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Symbiotic Organism Search Algorithm for Optimal Size and Siting of Distributed Generators in Distribution Systems

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  • Tri Phuoc Nguyen

    (Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam)

  • Vo Ngoc Dieu

    (Department of Power Systems, Electronic Electrical Engineering Faculty, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam)

  • Pandian Vasant

    (Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia)

Abstract

This paper presents a new approach for solving optimal placement of distributed generation (OPDG) problem in distribution systems for minimizing active power loss. In this research, the loss sensitivity factor is used to identify the optimal locations for installation of DGs and symbiotic organisms search (SOS) is used to find the optimal size of DGs. The proposed SOS approach is defined as symbiotic relationships observed between two organisms in the ecosystem, which does not need control parameters like other meta-heuristic algorithms. The OPDG problem is considered with two different scenarios including Scenario I for DGs installed at candidate buses to supply only active power to the system and Scenario II for same as Scenario I except that DGs are controlled to supply both active and reactive powers at a 0.85 p.f. The effectiveness of the proposed SOS method has been verified on the IEEE 33-bus and 69-bus radial distribution systems. The result comparison from the test systems has indicated that the proposed SOS is effective to obtain the optimal solution for the OPDG problem.

Suggested Citation

  • Tri Phuoc Nguyen & Vo Ngoc Dieu & Pandian Vasant, 2017. "Symbiotic Organism Search Algorithm for Optimal Size and Siting of Distributed Generators in Distribution Systems," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 6(3), pages 1-28, July.
    • Handle: RePEc:igg:jeoe00:v:6:y:2017:i:3:p:1-28
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    Cited by:

    1. Cheng, Min-Yuan & Vu, Quoc-Tuan, 2024. "Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management," Energy, Elsevier, vol. 302(C).
    2. Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Edward-J. Marín-García & Carlos Andres Ramos-Paja & Alberto-Jesus Perea-Moreno, 2022. "Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO," Energies, MDPI, vol. 15(20), pages 1-20, October.
    3. David Steveen Guzmán-Romero & Brandon Cortés-Caicedo & Oscar Danilo Montoya, 2023. "Development of a MATLAB-GAMS Framework for Solving the Problem Regarding the Optimal Location and Sizing of PV Sources in Distribution Networks," Resources, MDPI, vol. 12(3), pages 1-19, March.

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