Global analysis of optimal cleaning cycle and profit of soiling affected solar panels

被引:35
作者
Mithhu, Md. Mahamudul Hasan [1 ]
Rima, Tahmina Ahmed [1 ]
Khan, M. Ryyan [1 ]
机构
[1] East West Univ, Dept Elect & Elect Engn, Dhaka, Bangladesh
关键词
Soiling on solar panels; Cleaning cycle; Revenue; Profit; Global soiling rate; DUST DEPOSITION; ENERGY YIELD; PERFORMANCE; MODULES; DESIGN;
D O I
10.1016/j.apenergy.2021.116436
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
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页数:10
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