A genetic algorithm based economic dispatch (GAED) with environmental constraint optimisation

David J. King, Cuneyt Suheyl Ozveren, W. Warsono

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
31 Downloads (Pure)

Abstract

The role of renewable energy in power systems is becoming more significant due to the increasing cost of fossil fuels and climate change concerns. However, the inclusion of Renewable Energy Generators (REG), such as wind power, has created additional problems for power system operators due to the variability and lower predictability of output of most REGs, with the Economic Dispatch (ED) problem being particularly difficult to resolve. In previous papers we had reported on the inclusion of wind power in the ED calculations. The simulation had been performed using a system model with wind power as an intermittent source, and the results of the simulation have been compared to that of the Direct Search Method (DSM) for similar cases. In this paper we report on our continuing investigations into using Genetic Algorithms (GA) for ED for an independent power system with a significant amount of wind energy in its generator portfolio. The results demonstrate, in line with previous reports in the literature, the effectiveness of GA when measured against a benchmark technique such as DSM.
Original languageEnglish
Title of host publicationProceedings of 2011 46th International Universities' Power Engineering Conference (UPEC)
PublisherVDE Verlag GmbH
Number of pages6
ISBN (Print)9783800734023
Publication statusPublished - 2011
Event46th International Universities Power Engineering Conference - Soest, Germany
Duration: 5 Sep 20118 Sep 2011
Conference number: 46th

Conference

Conference46th International Universities Power Engineering Conference
Abbreviated titleUPEC
CountryGermany
CitySoest
Period5/09/118/09/11

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  • Cite this

    King, D. J., Ozveren, C. S., & Warsono, W. (2011). A genetic algorithm based economic dispatch (GAED) with environmental constraint optimisation. In Proceedings of 2011 46th International Universities' Power Engineering Conference (UPEC) VDE Verlag GmbH.