Abstract

Soiling of photovoltaics (PV) panels is affected by various factors such as relative humidity, dust concentration, and panel tilt angle. The soiling can lead to significant losses in electricity production, especially in a place like Dubai, UAE. Soiling can also lead to long-term damage of the PV panels such as degradation and delamination due to the hot spots caused by dirt deposition. It is important to choose the right cleaning strategy (method and frequency) to maximize the electricity production and economic performance of the PV facility. An optimization algorithm was developed and tested for multiple PV panel configurations based in Dubai Water and Electricity Authority’s (DEWA) outdoor test facility (OTF) solar lab. The algorithm’s input included electricity production, soiling rates (SRs), electricity price, and cleaning costs. The output included number of cleaning events and the extra revenue as compared with the current practice of periodic (5-day cycle) manual cleaning. Four different cleaning scenarios were tested and compared with the current scenario. Three scenarios resulted in improved net cost benefit (NCB), up to 34% for the case of performance-based manual cleaning. The fourth scenario resulted in diminished NCB, down by 245% for the case of daily automatic cleaning. Other findings of the study included higher tilt angles that resulted in lower cleaning requirements and thin-film PV panels that required less cleaning than first generation PV panels (mono/polycrystalline). The algorithm is an effective yet simple tool to help operators optimize the NCB of their PV facilities.

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