Techno-economic and environmental optimization of agrivoltaics: A case study of Cornell University

Tikumporn Kumdokrub and Fengqi You

Applied Energy, 2025, vol. 384, issue C, No S0306261925001667

Abstract: Agrivoltaic (AgV) co-locates crops and solar panels to mitigate land-use conflicts from rising production demands. This study advances AgV research by optimizing system efficiency through specific objective ratios for cost and environmental impact. Four models are developed to assess optimal AgV site design and benefits: two mixed-integer nonlinear programs (MINLP) that independently target economic gains and operational emission reductions, and two fractional programming (FP) models that evaluate economic and environmental benefits per water consumed, with varying minimum cropland constraints. Applied to agricultural and solar sites at Cornell University, Ithaca, New York, across seven crop types, optimal results from the MINLP models indicate that land should prioritize solar panels due to their superior economic and environmental benefits, with cabbage being the only exception due to its high crop value. In the FP models, prioritizing solar installation minimizes irrigation requirements, but economic benefits increase as more land is allocated to crops; a 90 % cropland allocation yields the highest revenues, ranging from 10.78 % to 186.77 % (US$5.86–34.88/m3) and achieving a land equivalent ratio of 4.40. The FP environmental model suggests limiting cropland to below 60 % for optimal emission reductions, reducing emissions to 54.01–112.18 metric tons of CO2eq/m3, which is lower than emissions from conventional separate crop and solar systems. The FP models balance economic and environmental benefits per irrigation unit, demonstrating AgV's water-use efficiency, and linking the complex relationship between inputs and outputs. Ultimately, site design choices should align with decision-makers' goals, whether prioritizing economic, environmental, or balanced system efficiencies.

Keywords: Agrivoltaic; Solar; Agriculture; Fractional programming; Land equivalent ratio; Environment; Pollution (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125436

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