WIT Press

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


Free (open access)


Volume 7 (2022), Issue 3



Page Range

276 - 286

Paper DOI



WIT Press


A. Domnikov, M. Khodorovsky & l. Domnikova


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.


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