Technologies for assessing the economic efficiency and risk management of small nuclear power plants at the main stages of their life cycle

Scientific approaches to the development of digital technologies for assessing economic efficiency and managing risks in energy production are presented using small nuclear plants as an example at the main stages of their life cycle. Efficiency indicators of the primary process equipment are determined based on the index method. It is shown that the calculation of the index enables a mathematically sound solution to the problem of efficiency assessment and risk management at the main stages of the plant life cycle. To evaluate the effectiveness of measures taken to ensure the safety of energy production, methods of predictive analysis and management in this area of modern production are proposed. The production system for operating complex technical systems is considered as an aggregate of three components: primary process equipment, resources, and human capital. A detailed set of factors influencing the reliability of personnel's production activities is presented. These factors include experience, safety culture, competence, and psychophysiological adaptation. Standard approaches to risk calculation are adopted, ensuring high reliability of the obtained results. The application of fuzzy set theory, as presented in the article, allows a mathematically correct solution to the optimization of the utility function and the determination of the possibilities and probabilities of risk realization. The damage resulting from risk realization is represented according to the composition and parameters accepted in the energy sector. The comprehensive risk analysis of energy production presented in the article enables fuller application of generally accepted methods of functional and cost analysis of complex technical systems at the main stages of their life cycle. The proposed technologies form the scientific and methodological foundation for neural network programming, machine learning, and predictive analysis in the creation and operation of small nuclear power plants using elements of artificial intelligence.

1. Mishcheryakov S. V., Grabchak E. P., Afanasyev V. Ya. Assessment of the technical condition of power equipment in the digital economy. // Reliability and safety of energy 2017; (10) 4: 268 – 275.

2. Mishcheryakov S. V., Afanasyev V. Ya., Grabchak E. P. Modern control technologies in the electric power industry. Moscow: De'libri 2021: 360.

3. Mishcheryakov S. V. Quantitative assessment of the quality of decisions taken to manage equipment at the stages of its Life Cycle. Moscow, Plekhanov Russian University of Economics 2024: 387.

4. Managing the economic efficiency of production technologies S. V. Mishcheryakov, V. Ya. Afanasyev, N. G. Lyubimova, V. F. Ukolov, Moscow, Scientific Library 2024: 342.

5. Grabchak E. P. Organizational and economic mechanism for managing the technical and economic efficiency of the Unified Energy System of Russia, Moscow, IPR RAS 2018: 173.

6. Decree of the Government of the Russian Federation dated December 19, 2016 №. 1401, base.garant.ru.

7. Order of the Ministry of Energy of the Russian Federation dated July 26, 2017 №. 676 "On approval of the methodology for assessing the technical condition of the main technological equipment and power transmission lines of electric power plants and electric networks", base. garant.ru.

8. Federal Law No. 35-FZ of March 26, 2003 "On the Electric Power Industry" (with amendments and additions), base.garant.ru.

9. Order of the Ministry of Energy of the Russian Federation № 1062 dated November 27, 2020 "On Approval of the Procedure for the Creation and Use of fuel Reserves by Thermal Power Plants, including during the heating season", base.garant.ru.

10. Bobrov A. F., Sedin A. A., Shcheblanov V. Yu. Functional reliability of a worker in the safety management system for the operation of nuclear energy facilities. Medical radiology and radiation safety 2021; (66) 3: 5 – 8.

11. Saati T. L. Decision-making in case of dependencies and feedbacks, monografi, M. LKI, 2008: 360.

12. Zhao Dong. Development of the corporate monitoring system in energy companies of the Russian Federation, Plekhanov Russian University of Economics 2019: 157.