A Novel Improved HBMO Algorithm Regarding Generation Expansion Planning in Deregulated Energy Networks

Document Type : Research paper

Authors

1 Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences

2 2 Department of Medical Electronics, Ramaiah Institute of Technology, Bengaluru, E-mail: mahendra.j@msrit.edu

3 Department of Occupational Therapy, Faculty of Associated Medical Sciences

4 Department of Mechanical Engineering, Universitas Muhammadiyah Kalimantan Timur, Samarinda, Indonesia

5 College of Dentistry, Al-Ayen University, Thi-Qar, Iraq

6 Department of Pharmaceutical Chemistry, College of Pharmacy, University of Mosul, Mosul, Iraq

7 Faculty of Biology and Ecology, Yanka Kupala State University of Grodno, 230023 Grodno, Belarus

8 College of technical engineering, The Islamic University, Najaf, Iraq

9 Department of Dentistry, Kut University College, Kut, Wasit, 52001, Iraq

10 Al-Nisour University College, Baghdad, Iraq

11 Universitas Pamulang, Tangerang Selatan, Banten, Indonesia

12 Kazan Federal University, Russia

Abstract

Electric energy demand is increasing rapidly in developing countries, making the installation of additional generating units necessary. Private generating stations are encouraged to add new generations in deregulated energy networks. Planning for transmission expansion must ensure increased market competition while maintaining high levels of dependability and system operation safety. New objectives and demands have been made for the transmission expansion issue as a result of the deregulation of the energy network. This study has attempted to provide a new population-base algorithm; called Modified Honey Bee Mating Optimization (MHBMO) for expansion development in deregulated energy systems that are applied in multi-objective processes. In addition, to diminish the elaborateness of the issue the benders decomposition is used in this study which categorize the original issue into two subproblems. First maximizing the profits of each PBGEP (GENCO) and second, satisfying security network constraints (SCGEP). Therefore, using the suggested MHBMO algorithm, value of each GENCO's profit and overall profit could be obtained. To demonstrate the viability and capabilities of the suggested algorithm, the planning methodology has been evaluated using the IEEE 30-bus test system. The results of the current study served as an example of the effectiveness of the suggested methodology.

Keywords

References

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Journal of Operation and Automation in Power Engineering
Volume 11, Special Issue
Sustainable Power Systems, Energy Management, and Global Warming
March 2023

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History

  • Receive Date: 15 March 2022
  • Revise Date: 25 January 2023
  • Accept Date: 20 February 2023
  • First Publish Date: 20 February 2023

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