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Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping Algorithm

Author

Listed:
  • Arun Onlam

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Daranpob Yodphet

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Rongrit Chatthaworn

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Chayada Surawanitkun

    (Faculty of Applied Science and Engineering, Khon Kaen University, Nong Khai Campus, Nong Khai 43000, Thailand)

  • Apirat Siritaratiwat

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Pirat Khunkitti

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

This paper proposes a novel adaptive optimization algorithm to solve the network reconfiguration and distributed generation (DG) placement problems with objective functions including power loss minimization and voltage stability index (VSI) improvement. The proposed technique called Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) was performed for solving network reconfiguration and DG installation in IEEE 33- and 69-bus distribution systems with seven different scenarios. The performance of ASFLA was compared to that of other algorithms such as Fireworks Algorithm (FWA), Adaptive Cuckoo Search Algorithm (ACSA) and Shuffled Frogs Leaping Algorithm (SFLA). It was found that the power loss and VSI provided by ASFLA were better than those given by FWA, ACSA and SFLA in both 33- and 69-bus systems. The best solution of power loss reduction and VSI improvement of both 33- and 69-bus systems was achieved when the network reconfiguration with optimal sizing and the location DG were simultaneously implemented. From our analysis, it was indicated that the ASFLA could provide better solutions than other methods since the generating process, local and global searching of this algorithm were significantly improved from a conventional method. Hence, the ASFLA becomes another effective algorithm for solving network reconfiguration and DG placement problems in electrical distribution systems.

Suggested Citation

  • Arun Onlam & Daranpob Yodphet & Rongrit Chatthaworn & Chayada Surawanitkun & Apirat Siritaratiwat & Pirat Khunkitti, 2019. "Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping," Energies, MDPI, vol. 12(3), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:553-:d:204774
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    References listed on IDEAS

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    3. Sultana, Beenish & Mustafa, M.W. & Sultana, U. & Bhatti, Abdul Rauf, 2016. "Review on reliability improvement and power loss reduction in distribution system via network reconfiguration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 297-310.
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    3. Panyawoot Boonluk & Sirote Khunkitti & Pradit Fuangfoo & Apirat Siritaratiwat, 2021. "Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand," Energies, MDPI, vol. 14(5), pages 1-20, March.
    4. Nien-Che Yang & Yan-Lin Zeng & Tsai-Hsiang Chen, 2021. "Assessment of Voltage Imbalance Improvement and Power Loss Reduction in Residential Distribution Systems in Taiwan," Mathematics, MDPI, vol. 9(24), pages 1-17, December.
    5. Nasreddine Belbachir & Mohamed Zellagui & Samir Settoul & Claude Ziad El-Bayeh & Ragab A. El-Sehiemy, 2023. "Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Al," Energies, MDPI, vol. 16(4), pages 1-24, February.
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    7. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    8. S. Angalaeswari & P. Sanjeevikumar & K. Jamuna & Zbigniew Leonowicz, 2020. "Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation," Sustainability, MDPI, vol. 12(14), pages 1-21, July.
    9. Eshan Karunarathne & Jagadeesh Pasupuleti & Janaka Ekanayake & Dilini Almeida, 2020. "Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm," Energies, MDPI, vol. 13(23), pages 1-25, November.
    10. Shanghua Guo & Jian Lin & Yuming Zhao & Longjun Wang & Gang Wang & Guowei Liu, 2020. "A Reliability-Based Network Reconfiguration Model in Distribution System with DGs and ESSs Using Mixed-Integer Programming," Energies, MDPI, vol. 13(5), pages 1-15, March.
    11. Qi Wang & Dasong Sun & Jianxiong Hu & Yi Wu & Ji Zhou & Yi Tang, 2019. "Risk Assessment Method for Integrated Transmission–Distribution System Considering the Reactive Power Regulation Capability of DGs," Energies, MDPI, vol. 12(16), pages 1-14, August.
    12. Habib Ur Rehman & Arif Hussain & Waseem Haider & Sayyed Ahmad Ali & Syed Ali Abbas Kazmi & Muhammad Huzaifa, 2023. "Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models," Energies, MDPI, vol. 16(5), pages 1-38, March.
    13. Ayşe Aybike Şeker & Tuba Gözel & Mehmet Hakan Hocaoğlu, 2021. "BIBC Matrix Modification for Network Topology Changes: Reconfiguration Problem Implementation," Energies, MDPI, vol. 14(10), pages 1-16, May.

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