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Optimal planning of a rooftop PV system using GIS-based reinforcement learning

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  • Jung, Seunghoon
  • Jeoung, Jaewon
  • Kang, Hyuna
  • Hong, Taehoon

Abstract

This study aimed to develop a geographic information system (GIS)-based reinforcement learning (RL) model for optimal planning of a rooftop PV system, considering the uncertainty of future scenarios throughout the life cycle of buildings. To that end, GIS was used to establish the spatial data for the rooftop PV installation, and an RL model was developed to maximize the economic profit of the rooftop PV installation in various locations and future scenarios. The developed model was applied to residential buildings in Nonhyeon district, South Korea to evaluate their economic profitability and to compare the model with the existing planning methods. With the use of the developed GIS-based RL model, the rooftop PV system became economically feasible, achieving average economic profit of 539,197 USD over all scenarios for all target buildings which was higher than that of the existing models by 4.4% and 4.3%. Furthermore, the developed model outperformed the existing models especially in volatile scenarios with lower solar radiation. Therefore, the use of the proposed GIS-based RL model can optimize the economic feasibility of rooftop PV systems for buildings, which will benefit building owners and community-level energy business owners. In conclusion, the developed model can promote the adoption of rooftop PV systems, which have 91.8% lower global warming potential than the Korean mixed grid, without additional subsidies to achieve Korea’s national CO2 emission reduction plan.

Suggested Citation

  • Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921006607
    DOI: 10.1016/j.apenergy.2021.117239
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    6. Pei Ye, Song & Hua Liu, Yi & Chung Wang, Shun & Yu Pai, Hung, 2022. "A novel global maximum power point tracking algorithm based on Nelder-Mead simplex technique for complex partial shading conditions," Applied Energy, Elsevier, vol. 321(C).
    7. Saheed Lekan Gbadamosi & Fejiro S. Ogunje & Samuel Tita Wara & Nnamdi I. Nwulu, 2022. "Techno-Economic Evaluation of a Hybrid Energy System for an Educational Institution: A Case Study," Energies, MDPI, vol. 15(15), pages 1-12, August.
    8. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).
    9. Kang, Hyuna & Jung, Seunghoon & Kim, Hakpyeong & Hong, Juwon & Jeoung, Jaewon & Hong, Taehoon, 2023. "Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    10. Ren, Haoshan & Ma, Zhenjun & Chan, Antoni B. & Sun, Yongjun, 2023. "Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities," Energy, Elsevier, vol. 263(PA).
    11. Ren, Haoshan & Sun, Yongjun & Norman Tse, Chung Fai & Fan, Cheng, 2023. "Optimal packing and planning for large-scale distributed rooftop photovoltaic systems under complex shading effects and rooftop availabilities," Energy, Elsevier, vol. 274(C).
    12. Kang, Hyuna & Jung, Seunghoon & Lee, Minhyun & Hong, Taehoon, 2022. "How to better share energy towards a carbon-neutral city? A review on application strategies of battery energy storage system in city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    13. Meng, B. & Loonen, R.C.G.M. & Hensen, J.L.M., 2022. "Performance variability and implications for yield prediction of rooftop PV systems – Analysis of 246 identical systems," Applied Energy, Elsevier, vol. 322(C).

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