Smart grid resources allocation using smart genetic heuristic

Abderezak Touzene*, Sultan Al Yahyai, Farid Melgani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this paper, we propose a new smart genetic algorithm SGA-SG which allows Smart Grid Constituencies (SGC) such as Power Generators, Power Distributers, and Power Consumers to optimise their pay-offs. The proposed resource allocation algorithm connects real time power consumers to the best power distributers in terms of cost. SGA-SG algorithm is using the concept of genetic algorithm, smartly guided towards the solution by reducing the random walk effect of the classical genetic algorithm. Usually, smart grid systems are large scale systems (millions of customers). Hence, the design of the proposed SGA-SG algorithm takes into consideration the scale of the system in terms of memory and speed requirements to produce a good quality allocation within a reasonable time. SGA-SG algorithm is designed to quickly respond to any power failure on a real-time basis. Experimental results show that SGA-SG algorithm gives near optimal solution and reduces by 20% the overall cost of the smart grid constituencies compared to the traditional grid system.

Original languageEnglish
Pages (from-to)125-134
Number of pages10
JournalInternational Journal of Computer Applications in Technology
Issue number1-2
Publication statusPublished - 2020


  • Optimisation
  • Resource allocation
  • Smart genetic algorithm
  • Smart grid

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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