Synergetic modelling of the Russian Federation’s energy system parameters

Damir A. Gaynanov, Olga G. Kantor, Ekaterina S. Kashirina

Abstract


The energy system in any country is the basis of the whole economy. The level of its development largely determines the quantity and quality of economic entities, periods of economic growth, fall and stagnation. A high percentage of the power-deficient municipalities in the Russian Federation shows the substantive issues in this sphere that carries a threat to the energy security of the state.

One of the promising trends for enhancing the energy security is the renewable energy sources (RES). Their use has the obvious benefits: it provides electricity to power-deficient and inaccessible areas, contributes to the introduction and spread of new technologies, thus solving the important social and economic problem. At that, it is important to determine the optimum ratio using of the recovery of renewable and conventional energy sources (CES). One of the main challenges in this regard is to build a model that adequately reflects the ratio of renewable and conventional energy sources in the Russian energy system.

The paper presents the results of a synergistic approach to the construction of such a model. The Lotka- Volterra model was the main instrument used, which allowed to study a behavior pattern of the considered systems on the basis of the simplified regularities. It was found that the best possible qualitative “jump” in the Russian energy sector was in 2008. The calculations allowed to investigate the behavior of the Russian energy system with the variation of the initial conditions and to assess the validity of the targets for the share of electricity produced through the use of renewable energy in the total electric power of the country.


Keywords


renewable energy sources; energy system; targets; synergetic approach

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References


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DOI: https://doi.org/10.15826/recon.2015.4.024

Copyright (c) 2018 Damir A. Gaynanov, Olga G. Kantor, Ekaterina S. Kashirina

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Online ISSN 2412-0731