The main focus of this paper is on the application of genetic algorithm (GA) to search for an optimal solution to a realistically formulated economic dispatch (ED) problem. GA is a global search technique based on principles inspired from the genetic and evolution mechanism observed in natural biological systems. A major drawback of the conventional GA (CGA) approach is that it can be time consuming. The micro-GA (µGA) approach has been proposed as a better time efficient alternative for some engineering problems. The effectiveness of CGA and µGA. to solving ED problem is initially verified on an IEEE 3-generating unit, 6-bus test system. Simulation results obtained on this network using CGA and µGA validate their effectiveness when compared with the published results obtained via the classical and the Hopfield neural network approaches. Finally, both GA approaches have been successfully applied to the coordination of the Nigerian 31-bus system fed by four thermal and three hydro generating units. Herein, use has been made of the loss formula developed for the Nigerian system from several power flow studies. For the Nigerian case study, the µGA. is shown to exhibit superior performance than the CGA from both optimal generation allocations and computational time viewpoints.

Meeting Name

IEEE Power Engineering Society General Meeting, 2005


Electrical and Computer Engineering

Keywords and Phrases

6-Bus Test System; Hopfield Neural Network; IEEE 3-Generating Unit; Nigerian Thermal Power Plants; Economic Dispatch; Evolution Mechanism; Genetic Algorithms; Global Search Technique; Hydro Generating Units; Optimal Generation Allocations; Power Generation Dispatch; Power Generation Economics; Thermal Power Stations

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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