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Optimized Network Reconfiguration with Integrated Generation Using Tangent Golden Flower Algorithm

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  • Dhivya Swaminathan

    (School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Arul Rajagopalan

    (School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

Abstract

The importance of integrating distributed generation (DG) units into the distribution network (DN) recently developed. To decrease power losses (PL), this article presents a meta-heuristic population-based tangent golden flower pollination algorithm (TGFPA) as an optimization technique for selecting the ideal site for DG. Furthermore, the proposed algorithm also finds the optimal routing configuration for power flow. TGFPA requires very few tuning parameters and is comprised of a golden section and a tangent flight algorithm (TFA). Hence, it is easy to update these parameters to obtain the best values, which provide highly reliable results compared to other existing techniques. In different case studies, the TGFPA’s performance was assessed on four test bus systems: IEEE 33-bus, IEEE 69-bus, IEEE 119-bus, and Indian-52 bus. According to simulation results, TGFPA computes the optimal reconfigured DN embedded along with DG, achieving the goal of minimal power loss.

Suggested Citation

  • Dhivya Swaminathan & Arul Rajagopalan, 2022. "Optimized Network Reconfiguration with Integrated Generation Using Tangent Golden Flower Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8158-:d:960373
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    References listed on IDEAS

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    1. Sangeeta Pant & Anuj Kumar & Mangey Ram, 2017. "Flower pollination algorithm development: a state of art review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1858-1866, November.
    2. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    3. Dalia Yousri & Thanikanti Sudhakar Babu & Dalia Allam & Vigna. K. Ramachandaramurthy & Eman Beshr & Magdy. B. Eteiba, 2019. "Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-27, September.
    4. Das, Sangeeta & Das, Debapriya & Patra, Amit, 2019. "Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
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    Citations

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    Cited by:

    1. Ardiaty Arief & Muhammad Bachtiar Nappu, 2023. "Novel Hybrid Modified Modal Analysis and Continuation Power Flow Method for Unity Power Factor DER Placement," Energies, MDPI, vol. 16(4), pages 1-18, February.
    2. Ola Badran & Jafar Jallad, 2023. "Multi-Objective Decision Approach for Optimal Real-Time Switching Sequence of Network Reconfiguration Realizing Maximum Load Capacity," Energies, MDPI, vol. 16(19), pages 1-32, September.
    3. Dhivya Swaminathan & Arul Rajagopalan & Oscar Danilo Montoya & Savitha Arul & Luis Fernando Grisales-Noreña, 2023. "Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution," Energies, MDPI, vol. 16(5), pages 1-24, March.
    4. Rasheed Abdulkader & Hayder M. A. Ghanimi & Pankaj Dadheech & Meshal Alharbi & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly & Dhivya Swaminathan & Sudhakar Sengan, 2023. "Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning," Energies, MDPI, vol. 16(6), pages 1-24, March.

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