Dust mitigation methods and multi-criteria decision-making cleaning strategies for photovoltaic systems: Advances, challenges, and future directions
Mahdi Gandomzadeh, Ali Yaghoubi, Arman Hoorsun, Alireza Parsay, Aslan Gholami, Majid Zandi, Roghayeh Gavagsaz‐Ghoachani, Hussein A. Kazem
Abstract
This review consolidates four decades of research (1983–2024) on dust mitigation for photovoltaic systems, categorizing strategies into four key areas: preventive measures, dust monitoring systems, active cleaning methods, and multi-criteria decision-making strategies for cleaning schedules. A systematic content analysis was employed to critically evaluate methodologies, findings, and emerging trends. Preventive measures, notably super-hydrophobic coatings, can reduce dust accumulation by up to 50 %. However, integrating monitoring systems remains complex. The review highlights the potential of hybrid cleaning methods combining manual, mechanical, electromechanical, and electrostatic approaches to balance their benefits and limitations for higher efficiency and practicality. It also emphasizes a shift from fixed-interval cleaning schedules to dynamic, AI-driven decisions based on real-time data, potentially reducing the levelized cost of energy by 8 % for large-scale photovoltaic plants and 4.3 % for smaller systems. Unmanned aerial vehicle-based cleaning methods are recognized as a promising future solution for large-scale photovoltaic systems. The review identifies critical research gaps and provides recommendations for advancing dust mitigation technologies and optimizing photovoltaic cleaning and maintenance strategies to minimize soiling effects. This comprehensive analysis aims to guide future research toward more sustainable and economically viable soil detection and mitigation systems as well as photovoltaic system management. • Classifying mitigation actions in preventive, active cleaning, and decision-making. • All-embracing assessment of manual, mechanical, vibratory & electrostatic Cleaning. • Introducing hybrid approaches combining cleaning, monitoring, and optimization. • Proposing artificial intelligent multi-criteria decision-making cleaning strategies. • Introducing Unmanned Aerial Vehicle-based monitoring/cleaning hybrid solution.