Beta
ManageSearchChatDiscover
23974

SOLUTION OF ECONOMIC DISPATCH PROBLEM USING EVOLUTIONARY PROGRAMMING TECHNIQUES

Article

Last updated: 04 Jan 2025

OverviewSimilar Items

Subjects

-

Tags

-

Abstract

This paper presents a comparison between various methods used to solve economic dispatch (ED) problem. These methods are heuristic methods such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) algorithm. Comparisons between these heuristic methods and conventional methods such as Interior Point method (IPM) are introduced. The objective function that consists of the fuel (generation) cost is minimized. Numerical examples typical to each method are presented. The solutions method adopted has yielded encouraging results that proved useful in the improvement of economic dispatch environment.

DOI

10.21608/asat.2007.23974

Keywords

Economic dispatch, Genetic Algorithms, Particle Swarm Optimization, and interior point method

Authors

First Name

F.

Last Name

Bendary

MiddleName

M.

Affiliation

-

Email

-

City

-

Orcid

-

First Name

A.

Last Name

Ishak

MiddleName

A.

Affiliation

-

Email

-

City

-

Orcid

-

First Name

M.

Last Name

Mosaad

MiddleName

I.

Affiliation

-

Email

-

City

-

Orcid

-

Volume

12

Article Issue

ASAT Conference, 29-31 May 2007

Related Issue

4431

Issue Date

2007-05-01

Receive Date

2019-01-09

Publish Date

2007-05-01

Page Start

1

Page End

10

Print ISSN

2090-0678

Online ISSN

2636-364X

Article File

ASAT_Volume 12_Issue ASAT Conference, 29-31 May 2007_Pages 1-10.pdf

PDF . 87.4kB

Link

https://asat.journals.ekb.eg/article_23974.html

Detail API

https://asat.journals.ekb.eg/service?article_code=23974

Order

55

Type

Original Article

Type Code

737

Publication Type

Journal

Publication Title

International Conference on Aerospace Sciences and Aviation Technology

Publication Link

https://asat.journals.ekb.eg/

MainTitle

SOLUTION OF ECONOMIC DISPATCH PROBLEM USING EVOLUTIONARY PROGRAMMING TECHNIQUES

Details

Type

Article

Created At

22 Jan 2023