Synergistic integration of digital twins and sustainable industrial internet of things for new generation energy investments

Gang Kou; Hasan Dinçer; Serhat Yüksel; Muhammet Deveci

doi:10.1016/j.jare.2023.11.023

Synergistic integration of digital twins and sustainable industrial internet of things for new generation energy investments

J Adv Res. 2023 Nov 24:S2090-1232(23)00362-4. doi: 10.1016/j.jare.2023.11.023. Online ahead of print.

Authors

Gang Kou¹, Hasan Dinçer², Serhat Yüksel³, Muhammet Deveci⁴

Affiliations

¹ School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China. Electronic address: kougang@swufe.edu.cn.
² School of Business, Istanbul Medipol University, Istanbul, Turkey; University of Religions and Denominations, Qom, Iran; Clinic of Economics, Azerbaijan State University of Economics (UNEC), Istiqlaliyyat Str. 6, Baku, AZ1141, Azerbaijan. Electronic address: hdincer@medipol.edu.tr.
³ School of Business, Istanbul Medipol University, Istanbul, Turkey; Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon; Clinic of Economics, Azerbaijan State University of Economics (UNEC), Istiqlaliyyat Str. 6, Baku, AZ1141, Azerbaijan. Electronic address: serhatyuksel@medipol.edu.tr.
⁴ Department of Industrial Engineering, Turkish Naval Academy, National Defence University, 34940 Tuzla, Istanbul, Turkey; The Bartlett School of Sustainable Construction, University College London, 1-19 Torrington Place, London WC1E 7HB, UK; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon. Electronic address: muhammetdeveci@gmail.com.

PMID: 38008174
DOI: 10.1016/j.jare.2023.11.023

Abstract

Introduction: This study aims to identify optimal digital twin policies for enhancing renewable energy projects. Through a comprehensive analysis, the research evaluates the potential of digital twins in the renewable energy sector while considering triple bottom line perspectives.

Objectives: The study's main goal is to prioritize digital twin policies that can effectively boost renewable energy projects. The research aims to demonstrate the practical application and reliability of a proposed evaluation model.

Methods: Nine criteria, derived from literature review and triple bottom line viewpoints, are selected. Using the decision-making trial and evaluation laboratory (DEMATEL) methodology and Quantum picture fuzzy rough sets, criteria weights are determined. Quantum picture fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) evaluates sustainable industrial internet of things strategies in new-gen energy investments. VIsekriterijumska optimizcija i KOmpromisno Resenje (VIKOR) methodology enables a comparative assessment, and sensitivity analysis is conducted across nine cases.

Results: Consistent outcomes across various methods validate the model's reliability. Ecosystem preservation carries the highest weight (0.1147), followed by resource policy optimization with digital twins (0.1139). Distributed energy resilience ranks first (RCi 0.576), closely followed by energy efficiency optimization (RCi 0.542).

Conclusion: This study underscores ecosystem preservation and efficient resource policies as pivotal for successful digital twin deployment in renewable energy projects. The findings highlight digital twins' potential contribution to environmental protection and ecosystem sustainability, emphasizing resource efficiency through their effective use.

Keywords: Digital Twins; Investment Decision, Renewables; Picture fuzzy rough numbers; Quantum Mechanics; Sustainable industrial internet of things; Triple Bottom Line.