ReNew has identified module cleaning as one of the major operational activities at its solar PV plants. The conventional time-based cleaning approach was inefficient and led to unnecessary water consumption. ReNew aimed to develop a model to clean panels based on dirt deposition conditions to achieve greater reliability, sustainability, and optimal performance of the plant.
To achieve this objective, ReNew started collecting minute-level data on radiation, environmental conditions, and DC/AC output, which was fed into a proprietary model developed by ReNew’s data science team for soiling loss calculation. The model was created to get the data fed through fully automated data pipelines daily without any manual intervention. The model calculated the soiling loss each day and triggered cleaning as soon as the soiling losses exceeded the cleaning cost. The model output was published daily on a dashboard available to the site teams. The status was checked, and cleaning began only on the flagged inverters, thereby taking timely corrective action on soiled modules and optimizing resource utilization.
On switching to condition-based cleaning, the panels were cleaned at the right time and at an optimal cost. This cleaning method created an optimum cleaning schedule based on the generation loss of modules while considering cleaning costs. The use case led to an increase in the yield of plants from 0.5% to 0.8%, in addition to the water savings, without incurring any additional capex.
This case study highlights the significance of using advanced digital analytical methods to optimize operational activities in renewable energy companies. The use of machine learning algorithms helped ReNew achieve greater reliability, sustainability, and optimal performance of the plant, thereby leading to a significant increase in yield and water savings.
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