Determination of optimal site and capacity of DG systems in distribution network based on genetic algorithm

Abdul-Wahab Alhadi Al-Hamali, Mohamed Emad Farrag, Geraint Bevan, Donald M. Hepburn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)
238 Downloads (Pure)


Concerns over global climate changes coupled with growing demand for energy are leading to increased penetration of distributed generation from intermittent sources into low voltage networks. In such cases distribution network (DN) operation will be affected. Consequently, there have been serious concerns over reliability and satisfactory operation of these power systems which contain distributed generation (DG) equipment. Distributed power generated from renewable sources is variable particularly in the case of wind generation or solar energy. The variability affects the stability of the system between supply and consumers.
In DN, the losses and voltage drop across the network are significant matters and the DG location has a critical impact on the network operation. So, there is a clear need to optimise the DG size and location in the DN; for example, optimising the number of DG’s and co-ordinating their operation can improve voltage drop and network losses.
In this paper, an optimisation technique based on the genetic algorithm (GA) in conjunction with the power flow (PF) method is used to improve the DN performance and to identify the best location and size of the DG’s. The main goal of the optimisation function is to reduce both the network losses and regulate the voltage level under different loading conditions.
Original languageEnglish
Title of host publication52nd International Universities Power Engineering Conference (UPEC), 2017
Number of pages6
ISBN (Electronic)9781538623442
ISBN (Print)9781538623442
Publication statusPublished - 21 Dec 2017


  • distributed generation (DG)
  • distribution systems
  • optimal DG allocation and genetic algorithms (GA)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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