Selection of optimum green energy source under smart city environment for sustainable energy management and planning: An eclectic decision

Authors

  • Chiranjib Bhowmik Department of Mechanical Engineering, Techno India University, Salt Lake, West Bengal, India
  • Manjeet Khare Department of Mechatronics Engineering, Parul Institute of Technology, Parul University Vadodara, Gujarat, India
  • Bhavesh Mewada Department of Mechanical Engineering, Parul Institute of Engineering & Technology, Parul University Vadodara, Gujarat, India
  • Prasenjit Chatterjee Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India
  • Gulay Demir Vocational School of Health Services, Sivas Cumhuriyet University, Sivas, Türkiye

DOI:

https://doi.org/10.31181/rme040105102023b

Keywords:

Smart city, Renewable energy, Sustainability, AHP, Sensitivity analysis

Abstract

The purpose of this paper is to present a decision support framework, able to assess and optimize the energy use in smart cities. This paper has taken a sincere endeavour to develop an innovative integrated analytical framework as a benchmark to understand to what extent the energy has been consumed in smart city spectrum. Initially five sectors (construction, water management, transport, waste treatment and public services) of smart city and their corresponding sub-factors are identified based on industry, academia partnership. Once the data were gathered, those were analysed using various statistical tools. This research is useful for the policymakers, executive people, especially those are working or involved in smart city development projects. The developed framework helps to identify the significant energy consumption sector and also suggest the suitable green energy alternatives for developing a cleaner and sustainable future. As this study discusses the various parameters related to smart city energy consumption sector in western India, it will have a huge practical potential on the proposed operational smart cities in India. Literature has witnessed minimum number of studies have been carried out on this proposed framework and that could improve the wellbeing of the people living in the cities.

References

Ahmad, S., & Tahar, R. M. (2014). Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: A case of Malaysia. Renewable energy, 63, 458-466.

Allen, C., Metternicht, G., & Wiedmann, T. (2016). National pathways to the Sustainable Development Goals (SDGs): A comparative review of scenario modelling tools. Environmental Science & Policy, 66, 199-207.

Al Garni, H., Kassem, A., Awasthi, A., Komljenovic, D., & Al-Haddad, K. (2016). A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia. Sustainable Energy Technologies and Assessments, 16, 137-150.

Alossta, A., Elmansouri, O., & Badi, I. (2021). Resolving a location selection problem by means of an integrated AHP-RAFSI approach. Reports in Mechanical Engineering, 2(1), 135–142. https://doi.org/10.31181/rme200102135a

Amer, M., & Daim, T. U. (2011). Selection of renewable energy technologies for a developing county: a case of Pakistan. Energy for Sustainable Development, 15(4), 420-435.

Badi, I., & Abdulshahed, A. (2021). Sustainability performance measurement for Libyan Iron and Steel Company using Rough AHP. Journal of Decision Analytics and Intelligent Computing, 1(1), 22-34. https://doi.org/10.31181/rme040129062023b

Badi, I., Abdulshahed, A., & Alghazel, E. (2023a). Using Grey-TOPSIS approach

+ for solar farm location selection in Libya. Reports in Mechanical Engineering, 4(1), 80-89. https://doi.org/10.31181/rme040129062023b

Badi, I., Stević, Ž., & Bouraima, M.E. (2023b). Overcoming Obstacles to Renewable Energy Development in Libya: An MCDM Approach towards Effective Strategy Formulation. Decision Making Advances, 1(1), 17-24. https://doi.org/10.31181/v120234

Baranovski, I., Stankovski, S., Ostojić, G., Nikolić, V., Simonović, M., & Stanojević, M. (2022). Toward a smart ecosystem with automated services. Facta Universitatis, Series: Mechanical Engineering. https://doi.org/10.22190/FUME220525033B

Beştepe, F., & Ozkan, S. (2019). A systematic review on smart city services and IoT-based technologies. In Proceedings of the 12th IADIS international conference information systems (pp. 255–259).

Beştepe, F., & Yildirim, S. Ö. (2022). Acceptance of IoT-based and sustainability-oriented smart city services: A mixed methods study. Sustainable Cities and Society, 80, 103794.

Bhowmik, C., Bhowmik, S., & Ray, A. (2020). Optimal green energy source selection: An eclectic decision. Energy & Environment, 31(5), 842-859.

Bibri, S.E., 2018. The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society 38, 230-253.

Calvillo, C. F., Sánchez-Miralles, A., & Villar, J. (2016). Energy management and planning in smart cities. Renewable and Sustainable Energy Reviews, 55, 273-287.

Cannemi, M., García-Melón, M., Aragonés-Beltrán, P., & Gómez-Navarro, T. (2014). Modeling decision making as a support tool for policy making on renewable energy development. Energy policy, 67, 127-137.

Erol, Ö., & Kılkış, B. (2012). An energy source policy assessment using analytical hierarchy process. Energy Conversion and management, 63, 245-252.

Giffnger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N., & Meijers, E. (2007). “Smart cities - Ranking of European medium-sized cities”. Vienna University of Technology

Ibrahim, M., El-Zaart, A., Adams, C., 2018. Smart sustainable cities roadmap: Readiness for transformation towards urban sustainability. Sustainable Cities and Society 37, 530-540.

Iddrisu, I., & Bhattacharyya, S. C. (2015). Sustainable Energy Development Index: A multi-dimensional indicator for measuring sustainable energy development. Renewable and Sustainable Energy Reviews, 50, 513-530.

Janeiro, L., & Patel, M. K. (2015). Choosing sustainable technologies. Implications of the underlying sustainability paradigm in the decision-making process. Journal of Cleaner Production, 105, 438-446.

Kahraman, C., Kaya, İ., & Cebi, S. (2009). A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy, 34(10), 1603-1616.

Narula, K., & Reddy, B. S. (2016). A SES (sustainable energy security) index for developing countries. Energy, 94, 326-343.

Nikolaou, I. E., & Kazantzidis, L. (2016). A sustainable consumption index/label to reduce information asymmetry among consumers and producers. Sustainable production and Consumption, 6, 51-61.

Patlitzianas, K. D., Doukas, H., Kagiannas, A. G., & Psarras, J. (2008). Sustainable energy policy indicators: Review and recommendations. Renewable Energy, 33(5), 966-973.

Petrillo, A., De Felice, F., Jannelli, E., Autorino, C., Minutillo, M., & Lavadera, A. L. (2016). Life cycle assessment (LCA) and life cycle cost (LCC) analysis model for a stand-alone hybrid renewable energy system. Renewable energy, 95, 337-355.

Pinter, L., Hardi, P., & Bartelmus, P. (2006). Sustainable development indicators: proposals for a way forward. 〈https://www.iisd.org/pdf/2005/measure_indicators_sd_way_forward.pdf〉, [2005, last accessed 01.08.2023].

Quijano H, R., Botero B, S., & Domínguez B. J. (2012). MODERGIS application: Integrated simulation platform to promote and develop renewable sustainable energy plans, Colombian case study. Renewable and Sustainable Energy Reviews, 16(7), 5176-5187.

Saaty, T. L. (1988). What is the analytic hierarchy process? (pp. 109-121). Springer Berlin Heidelberg.

Sahoo, S. K., & Goswami, S.S. (2023). A Comprehensive Review of Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions. Decision Making Advances, 1(1), 25-48. https://doi.org/10.31181/dma1120237

Singh, R. K., Murty, H. R., Gupta, S. K., & Dikshit, A. K. (2012). An overview of sustainability assessment methodologies. Ecological indicators, 15(1), 281-299.

Väisänen, S., Mikkilä, M., Havukainen, J., Sokka, L., Luoranen, M., & Horttanainen, M. (2016). Using a multi-method approach for decision-making about a sustainable local distributed energy system: A case study from Finland. Journal of Cleaner Production, 137, 1330-1338.

Wu, P. (2012). A mixed methods approach to technology acceptance research. Journal of the Association for Information Systems, 13(3). https://doi.org/10.17705/1jais.00287. available at.

Wüstenhagen, R., Wolsink, M., Bürer, M.J., 2007. Social acceptance of renewable energy innovation: An introduction to the concept. Energy Policy 35(5), 2683-2691.

Yi, S. K., Sin, H. Y., & Heo, E. (2011). Selecting sustainable renewable energy source for energy assistance to North Korea. Renewable and sustainable energy reviews, 15(1), 554-563.

Yigitcanlar, T., Kamruzzaman, M., Foth, M., Sabatini-Marques, J., da Costa, E., & Ioppolo, G. (2019). Can cities become smart without being sustainable? A systematic review of the literature. Sustainable cities and society, 45, 348-365.

Zeba, S. (2022). R&D Export and ICT Regimes in India. Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks: Real‐time Applications, 217-226.

Zhang, L., Xu, Y., Yeh, C. H., Liu, Y., & Zhou, D. (2016). City sustainability evaluation using multi-criteria decision making with objective weights of interdependent criteria. Journal of Cleaner Production, 131, 491-499.

Published

2023-10-05

How to Cite

Bhowmik , C., Khare , M., Mewada , B., Chatterjee, P., & Demir, G. (2023). Selection of optimum green energy source under smart city environment for sustainable energy management and planning: An eclectic decision. Reports in Mechanical Engineering, 4(1), 193–212. https://doi.org/10.31181/rme040105102023b