Importance The target-oriented and forecasting fuel and energy balance of Russia for the period until 2035 presumes advanced development of the electric-power industry for the realization of large-scale electrification of the national economy with the growth of installed power in power plants by more than 1/3 times increase and 1.6 times increase of generation of electricity. The change of functioning conditions directly impacts each electric-power industry facility. Because of this, at present, Russia is experiencing reforming of electric-power industry: of wholesale market liberalization of electric energy, implementation of the energy-saving and energy-efficiency programs, changing of tariff regulation and creation of wholesale market of power capacities. As a result, the organizations need new tools and technologies for transforming regulation market into competition market. A competition market is characterized by decision-making under condition of uncertainty. As a result, there is a need of forecasting of potential loses, and it means creating risk-management system. Objectives The aim of this research is the development of risk estimation model of electric energy company for constructing optimal strategy for market behavior. For achieving this goal, there were some tasks which were set up and solved, namely: different approaches to modeling risk-management system depending on quantity and quality of input data have been analyzed and compared; fuzzy-logic model of risk-management system of an electric-power company based upon key indicators and developed modeling of its parameters. Methods In this research, we have developed fuzzy-modeling methodology of evaluation and risks management of an electric-power company. Results We have constructed a model of risk management system. In the proposed model, we have used the Mamdani-Type Fuzzy Inference according to expert fuzzy knowledge basis. The development process of the system is implemented in MatLab environment using the Fuzzy Logic Toolbox package. The paper offered practical recommendations concerning construction methods of the mentioned system, and also carried out its parameters modeling. Conclusions and Relevance The practical significance of the research lies in the opportunity of applying the developed system as universal tool for assessing risks and creating a set of measures to minimize it.
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