Authors: N.V. Zavyalov, I.N. Paskar, G.M. Lebedev

Title of the article: Application of the digital (Z-) forecasting method when normalizing the technocenosis of the regional power system

Year: 2023, Issue: 5, Pages: 22-30

Branch of knowledge: 2.4.2. Electrotechnical complexes and systems (engineering)

Index UDK: 621.316

DOI: 10.26730/1816-4528-2023-5-22-30

Abstract: In this article, a technocenological approach to the study of the existing energy system and the accompanying Zipfian (Z-) forecasting method are implemented and applied in practice in order to find weak points and evaluate the implementation of optimization impacts aimed at increasing the reliability of power supply, reducing operating costs and increasing economic indicators. The article describes in detail the mathematical algorithm for implementing the study, collected and examined statistical data of the functioning Kuzbass energy system, and made a forecast of the behavior of electrical parameters for the long-term period. The data obtained during the study will form the basis for further research into the functioning energy system of the largest coal-mining region of the Russian Federation. The study noted that due to growing production on the territory of a constituent entity of the Russian Federation, the shortage of electricity in the energy system will grow. One of the promising sectors of the economy in the territory is the transport industry, which has the most pronounced indicators and is connected with the mining industry. The study was conducted based on statistical data from the System Operator of the Unified Energy System (hereinafter referred to as SO UES), data from the Federal State Statistics Service, as well as officially published current documents of the Government of the Russian Federation, schemes and a program for the long-term development of the electric power industry (hereinafter referred to as SIPRE) of the Kemerovo region - Kuzbass.

Key words: technocenosis power system optimization energy efficiency mathematical analysis forecasting

Receiving date: 29.10.2023

Approval date: 30.11.2023

Publication date: 19.12.2023

CITE DOWNLOAD