A non-linear-threshold-accepting function based algorithm for the solution of economic dispatch problem

Nabil Nahas, Mohamed Noomane Darghouth*, Mohammed Abouheaf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This article introduces a novel heuristic algorithm based on Non-Linear Threshold Accepting Function to solve the challenging non-convex economic dispatch problem. Economic dispatch is a power system management tool; it is used to allocate the total power generation to the generating units to meet the active load demand. The power systems are highly nonlinear due to the physical and operational constraints. The complexity of the resulting non-convex objective cost function led to inabilities to solve the problem by using analytical approaches, especially in the case of large-scale problems. Optimization techniques based on heuristics are used to overcome these difficulties. The Non-Linear Threshold Accepting Algorithm has demonstrated efficiency in solving various instances of static and dynamic allocation and scheduling problems but has never been applied to solve the economic dispatch problem. Existing benchmark systems are used to evaluate the performance of the proposed heuristic. Additional random instances with different sizes are generated to compare the adopted heuristic to the Harmony Search and the Whale Optimization Algorithms. The obtained results showed the superiority of the proposed algorithm in finding, for all considered instances, a high-quality solution in minimum computational time.

Original languageEnglish
Pages (from-to)1269-1289
Number of pages21
JournalRAIRO - Operations Research
Volume54
Issue number5
DOIs
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© EDP Sciences, ROADEF, SMAI 2020

Keywords

  • Constraints optimization
  • Economic dispatch
  • Meta-heuristics
  • Non-convex optimization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science Applications
  • Management Science and Operations Research

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