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A Decision Support Software Application for the Design of Agrophotovoltaic Systems in Republic of Korea

Author

Listed:
  • Youngjin Kim

    (Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea)

  • Yeongjae On

    (Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea)

  • Junyong So

    (Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea)

  • Sumin Kim

    (Department of Environmental Horticulture & Landscape Architecture, College of Life Science & Biotechnology, Dankook University, Cheonan-si 31116, Republic of Korea)

  • Sojung Kim

    (Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea)

Abstract

Agrophotovoltaic (APV) systems produce both solar energy and crops, so they are considered a sustainable alternative to traditional solar power plants, which can potentially destroy farmlands. However, it is challenging to diffuse APV systems because of their high installation and operating costs. Thus, to resolve the issue by maximizing the productivity and profits of an APV system, this study aims to propose a mobile-phone-based decision support system (DSS) for a supply chain network design for APV systems in South Korea using satellite imagery incorporating geographic information system (GIS) data. Particularly, polynomial regression models estimating annual corn ( Zea mays ) yields and the predicted generation of electricity were developed and integrated with the proposed DSS. Field experiment data provided by the APV system at Jeollanamdo Agricultural Research and Extension Services in South Korea were utilized. Two photovoltaic (PV) module types (mono-facial and bi-facial) and three different shading ratios for APV systems (21.3%, 25.6%, and 32.0%) were considered design factors for APV systems. An optimal network structure of 6 candidate APV systems and 15 agricultural markets was devised using the generalized reduced gradient (GRG) method. The profits of the six candidate APV systems are mainly affected by the transportation costs to the markets and the policy of the electricity selling prices. As a result, the proposed supply chain design framework successfully identifies an APV system network with maximum profits from crop production as well as electricity generation.

Suggested Citation

  • Youngjin Kim & Yeongjae On & Junyong So & Sumin Kim & Sojung Kim, 2023. "A Decision Support Software Application for the Design of Agrophotovoltaic Systems in Republic of Korea," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8830-:d:1159702
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    References listed on IDEAS

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    4. 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.
    5. Sojung Kim & Youngjin Kim & Youngjae On & Junyong So & Chang-Yong Yoon & Sumin Kim, 2022. "Hybrid Performance Modeling of an Agrophotovoltaic System in South Korea," Energies, MDPI, vol. 15(18), pages 1-13, September.
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    Cited by:

    1. Hyeongjun Lim & Sojung Kim, 2024. "Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production," Energies, MDPI, vol. 17(20), pages 1-19, October.

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