Network reconfiguration in balanced distribution systems with variable load demand and variable renewable resources generation

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15 Scopus citations

Abstract

There is increasing interest in renewable energy sources interconnections in distribution systems since they are inexhaustible and nonpolluting. Wind and photovoltaic are among most mature renewable energy sources, and their penetration continues to increase. This paper proposes a method for distribution network reconfiguration to minimize annual energy losses by determining the optimal configuration for each season of the year. Uncertainties including the daily time varying load and stochastic power generation of renewable distributed generators (DGs) are modeled and taken into account. The method is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DGs with their probabilities of occurrence, then accommodating this model in the reconfiguration problem. The reconfiguration problem, based on Genetic Algorithm, aims at achieving minimum annual energy losses. The constraints include the voltage limits, line current limits, radial topology, and feeding of all loads. The proposed method has been tested on two systems. Simulation results show significant reductions in the seasonal and annual energy losses for all the studied cases.

Original languageBritish English
Title of host publication2012 IEEE Power and Energy Society General Meeting, PES 2012
DOIs
StatePublished - 2012
Event2012 IEEE Power and Energy Society General Meeting, PES 2012 - San Diego, CA, United States
Duration: 22 Jul 201226 Jul 2012

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2012 IEEE Power and Energy Society General Meeting, PES 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period22/07/1226/07/12

Keywords

  • distributed generation
  • genetic algorithm
  • Network reconfiguration
  • photovoltaic
  • uncertainty
  • variable demand
  • wind power

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