Generation Expansion Planning in the Presence of Wind Power Plants Using a Genetic Algorithm Model

Journal article

Sahragard, A., Falaghi, H., Farhadi, M., Mosavi, A. and Estebsari, A. (2020). Generation Expansion Planning in the Presence of Wind Power Plants Using a Genetic Algorithm Model. Electronics (Switzerland). 9 (1143), p. 1143. https://doi.org/10.3390/electronics9071143
AuthorsSahragard, A., Falaghi, H., Farhadi, M., Mosavi, A. and Estebsari, A.
Abstract

One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on a genetic algorithm (GA) for GEP in the presence of wind power plants. Since it is desirable to integrate the maximum possible wind power production in GEP, the constraints for incorporating different levels of wind energy in power generation are investigated comprehensively. This will allow the maximum reasonable amount of wind penetration in the network to be obtained. Besides, due to the existence of different wind regimes, the penetration of strong and weak wind on GEP is assessed. The results show that the maximum utilization of wind power generation capacity could increase the exploitation of more robust wind regimes. Considering the growth of the wind farm industry and the cost reduction for building wind power plants, the sensitivity of GEP to the variations of this cost is investigated. The results further indicate that for a 10% reduction in the initial investment cost of wind power plants, the proposed model estimates that the overall cost will be minimized.

Keywordsgeneration expansion planning; wind power; genetic algorithm; least-cost generation expansion planning; machine learning; renewable energies;
Year2020
JournalElectronics (Switzerland)
Journal citation9 (1143), p. 1143
PublisherMDPI
ISSN2079-9292
Digital Object Identifier (DOI)https://doi.org/10.3390/electronics9071143
Web address (URL)https://www.mdpi.com/2079-9292/9/7/1143
Publication dates
Online14 Jul 2020
Publication process dates
Accepted07 Jul 2020
Deposited16 Jul 2020
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Open
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