A New Approach To The Solution Of Economic Dispatch Using Genetic Algorithm

Al-Shetwi, Ali Q. and Alomoush, Muwaffaq I. (2016) A New Approach To The Solution Of Economic Dispatch Using Genetic Algorithm. Journal of Engineering and Technology, 7 (1). pp. 40-48. ISSN 2180-3811 (print); 2289-814X (online). (Published)

fkee-2016-alshetwi-new approach to the solution of economic.pdf
Available under License Creative Commons Attribution.

Download (181kB) | Preview


Economic dispatch is the process of finding the optimal generation scheduling of a number of electricity generation facilities to meet the load of the system at lowest possible cost, subject to transmission and operational constraints on the system. The main idea of this paper focuses on the application of genetic algorithm in order to identify the best solution to an economic dispatch problem by using a new approach depending on Bmn coefficients and arithmetic crossover of the genetic algorithm. In this study, the proposed method solves the economic dispatch problem of three generator units whilst taking into consideration the coefficient losses to find the most important factors in electrical generation, which are the output power and total cost. The results of this study are compared with the classical optimization calculation techniques, and it is found that the results were almost equal. The MATLAB simulation is run to demonstrate clearly the effectiveness of the new genetic algorithm approach as a very important method in the solution of economic dispatch problems.

Item Type: Article
Uncontrolled Keywords: Economic Dispatch (ED); Genetic Algorithm (GA); power transmission losses; optimization
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Institute of Postgraduate Studies
Depositing User: Noorul Farina Arifin
Date Deposited: 21 Oct 2016 02:45
Last Modified: 18 Oct 2019 02:40
URI: http://umpir.ump.edu.my/id/eprint/14875
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item