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Techno-economic and environmental analyses of hybrid renewable energy systems for a remote location employing machine learning models

Author

Listed:
  • Roy, Dibyendu
  • Zhu, Shunmin
  • Wang, Ruiqi
  • Mondal, Pradip
  • Ling-Chin, Janie
  • Roskilly, Anthony Paul

Abstract

This article offers a detailed investigation into the technical, economic along with environmental performance of four configurations of hybrid renewable energy systems (HRESs), aiming at supplying renewable electricity to a remote location, Henry Island in India. The study explores combinations involving photovoltaic (PV) panels, wind turbines, biogas generators, batteries, and converters, while evaluating their economic, technical, and environmental performance. The economic analysis yield that among all the systems examined, the PV, wind turbine, biogas generator, battery, and converter integrated configuration stands out with highly favourable results, showcasing the minimal value of levelized cost of electricity (LCOE) at $0.4224 per kWh and the lowest net present cost (NPC) at $6.41 million. However, technical analysis yield that the configuration comprising wind turbines, PV panels, converters, and battery yields a maximum excess electricity output of 2,838,968 kWh/yr. Additionally, machine learning techniques are employed to analyse economic and environmental performance data. The study shows Bilayered Neural Network model achieves exceptional accuracy in predicting LCOE, while the Medium Neural Network model proves to be the most accurate in predicting environmental performance. These findings provide valuable perception into the design and optimisation of HRES systems for off-grid applications in remote regions, taking into account their technical, economic, and environmental aspects.

Suggested Citation

  • Roy, Dibyendu & Zhu, Shunmin & Wang, Ruiqi & Mondal, Pradip & Ling-Chin, Janie & Roskilly, Anthony Paul, 2024. "Techno-economic and environmental analyses of hybrid renewable energy systems for a remote location employing machine learning models," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002678
    DOI: 10.1016/j.apenergy.2024.122884
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    References listed on IDEAS

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