Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/10730
Title: Embedded immune-evolutionary programming for Economic Dispatch of generators with prohibited operating zones
Authors: Razali, N.S. 
Mansor, M.H. 
Musirin, I. 
Othman, M.M. 
Akoury, M. 
Issue Date: 2018
Abstract: Economic Dispatch (ED) is one of the popular power system problems. It is solved by the grid system operators (GSOs) to find the cheapest production cost for dispatching power from generations to the demands (loads). It is crucial to have cheap production cost and at the same time satisfying all system constraints such as generating units' operating limits, transmission lines capacity and spinning reserve. ED of generators with prohibited operating zones is considered as a nonconvex ED as it has inequality constraints that need to be considered. This inequality constraint is the operating limits of generating units that considering the allowable and prohibited operating zones. The generating units must be ensured, that they are only operated within the allowable limits to avoid problem with the equipments that related to them. Some generating units will experience high vibration at certain level of operating limits and will burn more fuel to maintain at certain level of load. This paper presents an embedded technique to solve ED of generators problem with prohibited operating zones. The cloning process of Artificial Immune System (AIS) is inserted into Evolutionary Programming (EP) algorithm to form a new technique termed as Embedded Immune-Evolutionary Programming (EIEP). The proposed embedded technique has been tested on the IEEE 26-Bus Reliability Test System (RTS) with three different conditions of load. Besides that the results produced by the embedded technique has been compared with two single techniques which are AIS and EP. It is found that, the results of ED of generators with prohibited operating zones produced by EIEP is better than the two single techniques in terms of low total production cost. © 2018 Authors.
URI: http://dspace.uniten.edu.my/jspui/handle/123456789/10730
DOI: 10.14419/ijet.v7i3.15.17526
Appears in Collections:CSIT Scholarly Publication

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