Reliability Enhancement of LV Rural Networks using Smart Grid Technologies

Maximilian L Ellery; Mike Brian Ndawula; Ignacio Hernando Gil

doi:10.1109/SEST.2019.8849045

Reliability Enhancement of LV Rural Networks using Smart Grid Technologies

Maximilian L Ellery, Mike Brian Ndawula, Ignacio Hernando Gil

Department of Electronic & Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper analyses the effect of new smart grid technologies (SGTs) on the reliability indices typically specified by distribution network operators in low-voltage rural distribution systems. Rural areas generally denoted as “thinly-populated”, are to a large extent neglected in the anticipated transformation of existing networks into the future smart grid. An innovative Monte Carlo simulation technique is refined in this analysis to model the stochastic failure rates of power components over a specific time period, which are then applied to network load flow analysis to assess the quality of supply enhancement of a modelled rural distribution network. The proposed method enables much faster and more refined reliability studies, allowing for larger data sets to capture the inherent uncertainty from the new SGTs. Simulation results providing base case reliability indices, and the addition of SGTs accumulated from models in previous works, provide scenarios used for comparison into SGT-effectiveness.

Original language	English
Title of host publication	2019 International Conference on Smart Energy Systems and Technologies (SEST)
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	978-1-7281-1156-8
ISBN (Print)	978-1-7281-1157-5
DOIs	https://doi.org/10.1109/SEST.2019.8849045
Publication status	Published - 26 Sep 2019

Publication series

Name	SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies

Keywords

Load modeling
Reliability
Smart grids
Mathematical model
Probability density function ,
Solar power generation
Analytical models
rural distribution network
renewable resources ,
Monte Carlo Simulation
smart grid
renewable resources
reliability performance
monte carlo simulation

ASJC Scopus subject areas

Artificial Intelligence
Control and Optimization
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Computer Networks and Communications
Renewable Energy, Sustainability and the Environment

Access to Document

10.1109/SEST.2019.8849045

Cite this

Ellery, M. L., Ndawula, M. B., & Hernando Gil, I. (2019). Reliability Enhancement of LV Rural Networks using Smart Grid Technologies. In 2019 International Conference on Smart Energy Systems and Technologies (SEST) (pp. 1-5). (SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies). IEEE. https://doi.org/10.1109/SEST.2019.8849045

Reliability Enhancement of LV Rural Networks using Smart Grid Technologies. / Ellery, Maximilian L; Ndawula, Mike Brian; Hernando Gil, Ignacio.

2019 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2019. p. 1-5 (SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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abstract = "This paper analyses the effect of new smart grid technologies (SGTs) on the reliability indices typically specified by distribution network operators in low-voltage rural distribution systems. Rural areas generally denoted as “thinly-populated”, are to a large extent neglected in the anticipated transformation of existing networks into the future smart grid. An innovative Monte Carlo simulation technique is refined in this analysis to model the stochastic failure rates of power components over a specific time period, which are then applied to network load flow analysis to assess the quality of supply enhancement of a modelled rural distribution network. The proposed method enables much faster and more refined reliability studies, allowing for larger data sets to capture the inherent uncertainty from the new SGTs. Simulation results providing base case reliability indices, and the addition of SGTs accumulated from models in previous works, provide scenarios used for comparison into SGT-effectiveness.",

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