Application of robust mathematical optimization approach in solving the problem of selecting energy supply methods for blockchain technology in industrial environments
Subject Areas : Operations Management
Akbar Alam Tabriz
1
,
Ali Rezaian
2
,
akram alikazemi
3
1 - Department of Management, Shahid Beheshti University, Tehran, Iran.
2 - استاد دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی
3 - azad university
Keywords: Energy Supply Methods, Economic Evaluation, Best-Worst Method, VIKOR method, Robust Planning,
Abstract :
The purpose of this research is to select the best energy supply methods for blockchain technology and to optimize the use of each of the selected methods.For this purpose,a two-part hybrid approach based on best-worst multi-criteria decision making methods,VIKOR method and mathematical programming model has been used under uncertainty conditions.To implement this approach,first by reviewing the literature,the effective criteria in selecting the best method were extracted and then,the options or energy supply methods using blockchain technology were determined based on the experts’ opinion.Finally,the weight of the criteria and the final ranking of the selected options were determined by the best-worst and VIKOR methods, respectively.In the second part,in order to determine the optimal amount of using available facilities for blockchain technology to implement the selected method,a robust mathematical model is designed in which the NPV criterion is used as an economic evaluation measure of using the selected method in the first part. was taken As the obtained results show,the criteria of "implementability"and"productivity level"and"coordination with consumption pattern correction policies"have the highest level of importance with the weight of 0.339, 0.14and 0.14, respectively,in order to choose the best option.Also, the best score compared to other options belongs to the energy supply method"electricity generation from the combined use of national grid and solar panels".Solving the mathematical model shows that the highest level of economic efficiency belongs to the use of solar panels to provide a significant part of the need for power.
1. Guo, H. and X. Yu, A survey on blockchain technology and its security. Blockchain: research and applications, 2022. 3(2): p. 100067.
2. Gad, A.G., et al., Emerging trends in blockchain technology and applications: A review and outlook. Journal of King Saud University-Computer and Information Sciences, 2022. 34(9): p. 6719-6742.
3. Wang, Q. and M. Su, Integrating blockchain technology into the energy sector—from theory of blockchain to research and application of energy blockchain. Computer Science Review, 2020. 37: p. 100275.
4. Alzoubi, Y.I. and A. Mishra, Green blockchain–A move towards sustainability. Journal of Cleaner Production, 2023. 430: p. 139541.
5. Wang, Q., R. Li, and L. Zhan, Blockchain technology in the energy sector: From basic research to real world applications. Computer Science Review, 2021. 39: p. 100362.
6. Sedlmeir, J., et al., The energy consumption of blockchain technology: Beyond myth. Business & Information Systems Engineering, 2020. 62(6): p. 599-608.
7. Capponi, A., R. Jia, and Y. Wang, The evolution of blockchain: from lit to dark. arXiv preprint arXiv:2202.05779, 2022.
8. UNCTAD, Harnessing Blockchain for Sustainable Development: Prospects and Challenges. 2021: UN.
9. Sharif, A., et al., Analysis of the spillover effects between green economy, clean and dirty cryptocurrencies. Energy Economics, 2023. 120: p. 106594.
10. Schulz, K. and M. Feist, Leveraging blockchain technology for innovative climate finance under the Green Climate Fund. Earth System Governance, 2021. 7: p. 100084.
11. Vranken, H., Sustainability of bitcoin and blockchains. Current opinion in environmental sustainability, 2017. 28: p. 1-9.
12. House, W., Climate and energy implications of crypto-assets in the united states. Accessed October, 2022. 21.
13. Bada, A.O., et al. Towards a green blockchain: A review of consensus mechanisms and their energy consumption. in 2021 17th international conference on distributed computing in sensor systems (DCOSS). 2021. IEEE.
14. Nygaard, A. and R. Silkoset, Sustainable development and greenwashing: How blockchain technology information can empower green consumers. Business Strategy and the Environment, 2023. 32(6): p. 3801-3813.
15. Varavallo, G., et al., Traceability platform based on green blockchain: an application case study in dairy supply chain. Sustainability, 2022. 14(6): p. 3321.
16. Bao, Z., et al., Towards green and efficient blockchain for energy trading: a non-cooperative game approach. IEEE Internet of Things Journal, 2023.
17. Mohamed, S.K., et al., Blockchain technology adoption for improved environmental supply chain performance: The mediation effect of supply chain resilience, customer integration, and green customer information sharing. Sustainability, 2023. 15(10): p. 7909.
18. Qin, M., et al., Blockchain market and green finance: The enablers of carbon neutrality in China. Energy Economics, 2023. 118: p. 106501.
19. Polas, M.R.H., et al., Blockchain technology as a game changer for green innovation: Green entrepreneurship as a roadmap to green economic sustainability in Peru. Journal of Open Innovation: Technology, Market, and Complexity, 2022. 8(2): p. 62.
20. Kontu, K., et al., Multicriteria evaluation of heating choices for a new sustainable residential area. Energy and Buildings, 2015. 93: p. 169-179.
21. Štreimikienė, D., J. Šliogerienė, and Z. Turskis, Multi-criteria analysis of electricity generation technologies in Lithuania. Renewable energy, 2016. 85: p. 148-156.
22. Al Garni, H., et al., A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia. Sustainable energy technologies and assessments, 2016. 16: p. 137-150.
23. Çelikbilek, Y. and F. Tüysüz, An integrated grey based multi-criteria decision making approach for the evaluation of renewable energy sources. Energy, 2016. 115: p. 1246-1258.
24. Jung, N., et al., Social acceptance of renewable energy technologies for buildings in the Helsinki Metropolitan Area of Finland. Renewable energy, 2016. 99: p. 813-824.
25. Barragán, A., P. Arias, and J. Terrados, Renewable energy generation technologies on urban scale. Renewable Energy and Power Quality, 2017. 1(15).
26. Talukdar, M.A., H. Rahman, and P.C. Sarker, Multi criteria decision analysis Algorithm based optimal selection of PV panel for grid-tie PV electricity generation system in context of dhaka, Bangladesh. Life, 2017. 5(2).
27. Strantzali, E., K. Aravossis, and G.A. Livanos, Evaluation of future sustainable electricity generation alternatives: The case of a Greek island. Renewable and sustainable energy reviews, 2017. 76: p. 775-787.
28. Kausika, B., O. Dolla, and W. Van Sark, Assessment of policy based residential solar PV potential using GIS-based multicriteria decision analysis: A case study of Apeldoorn, The Netherlands. Energy Procedia, 2017. 134: p. 110-120.
29. Haddad, B., A. Liazid, and P. Ferreira, A multi-criteria approach to rank renewables for the Algerian electricity system. Renewable energy, 2017. 107: p. 462-472.
30. Kumar, M. and C. Samuel, Selection of best renewable energy source by using VIKOR method. Technology and Economics of Smart Grids and Sustainable Energy, 2017. 2: p. 1-10.
31. Wu, Y., et al., Social sustainability assessment of small hydropower with hesitant PROMETHEE method. Sustainable cities and society, 2017. 35: p. 522-537.
32. Aboushal, E., Applying GIS Technology for optimum selection of Photovoltaic Panels “Spatially at Defined Urban Area in Alexandria, Egypt”. Alexandria engineering journal, 2018. 57(4): p. 4167-4176.
33. Rathore, N. and M. Singh. Selection of optimal renewable energy resources in uncertain environment using ARAS-Z methodology. in 2019 International Conference on Communication and Electronics Systems (ICCES). 2019. IEEE.
34. Rani, P., et al., A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection. Journal of Cleaner Production, 2020. 257: p. 120352.
35. Liu, J., et al., Selection of renewable energy alternatives for green blockchain investments: A hybrid IT2-based fuzzy modelling. Archives of Computational Methods in Engineering, 2021: p. 1-15.
36. Rezaei, J., Best-worst multi-criteria decision-making method. Omega, 2015. 53: p. 49-57.
37. Rezaei, J., Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 2016. 64: p. 126-130.
38. Opricovic, S., Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 1998. 2(1): p. 5-21.
39. Gupta, H., Evaluating service quality of airline industry using hybrid best worst method and VIKOR. Journal of Air Transport Management, 2018. 68: p. 35-47.
