Skip to main content

Research Repository

Advanced Search

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

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

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


Authors

Dr Ruiqi Wang ruiqi.wang@durham.ac.uk
Post Doctoral Research Associate

Pradip Mondal



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.

Citation

Roy, D., Zhu, S., Wang, R., Mondal, P., Ling-Chin, J., & Roskilly, A. P. (2024). Techno-economic and environmental analyses of hybrid renewable energy systems for a remote location employing machine learning models. Applied Energy, 361, Article 122884. https://doi.org/10.1016/j.apenergy.2024.122884

Journal Article Type Article
Acceptance Date Feb 16, 2024
Online Publication Date Mar 5, 2024
Publication Date 2024-05
Deposit Date Mar 12, 2024
Publicly Available Date Mar 13, 2024
Journal Applied Energy
Print ISSN 0306-2619
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 361
Article Number 122884
DOI https://doi.org/10.1016/j.apenergy.2024.122884
Keywords Management, Monitoring, Policy and Law; Mechanical Engineering; General Energy; Building and Construction
Public URL https://durham-repository.worktribe.com/output/2326595

Files





You might also like



Downloadable Citations