yield and economics are of great importance for these locations. In this work, we have extended the empirical soiling model found in the literature to include the effects of temporal variation on soiling and insolation. Our study on variation in revenue with unoptimized cleaning intervals estimates the soiling loss, which can particularly interest PV farms with accessibility issues such as agrophotovoltaic systems. We analyze the optimal cleaning cycle and corresponding normalized revenue (cash inflow normalized to the rated clean farm revenue). A numerical model is used to explain the effects under seasonal and sudden (e.g., sand storm or rain) variations in soiling and insolation. Our closed-form analytical expressions can predict the cleaning cycle and normalized revenue within 0.1% of the numerical results by using the soiling and insolation data averaged over the seasons. Finally, we discuss these results in a global scenario using our estimated world-map for soiling rates. This predicts the worldwide revenue loss under location-specific optimal cleaning cycles, assuming one of the lowest cleaning costs seen globally. In Asia and MENA regions, for example, median revenue loss due to soiling is 2.5% even after optimal cleaning every 5–6 days. This loss is 1.5% in the US for 12 days of cleaning interval. Considering region-specific cleaning costs, the revenue loss of optimally cleaned PV farm is 2%–5% for Asia, MENA, North America, and Europe.



中文翻译:

最佳清洁周期和污染太阳能电池板收益的全局分析

光伏(PV)行业有望在未来几十年内占领大部分能源领域。随着已安装的光伏容量的增加,即使是对太阳能发电场的系统和操作进行最小的改进,也可以累积大量收益。一种这样的范围是有效减轻面板上的灰尘积聚或弄脏。尽管装机容量是亚洲和美国沙漠地区中装机容量最高的两个,但这些地区也容易产生灰尘。具有良好日照的中东和北非(MENA)地区也很可能成为光伏电站的候选者,而且污染损失更加复杂。因此,尽管污染在全球许多地方可能不是问题,但实际上与大多数光伏安装场所有关。对于这些地区,分析污损对能源产量和经济的影响非常重要。在这项工作中,我们扩展了文献中发现的经验污染模型,以包括时间变化对污染和日照的影响。我们对清洁间隔未优化的收益变化的研究估计了污损,这尤其会使光伏农场面临诸如农业光伏系统等可及性问题。我们分析了最佳清洁周期和相应的标准化收入(将现金流入标准化为额定清洁农场收入)。使用数值模型来解释在季节性和突变(例如沙尘暴或降雨)变化下土壤和日照的影响。我们的封闭式分析表达式可以预测清洗周期和归一化收益在0以内。通过使用各个季节的平均污染和日照数据,数值结果的1%。最后,我们使用估算的污染率世界地图在全球场景中讨论这些结果。假设全球范围内最低的清洁成本之一,这将根据特定地点的最佳清洁周期预测全球收入损失。例如,在亚洲和中东和北非地区,即使每5-6天进行了最佳清洁,由于污染造成的收入中位数损失仍为2.5%。在美国,每隔12天的清洁间隔损失为1.5%。考虑到特定地区的清洁成本,亚洲,中东和北非,北美和欧洲的最佳清洁光伏电站收益损失为2%–5%。我们使用估算的污染率世界地图在全球场景中讨论这些结果。假设全球范围内最低的清洁成本之一,这将根据特定地点的最佳清洁周期预测全球收入损失。例如,在亚洲和中东和北非地区,即使每5-6天进行了最佳清洁,由于污染造成的收入中位数损失仍为2.5%。在美国,此间隔为12天的清洁间隔为1.5%。考虑到特定地区的清洁成本,亚洲,中东和北非,北美和欧洲的最佳清洁光伏电站收益损失为2%–5%。我们使用估算的污染率世界地图在全球场景中讨论这些结果。假设全球范围内最低的清洁成本之一,这将根据特定地点的最佳清洁周期预测全球收入损失。例如,在亚洲和中东和北非地区,即使每5-6天进行了最佳清洁,由于污染造成的收入中位数损失仍为2.5%。在美国,每隔12天的清洁间隔损失为1.5%。考虑到特定地区的清洁成本,亚洲,中东和北非,北美和欧洲的最佳清洁光伏电站收益损失为2%–5%。在美国,每隔12天的清洁间隔损失为1.5%。考虑到特定地区的清洁成本,亚洲,中东和北非,北美和欧洲的最佳清洁光伏电站收益损失为2%–5%。在美国,每隔12天的清洁间隔损失为1.5%。考虑到特定地区的清洁成本,亚洲,中东和北非,北美和欧洲的最佳清洁光伏电站收益损失为2%–5%。

更新日期:2021-01-12
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