Clean Technologies and Environmental Policy

, Volume 21, Issue 8, pp 1645–1654 | Cite as

Cleaning cycle optimization and cost evaluation of module dust for photovoltaic power plants in China

  • Bo ZhaoEmail author
  • Shuwei Zhang
  • Shengxian Cao
  • Qi ZhaoEmail author
Original Paper


The photovoltaic (PV) power plants installed in the northwest and northeast areas of China have a serious dust pollution problem. In this paper, a model for optimizing the cleaning cycle of module dust and evaluating the cost for the PV power plants in China was proposed by the use of dust deposition monitoring with image recognition and two cleaning technologies. Outdoor experimental results showed that the degradation of power conversion efficiency changed linearly with increasing the image gray value and the dust deposition density had an asymptotic relationship with dust deposition time. Based on the proposed model and corresponding dry and wet cleaning technologies, the optimal cleaning cycles for a PV power plant in northeast China were approximately 10.1 and 22.8 days when the power conversion efficiency was reduced by 4.5% and 10.2%, respectively. The annual cost resulting from dust on the PV power modules in China was estimated to be $0.0161–0.0222 million per MW with current fixed cleaning cycle and wet cleaning technology. However, the annual cost could be reduced to 36.5–50.3% by using the optimized cleaning cycle and applying dry cleaning technology.

Graphic abstract


PV modules Dust deposition Power conversion efficiency Cleaning cycle Image recognition Cost evaluation 

List of symbols


Grid-connected price ($/kWh)


Annual total cost caused by dust ($)


Cleaning and maintenance cost in a single cleaning cycle ($)


Power loss cost in a single cleaning cycle ($)


Residual dust cost in a single cleaning cycle ($)


Image gray value of a point


Average image gray value of dirty PV modules


Installed capacity of PV power plant (kW)


Maximum power output (Wp)


Average temperature of PV module array (°C)


Normal operating cell temperature (°C)


Fuel consumption cost ($/MW)


Staff cost per unit capacity ($/MW)


Annual maintenance cost of the cleaning equipment ($)


Annual depreciation cost of the cleaning equipment ($)


Daily power loss cost ($/day)


Daily residual dust cost ($/day)


Power temperature coefficient (%/°C)


Cleaning frequency (time)


Useful life of the cleaning equipment (year)


Dust deposition time (day)


Cleaning interval (day)


Cleaning time (day)


Original value of the cleaning equipment ($)


Power conversion efficiency (%)


Power conversion efficiency of PV modules in clean condition (%)


Power conversion efficiency of PV modules in dust condition (%)


Estimated net residual rate of the cleaning equipment (%)


Maintenance rate of the cleaning equipment (%)


Loss ratio of power output (%)


Annual operation period (day)



This study was supported by the National Natural Science Foundation of China (51606035) and the Science and Technology Development Plan of Jilin Province (20190302079GX and 20190201098JC). The authors gratefully acknowledge the contributions of G. Wang, X. M. Gao, R. M. Bao, and J. Wu for their work on the field test of MDCA.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Automation EngineeringNortheast Electric Power UniversityJilinChina
  2. 2.School of Science and EngineeringUniversity of DundeeDundeeUK

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