Fuzzy unit commitment for cost minimization in power system planning

N. A. Rahmat, I. Musirin, A. F. Abidin

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

1 Citation (Scopus)

Abstract

Unit commitment is among of the key elements in power system planning. Unit commitment is extensively applied by the power utilities to plan the optimal dispatch of generating units in the system. In the deregulated power system industry, it is important to consider several uncertainty constraints during the planning process. This research proposes the application of fuzzy set modeling to determine the uncertainty constraints. Several intelligence techniques including Particle Swarm Optimization, Ant Colony Optimization, and Differential Evolution Immunized Ant Colony Optimization approaches have been used to optimize the fuzzy unit commitment problem. The verification process was performed on IEEE 30-Bus Reliable Test System (RTS). Comparative studies among PSO, ACO and DEIANT indicate the superiority of DEIANT in solving the fuzzy unit commitment problem.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013
Pages680-685
Number of pages6
DOIs
Publication statusPublished - 28 Aug 2013
Event2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013 - Langkawi, Malaysia
Duration: 03 Jun 201304 Jun 2013

Publication series

NameProceedings of the 2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013

Other

Other2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013
CountryMalaysia
CityLangkawi
Period03/06/1304/06/13

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Fuel Technology

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  • Cite this

    Rahmat, N. A., Musirin, I., & Abidin, A. F. (2013). Fuzzy unit commitment for cost minimization in power system planning. In Proceedings of the 2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013 (pp. 680-685). [6564633] (Proceedings of the 2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013). https://doi.org/10.1109/PEOCO.2013.6564633