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JOURNALS // Upravlenie Bol'shimi Sistemami // Archive

UBS, 2019 Issue 78, Pages 221–234 (Mi ubs998)

This article is cited in 1 paper

Reliability and Diagnostics of Control Systems and Tools

Models and methods for reliability analysis of the energy supply of remote settlements

E. Gubiy, V. I. Zorkal'tsev

Melentiev Energy Systems Institute SB RAS, Irkutsk

Abstract: The mathematical models for the reliability analysis of energy supply of remote settlements are considered. The three-level complex of nested models is proposed. The lower level represents the model of functioning of the energy supply system of a remote settlement during a unit of time. The second level is a model of energy supply reliability analysis. This analysis is based on a multiple-fold imitation of the functioning of the energy supply system in randomly formed conditions. The values of energy demand and energy production, as well as the values of carryover energy reserves in storage devices are considered as random. The values of the demand and energy production for the imitations of functioning are formed by the Monte Carlo method from the given laws of probability of these quantities. The random value of the carryover energy reserves is formed by an algorithm that generates a Markov sequence of these reserves. The upper level represents the model for selecting the optimal composition of the means of ensuring reliability (power reserves in energy production and the capacity of energy storages). The mathematical expectation of the sum of the reduced costs for the operation of the energy supply system and the losses from the deficit is minimized. The values of such an objective function for a given means of ensuring reliability are determined as a result of a cycle of calculations on the model of reliability analysis. The results of studies of the reliability of biofuel supply from the energy plantation to a remote settlement, in the natural-climatic conditions of the coastal of the Baikal Lake are presented.

Keywords: energy resource reserves, Monte Carlo method, power reserves, power supply reliability, random process.

UDC: 51.74
BBK: 22.1

Received: February 6, 2019
Published: March 31, 2019

DOI: 10.25728/ubs.2019.78.9



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