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Presentation Details
| Sensitivity of models for snow-induced energy losses in fixed-tilt PV systems due to different ground snow depth data sources (yes) Shelbie Wickett, Ayush Chutani, Isobel Bowker, Ana Dyreson. Michigan Technological University, Houghton, MI, USA |
Abstract
With the increase of utility-scale PV installations in snowy climates, modeling generation losses from snow cover blocking light to PV panels (snow loss) has become an area of interest for grid planners and PV developers. PV snow loss estimations require site-specific snow depth data, but data availability is limited, especially across wide geographic areas. However, accessible ground snow depth gridded datasets have been developed for hydrological research using satellite data, numerical weather modeling, and in-situ measurements. Our research investigates the sensitivity of simulated snow-induced PV losses to two gridded snow depth datasets using industry-standard PV system simulation software. We compare simulated snow losses from gridded and site-measured snow depth inputs, investigating the impacts of snow depth dataset choice when modeling a 35-degree fixed-tilt PV system at a snowy site in Michigan, USA. During March and April 2023, the two gridded datasets resulted in different snow loss estimates. When compared to the site-measured simulation, one dataset overestimated total snow loss by 35.1% and the other underestimated by 19.2%. We extended one of the gridded snow depth simulations and the site-measured simulation to longer analysis periods in 2024 and 2025 (7 snow months each year). The gridded snow depth simulation predicted snow-induced energy loss within 10 to 24% of the site-measured simulation energy loss. The annual DC generation produced by the gridded snow depth simulation was only about 1% different than the annual DC generation from the site-measured simulation in 2024 and 2025. Overall, these results highlight differences in snow loss timing and magnitude depending on the snow depth dataset used, presenting a need for local snow depth measurements when daily to hourly losses are required, but also suggesting gridded snow depth datasets may be adequate for annual snow-induced generation losses.
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No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.