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Economic load dispatch using memetic sine cosine algorithm.

Authors :
Al-Betar, Mohammed Azmi
Awadallah, Mohammed A.
Zitar, Raed Abu
Assaleh, Khaled
Source :
Journal of Ambient Intelligence & Humanized Computing; Sep2023, Vol. 14 Issue 9, p11685-11713, 29p
Publication Year :
2023

Abstract

In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. ELD is tackled by assigning a set of generation units with a minimum fuel costs to generate predefined load demand with accordance to a set of equality and inequality constraints. SCA is a recent population based optimizer turned towards the optimal solution using a mathematical-based model based on sine and cosine trigonometric functions. As other optimization methods, SCA has main shortcoming in exploitation process when a non-linear constraints problem like ELD is tackled. Therefore, β -hill climbing optimizer, a recent local search algorithm, is hybridized as a new operator in SCA to empower its exploitation capability to tackle ELD. The proposed hybrid algorithm is abbreviated as SCA- β HC which is evaluated using two sets of real-world generation cases: (i) 3-units, two versions of 13-units, and 40-units, with neglected Ramp Rate Limits and Prohibited Operating Zones constraints. (ii) 6-units and 15-units with Ramp Rate Limits and Prohibited Operating Zones constraints. The sensitivity analysis of the control parameters for SCA- β HC is initially studied. The results show that the performance of the SCA- β HC algorithm is increased by tuning its parameters in proper value. The comparative evaluation against several state-of-the-art methods show that the proposed method is able to produce new best results for some tested cases as well as the second-best for others. In a nutshell, hybridizing β HC optimizer as a new operator for SCA is very powerful algorithm for tackling ELD problems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18685137
Volume :
14
Issue :
9
Database :
Complementary Index
Journal :
Journal of Ambient Intelligence & Humanized Computing
Publication Type :
Academic Journal
Accession number :
166105581
Full Text :
https://doi.org/10.1007/s12652-022-03731-1