Methodological approach to choosing alternatives for the development of energy systems in conditions of uncertainty and multi-criteria

Methodological approach to choosing alternatives for the development of energy systems in conditions of uncertainty and multi-criteria

A. Domnikov M. Khodorovsky l. Domnikova

Academic Department of Banking and Investment Management, Ural Federal University named after the First President of Russia B.N. Yeltsin, Russia

Page: 
276-286
|
DOI: 
https://doi.org/10.2495/EQ-V7-N3-276-286
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

© 2022 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract: 

Power engineering is one of the most dynamic industries in the modern world; it applies specific production and management technologies and also assumes a complex structural transformation of power systems and transition of power engineering business to a qualitatively new level providing sustainable power supply. Within the context of existing situation, the electric power industry, which is currently actively developing, is an important element of power infrastructure that requires a long-term and continuous solution of the challenges the industry faces. These are the circumstances of the development of methodological tools and decision-making procedures based on multi-criteria analysis, since the tasks of developing energy systems in modern conditions represent the most typical class of tasks where the problem of taking into account multi-criteria and uncertainty is most acute. The purpose of the study is to develop methods for the formation and comparison of options for the development of electric power systems in conditions of uncertainty and multi-criteria. The use of fuzzy set reporting models and new decision-making procedures based on fuzzy relations are proposed to address the development challenges. When addressing them, a considerable room for applying multi-criteria analysis algorithms to various aspects of the problem of power systems development in the fuzzy information environment was demonstrated. The results of the study are presented in the form of an analysis of the rational concentration of power plant capacities, which made it possible to identify the most effective way to reduce the plant’s installed capacity while increasing the role of environmental criteria.

Keywords: 

competition, ecology, efficiency, fuzzy sets, mathematical economic models, power industry, reliability, strategy, uncertainty

  References

[1] Berg, C., Sustainable action: overcoming the barriers. Routledge studies in sustainability: London, 2019. https://doi.org/10.4324/9780429060786

[2] EIB Group Climate Bank Roadmap 2021–2025. European Investment Bank, 2020. https://doi.org/10.2867/503343

[3] Horton, S., Financing the sustainable development goals. Achieving the Sustainable Development Goals, Routledge: London, pp. 206–225, 2019. https://doi.org/10.4324/9780429029622

[4] Bhattacharyya, S. C., Energy data and energy balance. Energy Economics, Springer Verlag: London, 2011. https://doi.org/10.1007/978-0-85729-268-1

[5] Calderóna, C., Underwood, C., Yi, J., Mcloughlin, A. & Williams, B., An area-based modelling approach for planning heating electrification. Energy Policy, 131, pp. 262–280, 2019. https://doi.org/10.1016/j.enpol.2019.04.023

[6] Zadeh, L., Fuzzy sets. Information and Control, 8(3), pp. 338–353, 1965. https://doi.org/10.1016/s0019-9958(65)90241-x

[7] Zadeh, L., Fuzzy logic – A personal perspective. Fuzzy Sets and System, 281, pp. 4–20, 2015. https://doi.org/10.1016/j.fss.2015.05.009

[8] Zadeh, L., The Concept of a Linguistic Variable and its Application to Approximate Reasoning. American Elsever Publishing Company: New York, 1973.

[9] Seising, R., Pioneers of vagueness, haziness and fuzziness in the 20th century. Forging New Frontiers: Fuzzy Pioneers, eds. M. Nikravesh, J. Kacprzyk, & L. Zadeh, Studies in Fuzziness and Soft Computing. Springer: Berlin-Heidelberg, pp. 55–81, 2007. https://doi.org/10.1007/978-3-540-73182-5_4

[10] Wang, X., Ruan, D. & Kerre, E., Mathematics of Fuzziness - Basic Issues. SpringerVerlag: Berlin-Heidelberg, 2009.

[11] Domnikov, A., Khodorovsky, M. & Domnikova, L., Methodological approach to the research of energy cogeneration systems operational reliability indicators. International Journal of Energy Production and Management, 6(3), pp. 263–276, 2021. https://doi.org/10.2495/EQ-V6-N3-263-276

[12] Domnikov, A., Khodorovsky, M. & Domnikova, L., Decision support system used to improve the competitiveness of a power generating company under conditions of uncertainty. WIT Transactions on Ecology and the Environment, 254, pp. 15–23, 2021. https://doi.org/10.2495/ESUS210021

[13] Jardine, N. & Sibson, R., Mathematical Taxonomy. John Wiley and Sons: London, 1971.

[14] Domnikov, A., Khodorovsky, M. & Domnikova, L., Identification and classification of the states of cogeneration systems by competitiveness levels of power generating companies. WIT Transactions on Ecology and the Environment, 254, pp. 25–32, 2021. https://doi.org/10.2495/ESUS210031

[15] Rada, E. C., Ragazzi, M., Torretta, V., Castagna, G., Adami, L. & Cioca, L. I., Circular economy and waste to energy. AIP Conference Proceedings, 1968(1), p. 030050, 2018. https://doi.org/10.1063/1.5039237

[16] Goldbach, K., Rotaru, A., Reichert, S., Stiff, G. & Gölz, S., Which digital energy services improve energy efficiency? A multi-criteria investigation with European experts. Energy Policy, 115, pp. 239–248, 2018. https://doi.org/10.1016/j.enpol.2017.12.036

[17] Johnstone, P. & Kivimaa, P., Multiple dimensions of disruption, energy transitions and industrial policy. Energy Research & Social Science, 37, pp. 260–265, 2018. https://doi.org/10.1016/j.erss.2017.10.027

[18] Manusov, V. & Ahyoev, J., Technical diagnostics of electric equipment with the use of fuzzy logic models. Applied Mechanics and Materials, 792, pp. 324–329, 2015. https://doi.org/10.4028/www.scientifc.net/amm.792.324

[19] Saaty, T., Relative measurement and its generalization in decision making why pairwise comparisions are central in mathematics for the measurement of intangible factors the analytic hierarchy/network process. Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales, 102, pp. 251–318, 2008. https://doi.org/10.1007/bf03191825

[20] Scholten, D. & Künneke, R., Towards the comprehensive design of energy infrastructures. Sustainability, 8(12), pp. 1291–1295, 2016. https://doi.org/10.3390/su8121291

[21] Lombardi, P. & Schwabe, F., Sharing economy as a new business model for energy storage systems. Applied Energy, 188, pp. 485–496, 2017. https://doi.org/10.1016/j.apenergy.2016.12.016

[22] McInerney, C. & Bunn, D., Expansion of the investor base for the energy transition. Energy Policy, 129, pp. 1240–1244, 2019. https://doi.org/10.1016/j.enpol.2019.03.035

[23] Xu, Y., Yang, K., Zhou, J. & Zhao, G., Coal-biomass co-firing power generation technology: current status, challenges and policy implications. Sustainability, 12, 3692, 2020. https://doi:10.3390/su12093692

[24] Celikyilmaz, A., Kacprzyk, J. & Türksen, I., Modeling uncertainty with fuzzy logic. with recent theory and applications. Studies in Fuzziness and Soft Computing. Springer: Berlin, Heidelberg, 2009.