Reliability Enhancement of LV Rural Networks using Smart Grid Technologies

Maximilian L Ellery, Mike Brian Ndawula, Ignacio Hernando Gil

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

3 Citations (SciVal)


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 languageEnglish
Title of host publication2019 International Conference on Smart Energy Systems and Technologies (SEST)
Number of pages5
ISBN (Electronic)978-1-7281-1156-8
ISBN (Print)978-1-7281-1157-5
Publication statusPublished - 26 Sept 2019

Publication series

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


  • 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


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