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An analytical method for the measurement of energy system sustainability in urban areas

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  • Jovanovic, Marina
  • Afgan, Naim
  • Bakic, Vukman

Abstract

Assessing the sustainability of urban energy systems and forecasting their development are important topics that have been the focus of recent research. In this paper, an approach for the measurement the sustainability of an urban energy system is introduced. The approach is based on prediction of the future energy needs within the consuming sectors of a city by specification of energy system development scenarios and validation of the scenarios by a multi-criteria decision method. Prediction of energy needs for the area of the city using the simulation model, model for analysis of the energy demands (MAED) is done. Finish the last level of aggregation, using the method of multi-criteria analysis, is getting the General Index of Sustainability (GIS), which shows a measure of the validity or viability, or quality of the investigated scenarios. In this way, the mathematical and graphical made a synthesis of all the indicators that are relevant to sustainable development. The accuracy in determining the mean of the GIS is checked by calculating the standard deviation. Also, a measure of reliability of the preference when watching a few consecutive scenarios was performed. The defined scenarios take into account the utilization of different energy sources, the exploitation of existing energy plants and infrastructure, and the building of new plants. The sustainability criteria are described by a unique set of economic, social and ecological indicators. The new approach was used to forecast the development of sustainable energy system in Belgrade, Serbia.

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  • Jovanovic, Marina & Afgan, Naim & Bakic, Vukman, 2010. "An analytical method for the measurement of energy system sustainability in urban areas," Energy, Elsevier, vol. 35(9), pages 3909-3920.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:9:p:3909-3920
    DOI: 10.1016/j.energy.2010.06.010
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    9. Pingtao Yi & Weiwei Li & Lingyu Li, 2018. "Evaluation and Prediction of City Sustainability Using MCDM and Stochastic Simulation Methods," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    10. Indre Siksnelyte & Edmundas Kazimieras Zavadskas & Dalia Streimikiene & Deepak Sharma, 2018. "An Overview of Multi-Criteria Decision-Making Methods in Dealing with Sustainable Energy Development Issues," Energies, MDPI, vol. 11(10), pages 1-21, October.
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    12. Sarangi, Gopal K. & Mishra, Arabinda & Chang, Youngho & Taghizadeh-Hesary, Farhad, 2019. "Indian electricity sector, energy security and sustainability: An empirical assessment," Energy Policy, Elsevier, vol. 135(C).
    13. Azevedo, Isabel & Leal, Vítor M.S., 2017. "Methodologies for the evaluation of local climate change mitigation actions: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 681-690.
    14. Strantzali, Eleni & Aravossis, Konstantinos, 2016. "Decision making in renewable energy investments: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 885-898.
    15. Pardo Martínez, Clara Inés, 2015. "Energy and sustainable development in cities: A case study of Bogotá," Energy, Elsevier, vol. 92(P3), pages 612-621.
    16. Lazaroiu, George Cristian & Roscia, Mariacristina, 2012. "Definition methodology for the smart cities model," Energy, Elsevier, vol. 47(1), pages 326-332.
    17. Isabel Azevedo & Vítor Leal, 2020. "Factors That Contribute to Changes in Local or Municipal GHG Emissions: A Framework Derived from a Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-47, June.
    18. Liu, Gang, 2014. "Development of a general sustainability indicator for renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 611-621.
    19. Han, Baolong & Liu, Hongxiao & Wang, Rusong, 2015. "Urban ecological security assessment for cities in the Beijing–Tianjin–Hebei metropolitan region based on fuzzy and entropy methods," Ecological Modelling, Elsevier, vol. 318(C), pages 217-225.
    20. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    21. Pasimeni, Maria Rita & Petrosillo, Irene & Aretano, Roberta & Semeraro, Teodoro & De Marco, Antonella & Zaccarelli, Nicola & Zurlini, Giovanni, 2014. "Scales, strategies and actions for effective energy planning: A review," Energy Policy, Elsevier, vol. 65(C), pages 165-174.
    22. Pukšec, Tomislav & Mathiesen, Brian Vad & Novosel, Tomislav & Duić, Neven, 2014. "Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia," Energy, Elsevier, vol. 76(C), pages 198-209.
    23. Pingtao Yi & Lu Wang & Danning Zhang & Weiwei Li, 2019. "Sustainability Assessment of Provincial-Level Regions in China Using Composite Sustainable Indicator," Sustainability, MDPI, vol. 11(19), pages 1-20, September.

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