Solar PV Panel soiling commonly known as soiling losses impact clean energy production of solar power PV plant. Read to know what are soiling losses and how you can reduce solar O&M costs and track cleaning effectiveness of any kind of solar PV plant using AI-based Dynamic Cleaning Schedule.
One of the major roadblocks in solar PV plant’s performance is “Panel Soiling” caused due environmental and natural factors. Predominantly, deposition of dust, bird droppings, industrial dust, dust storm, snow etc. Such soiling causes reflection, absorption and partial scattering of irradiance which leads to reduced amount of solar energy that reaches the solar PV cell.
What are the effects of soiling on solar PV plant?
- Reduces the Energy Yield/Performance Ratio (PR).
- Increase in solar O&M cost.
- Induces more uncertainty in the solar energy production forecast.
- Decrease in the share of renewable energy in the market.
The estimated annual losses due to soiling ranges between ~2% and ~6% depending on the geographical location of the PV system. Therefore, an adequate methodology to mitigate soiling losses has become the need of the hour. Unlike other PV system losses, soiling is reversible i.e soiling can be mechanically removed from the surfaces of the solar panels either through natural means such as rain, wind or artificial means such as manual cleaning, semi-automated or fully automated robotic cleaning.
Robotic cleaning and manual wet cleaning of solar PV panels.
The most common anti-soiling technique adopted is the manual wet-cleaning of the entire plant as per fixed schedule. Globally, there has been a visible shift towards robotic cleaning adoption. However, the wide-area spread of the solar plants creates operational challenges for the solar asset managers. In most cases, the soil disposition across the plant is non-uniform and dynamic in nature. Moreover, meteorological events (like rainfall, snow, dust storm, etc.) play a vital role in the overall deposition of soil; hence, these factors should also be considered in the cleaning schedule. A fixed cleaning schedule that normally does not cover these factors, leads to inefficient cleaning and eventually leads to significant loss of energy production and resources (time, water, and labor).
Smarthelio’s Dynamic Cleaning Scheduler
Smarthelio’s Dynamic Cleaning Scheduler takes into consideration soiling trends, meteorological events, cleaning cost and electricity cost and follows an Al-based optimization process to generate a dynamic cleaning schedule and track the cleaning effectiveness for different sections of the plant.
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