JOURNAL ARTICLE

Genetic Algorithm For Solving Non-Convex Economic Dispatch Problem

Javidtash, NavidAbdolmohamad DavodiHakimzadeh, MojtabaAbdolreza Roozbeh

Year: 2014 Journal:   Zenodo (CERN European Organization for Nuclear Research)   Publisher: European Organization for Nuclear Research

Abstract

Economic dispatch (ED) is considered to be one of the
key functions in electric power system operation. This paper presents
a new hybrid approach based genetic algorithm (GA) to economic
dispatch problems. GA is most commonly used optimizing algorithm
predicated on principal of natural evolution. Utilization of chaotic
queue with GA generates several neighborhoods of near optimal
solutions to keep solution variation. It could avoid the search process
from becoming pre-mature. For the objective of chaotic queue
generation, utilization of tent equation as opposed to logistic equation
results in improvement of iterative speed. The results of the proposed
approach were compared in terms of fuel cost, with existing
differential evolution and other methods in literature.

Keywords:
Economic dispatch Genetic algorithm Key (lock) Electric power system Queue Iterative method Convergence (economics) Differential evolution

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Topics

Electric Power System Optimization
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Metaheuristic Optimization Algorithms Research
Physical Sciences →  Computer Science →  Artificial Intelligence
Optimal Power Flow Distribution
Physical Sciences →  Engineering →  Electrical and Electronic Engineering

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