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Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices

Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices

Susanta Dutta, Provas Kumar Roy, Debashis Nandi
Copyright: © 2014 |Volume: 3 |Issue: 2 |Pages: 21
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781466654082|DOI: 10.4018/ijeoe.2014040103
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MLA

Dutta, Susanta, et al. "Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices." IJEOE vol.3, no.2 2014: pp.38-58. http://doi.org/10.4018/ijeoe.2014040103

APA

Dutta, S., Roy, P. K., & Nandi, D. (2014). Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices. International Journal of Energy Optimization and Engineering (IJEOE), 3(2), 38-58. http://doi.org/10.4018/ijeoe.2014040103

Chicago

Dutta, Susanta, Provas Kumar Roy, and Debashis Nandi. "Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices," International Journal of Energy Optimization and Engineering (IJEOE) 3, no.2: 38-58. http://doi.org/10.4018/ijeoe.2014040103

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Abstract

This paper illustrates, for the first time, the use of artificial bee colony optimization (ABC) technique to study optimal reactive power dispatch (ORPD) in power system with the use of flexible AC transmission systems (FACTS). FACTS controller cannot only increase the power transmission capacity without installing new transmission lines, but they can also enhance voltage profile and reduce transmission loss in power system. Two types of FACTS devices namely, thyristor-controlled series capacitor (TCSC) and thyristor-controlled phase shifter (TCPS) are considered in this paper. A standard IEEE 30-bus test system with multiple TCSC and TCPS devices is used for two different objective functions to validate the performance of the proposed method. The simulation results demonstrates the ability of the ABC to produce better optimal solutions compared to particle swarm optimization with inertia weight approach (PSOIWA) and real coded genetic algorithm (RGA).

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