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Optimization of the design of an agrophotovoltaic system in future climate conditions in South Korea

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  • Kim, Sumin
  • Kim, Sojung

Abstract

A hybrid framework is proposed to identify the optimal design of agrophotovoltaic (APV) system that can be a promising alternative to resolve the food security issue by producing both solar energy and crops. It consists of four components: (1) Environmental database involving historical climate and soil data, (2) Solar energy module estimating energy quantity via polynomial regression (PR), (3) ALMANAC simulation that estimates crop parameters and yields; and (4) Analysis module identifying the optimum operational plan under climate change scenarios. The framework is calibrated with historical data collected from the APV system at the Jeollanamdo Agricultural Research and Extension Services (35.0161° N, 126.7108° E) in South Korea. Five crops of sesame, mungbean, red bean, corn, and soybean are considered under four climate change scenarios (i.e., SSP126, SSP245, SSP370, and SSP585) with two different time horizons (i.e., 2021–2050 and 2051–2080). According to the experiment, the APV system with mungbean is the most profitable with the unit profit of $ 77.44/m2 under 25.6% shading ratio from 2021 to 2050. The novel framework for the optimal design of the APV system enables to increase the income of a famer and resolve the food security under climate change environment in future.

Suggested Citation

  • Kim, Sumin & Kim, Sojung, 2023. "Optimization of the design of an agrophotovoltaic system in future climate conditions in South Korea," Renewable Energy, Elsevier, vol. 206(C), pages 928-938.
    • Handle: RePEc:eee:renene:v:206:y:2023:i:c:p:928-938
    DOI: 10.1016/j.renene.2023.02.090
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    References listed on IDEAS

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    1. Mamun, Mohammad Abdullah Al & Dargusch, Paul & Wadley, David & Zulkarnain, Noor Azwa & Aziz, Ammar Abdul, 2022. "A review of research on agrivoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
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    3. Sojung Kim & Sumin Kim, 2021. "Performance Estimation Modeling via Machine Learning of an Agrophotovoltaic System in South Korea," Energies, MDPI, vol. 14(20), pages 1-13, October.
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    7. Dupraz, C. & Marrou, H. & Talbot, G. & Dufour, L. & Nogier, A. & Ferard, Y., 2011. "Combining solar photovoltaic panels and food crops for optimising land use: Towards new agrivoltaic schemes," Renewable Energy, Elsevier, vol. 36(10), pages 2725-2732.
    8. Casares de la Torre, F.J. & Varo, Marta & López-Luque, R. & Ramírez-Faz, J. & Fernández-Ahumada, L.M., 2022. "Design and analysis of a tracking / backtracking strategy for PV plants with horizontal trackers after their conversion to agrivoltaic plants," Renewable Energy, Elsevier, vol. 187(C), pages 537-550.
    9. Trommsdorff, Max & Kang, Jinsuk & Reise, Christian & Schindele, Stephan & Bopp, Georg & Ehmann, Andrea & Weselek, Axel & Högy, Petra & Obergfell, Tabea, 2021. "Combining food and energy production: Design of an agrivoltaic system applied in arable and vegetable farming in Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
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    1. Sojung Kim & Sumin Kim, 2023. "Economic Feasibility Comparison between Building-Integrated Photovoltaics and Green Systems in Northeast Texas," Energies, MDPI, vol. 16(12), pages 1-14, June.

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