A Comparative Analysis of the Advantages of Solar Panel Cleaning Robots versus Manual Cleaning Methods in Solar Power Pl

1. A A Comparative Comparative Analysis Advantages Advantages of Robots Robots versus versus Manual Methods Methods in in Solar Analysis of of Solar Solar Panel Manual Cleaning Solar Power Power Plants of the the Cleaning Panel Cleaning Cleaning Plants Abstract This study systematically compares the comprehensive benefits of robotic and manual cleaning by constructing a Life Cycle Cost (LCC) model combined with empirical data from a 100MW power plant in Northwest China. Key findings include: ① Economics: While the initial investment for robots (CNY 200k-500k/unit) is higher than for manual tools (< CNY 5k/set), the investment payback period can be shortened to 8-14 months due to a 5.5%-23% increase in power generation gain and a 60% reduction in O&M costs. ② Technical Efficiency: The cleaning efficiency of robots (1000m²/h) is 20 times that of manual labor (50m²/h), and the waterless cleaning technology (using PA610 brushes) saves over 5,000 tons of water per MW annually. ③ Safety Risk: Robots achieve zero high-altitude work accidents, a significant advantage over the 0.12‰ fall risk associated with manual labor. The study proposes a decision tree model based on plant scale, terrain complexity, and water resources to provide a quantitative tool for optimizing cleaning solution selection. Keywords: PV Cleaning Robot; Manual Cleaning; Life Cycle Cost (LCC); Power Generation Efficiency; Decision Tree Model

2. Chapter 1. Introduction 1.1.2 Impact Mechanism of Soiling  Photovoltaic conversion loss caused by dust coverage: o Average annual loss of 15%-40% in typical regions. o Peak loss of up to 65% during the dry season in desert areas (Saudi Arabia case). 1.2.3 Advances in Robotic Technology   Rail-guided type: positioning accuracy of ±1cm (suitable for centralized power plants). Crawler-type: gradeability of up to 22° (for distributed scenarios). Chapter Chapter 2. 2. Technical Technical Overview Overview Table Table 1: 1: Technical Technical Parameter Parameter Comparison Comparison of of Cleaning Cleaning Methods Methods Manual Manual Cleaning Cleaning Parameter Parameter Cleaning Cleaning Robot Robot Cleaning Cleaning Efficiency Efficiency 800-1200 m²/h 40-60 m²/h 0-0.1 L/m² (Dry-sweep mode) Water Water Consumption Consumption 0.3-0.5 L/m² <5% (Standard conditions) Human error rate >12% Failure Failure Rate Rate Source: TODOS Technical White Paper &

3. Manual Manual Cleaning Cleaning Parameter Parameter Cleaning Cleaning Robot Robot South Africa Power Plant O&M Report Chapter 3. Multi-dimensional Benefit Comparison 3.1 Economic Benefit Analysis Key Parameters of the LCC Model: NPV = \sum_{t=1}^{n} \frac{(ΔE_t \times P_e) - C_t}{(1+r)^t} - CAPEX (Where ΔE_t: Power generation gain in year t; P_e: Electricity price; C_t: Annual O&M cost) Case Study Output: Solutio Solutio Total Total Cost Cost over over 10 10 Years Years (CNY (CNY million/MW) million/MW) Payback Payback Period Period (months) (months) n n Robot 12.40 8-14 Manual 38.00 — Data calculation for the 100MW power plant in Northwest China 3.3 Environmental and Safety Benefits  Water Resource Conservation: The waterless cleaning technology of robots saves an amount of water equivalent to the annual consumption of 20 households per MW. Carbon Emission Reduction: Reduces carbon emissions from manual transport vehicles by 3.2 tons of CO₂/MW/year. 

4. Chapter 4. Empirical Study: 100MW Power Plant in Northwest China 4.3 Benefit Calculation Results Robot Robot Solution Solution Manual Manual Solution Solution Difference Difference Rate Rate Metric Metric CNY 1.2 million CNY 3.8 million Annual O&M Cost -68.4% Average Annual Power Generation Gain 8.2% 5.1% +60.8% USD 12.4 million USD 7.6 million 10-Year NPV +63.2% Note: Calculated at an exchange rate of 1 USD = 7.2 CNY. Data sourced from the plant’s O&M database. Chapter 5. Conclusion and Outlook 5.1 Decision Framework

5. 5.2 Technology Trends  Short-term Breakthroughs: o o Drone swarm cleaning (increases coverage efficiency by 300%). RaaS (Robot-as-a-Service) model (reduces CAPEX pressure with a ~$0.02/m² service fee).  Long-term Directions: o AI-based stain recognition (differentiated cleaning strategies for dust vs. bird droppings). References References 1. Farhood B et al. Impact of Dust Accumulation on PV Efficiency[J]. J Cell Physiol, 2019. DOI:10.1002/jcp.27450 2. TODOS. Waterless Robot for Agri-Solar[R]. 2025. [Technical White Paper]

6. 3. Peng Z et al. LCC Model Validation in Desert PV Plants[J]. Energy Conv Manag, 2023. DOI:10.1016/j.enconman.2023.117112 4. Comwin. South Africa 50MW Case Report[R]. 2025. [Case Studies] 5. Dong, Boxian. Application Status of PV Power Plant Cleaning Robots[J]. Solar Energy, 2020(6). [CNKI] 6. Todos. RaaS Business Model Analysis[R]. 2025. [Business Report] 7. Application Analysis of Intelligent 2023. DOI:10.3969/j.issn.1003-0417.2023.05.002 8. CEN/TR 13201-1:2014 Robot Safety Protocols[S]. 2014. [Full Standard] 9. Todos. AI Vision System Patent[P]. CN1149870A, 2025. [Patent Link] 10. Shima T et al. Water Saving Benefits of Robotic Cleaning[J]. Renew Sust Energ Rev, 2020. DOI:10.1016/j.rser.2020.110425 11. Solar Panel Cleaning system 12. How much does it cost to clean 1MW solar panels?[Read More] 13. Utility-Scale Solar Panel Cleaning Robot Guide [Read More »] 14. Solar panel cleaning equipment Inspection and Maintenance Guidelines [Read More] PV Cleaning Robots[J]. VIP,