A Simple Approach for Clustering Common Insolation Profiles in Agrivoltaic Systems

Heterogeneous insolation distribution in agrivoltaic systems (AVS) impacts plant growth beneath solar panels via shading and perturbed evapotranspiration profiles. Most agricultural systems models, meanwhile, assume uniform irradiance patterns across an entire field when simulating biomass productio...

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Main Authors: Ganesh Pandey, Sarah Lyden, Evan Franklin, Matthew Tom Harrison
Format: Article
Language:English
Published: TIB Open Publishing 2025-04-01
Series:AgriVoltaics Conference Proceedings
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Online Access:https://www.tib-op.org/ojs/index.php/agripv/article/view/1370
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author Ganesh Pandey
Sarah Lyden
Evan Franklin
Matthew Tom Harrison
author_facet Ganesh Pandey
Sarah Lyden
Evan Franklin
Matthew Tom Harrison
author_sort Ganesh Pandey
collection DOAJ
description Heterogeneous insolation distribution in agrivoltaic systems (AVS) impacts plant growth beneath solar panels via shading and perturbed evapotranspiration profiles. Most agricultural systems models, meanwhile, assume uniform irradiance patterns across an entire field when simulating biomass production, meaning that they cannot readily account for spatiotemporal trade-offs between agricultural production and energy generation pertaining to AVS. We develop a simple approach for enumerating trade-offs between crop/pasture production and energy generation that accounts for spatial heterogeneity in insolation that typifies most AVS fields. First, long-term spatially explicit daily insolation profiles at the ground surface are produced for several layouts, including variations in PV panel orientations, row spacings, heights and tilt angles. A clustering technique was then applied to all insolation profiles to group them into rationally bounded cluster groups. The insolation profile of each cluster group was set as an input to a conventional point-based agricultural systems model to determine agricultural production under heterogeneous insolation profiles. The proposed approach is applied to a case study near Hobart, Australia, to determine an optimal layout that maximizes energy generation and plant growth associated with 81 AVS layouts. We find a manageable number (19 clusters) of point-based agricultural model scenarios capture much of the variance in insolation variability associated with varying AVS layouts. Compared with open fields, we show that AVS can amplify pasture growth rates during late spring and early summer. The optimal layout for our case study region enhanced land productivity by 47% while maintaining 80% of agricultural production compared with open-field agriculture.
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spelling doaj-art-43c1e83bac7345dcaa061c8bfa2c55df2025-08-20T02:13:22ZengTIB Open PublishingAgriVoltaics Conference Proceedings2751-61722025-04-01310.52825/agripv.v3i.1370A Simple Approach for Clustering Common Insolation Profiles in Agrivoltaic SystemsGanesh Pandey0https://orcid.org/0000-0002-9877-2732Sarah Lyden1https://orcid.org/0000-0002-5364-6011Evan Franklin 2https://orcid.org/0000-0002-8146-2770Matthew Tom Harrison3https://orcid.org/0000-0001-7425-452XUniversity of TasmaniaUniversity of TasmaniaUniversity of TasmaniaTasmanian Institute of AgricultureHeterogeneous insolation distribution in agrivoltaic systems (AVS) impacts plant growth beneath solar panels via shading and perturbed evapotranspiration profiles. Most agricultural systems models, meanwhile, assume uniform irradiance patterns across an entire field when simulating biomass production, meaning that they cannot readily account for spatiotemporal trade-offs between agricultural production and energy generation pertaining to AVS. We develop a simple approach for enumerating trade-offs between crop/pasture production and energy generation that accounts for spatial heterogeneity in insolation that typifies most AVS fields. First, long-term spatially explicit daily insolation profiles at the ground surface are produced for several layouts, including variations in PV panel orientations, row spacings, heights and tilt angles. A clustering technique was then applied to all insolation profiles to group them into rationally bounded cluster groups. The insolation profile of each cluster group was set as an input to a conventional point-based agricultural systems model to determine agricultural production under heterogeneous insolation profiles. The proposed approach is applied to a case study near Hobart, Australia, to determine an optimal layout that maximizes energy generation and plant growth associated with 81 AVS layouts. We find a manageable number (19 clusters) of point-based agricultural model scenarios capture much of the variance in insolation variability associated with varying AVS layouts. Compared with open fields, we show that AVS can amplify pasture growth rates during late spring and early summer. The optimal layout for our case study region enhanced land productivity by 47% while maintaining 80% of agricultural production compared with open-field agriculture. https://www.tib-op.org/ojs/index.php/agripv/article/view/1370Agrivoltaic System (AVS)AVS Layout OptimizationLivestock-Based AVSK-Means ClusteringLand Equivalent Ratio
spellingShingle Ganesh Pandey
Sarah Lyden
Evan Franklin
Matthew Tom Harrison
A Simple Approach for Clustering Common Insolation Profiles in Agrivoltaic Systems
AgriVoltaics Conference Proceedings
Agrivoltaic System (AVS)
AVS Layout Optimization
Livestock-Based AVS
K-Means Clustering
Land Equivalent Ratio
title A Simple Approach for Clustering Common Insolation Profiles in Agrivoltaic Systems
title_full A Simple Approach for Clustering Common Insolation Profiles in Agrivoltaic Systems
title_fullStr A Simple Approach for Clustering Common Insolation Profiles in Agrivoltaic Systems
title_full_unstemmed A Simple Approach for Clustering Common Insolation Profiles in Agrivoltaic Systems
title_short A Simple Approach for Clustering Common Insolation Profiles in Agrivoltaic Systems
title_sort simple approach for clustering common insolation profiles in agrivoltaic systems
topic Agrivoltaic System (AVS)
AVS Layout Optimization
Livestock-Based AVS
K-Means Clustering
Land Equivalent Ratio
url https://www.tib-op.org/ojs/index.php/agripv/article/view/1370
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