IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i10p2238-d1389295.html

Optimal Planning of PV Sources and D-STATCOM Devices with Network Reconfiguration Employing Modified Ant Lion Optimizer

Author

Listed:
  • Sujatha B. C.

    (Electrical and Electronics Engineering, BMSCE, Visvesvaraya Technological University, Belagavi 590018, Karnataka, India)

  • Usha A.

    (Electrical and Electronics Engineering, BMSCE, Visvesvaraya Technological University, Belagavi 590018, Karnataka, India)

  • Geetha R. S.

    (Electrical and Electronics Engineering, BMSCE, Visvesvaraya Technological University, Belagavi 590018, Karnataka, India)

Abstract

This research emphasizes a meta-heuristic modified ant lion optimizer (MALO) optimization approach for the simultaneous utilization of DSTATCOM devices and distributed photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration, the majority of the research is based on constant power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the DSTATCOM device’s optimal size and position. The effect of the constant power (CP) and polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration is examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm is verified on 118-node test systems. The investigation is carried out for planning network upgrading to a high-voltage distribution system (HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited (BESCOM) radial distribution scheme. The simulated results obtained with this method are validated with the BAT algorithm and techniques available in the literature. It is observed that in the IEEE 118-bus system, via the simultaneous placement and sizing of PV sources considering a 25% penetration level and DSTATCOM devices during network reconfiguration, the total power loss reduction is 41.47% and 42.98% for the constant power model and ZIP with the load growth model. For the 317-bus system, the total power loss reduction observed for 11 kV is 49.77% and 59.34% for the constant power model and ZIP model with load growth. Similarly, for the 22 kV system, the power loss reduction observed is 51.69% and 55.75% for the constant power model and ZIP with the load growth model.

Suggested Citation

  • Sujatha B. C. & Usha A. & Geetha R. S., 2024. "Optimal Planning of PV Sources and D-STATCOM Devices with Network Reconfiguration Employing Modified Ant Lion Optimizer," Energies, MDPI, vol. 17(10), pages 1-25, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2238-:d:1389295
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/10/2238/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/10/2238/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben Hamida, Imen & Salah, Saoussen Brini & Msahli, Faouzi & Mimouni, Mohamed Faouzi, 2018. "Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs," Renewable Energy, Elsevier, vol. 121(C), pages 66-80.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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 Algorithm," Energies, MDPI, vol. 16(4), pages 1-24, February.
    2. Manuel S. Alvarez-Alvarado & Johnny Rengifo & Rommel M. Gallegos-Núñez & José G. Rivera-Mora & Holguer H. Noriega & Washington Velasquez & Daniel L. Donaldson & Carlos D. Rodríguez-Gallegos, 2022. "Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation," Energies, MDPI, vol. 15(22), pages 1-12, November.
    3. 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.
    4. Fatma Yaprakdal & Mustafa Baysal & Amjad Anvari-Moghaddam, 2019. "Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources," Energies, MDPI, vol. 12(10), pages 1-17, May.
    5. Ali, Aamir & Bughio, Ateeq-u-Rehman & Abbas, Ghulam & Keerio, M.U. & Mugheri, N.H. & Memon, Shaina & Saand, A.S., 2024. "Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm," Energy, Elsevier, vol. 311(C).
    6. Thai Dinh Pham & Thang Trung Nguyen & Le Chi Kien, 2022. "An Improved Equilibrium Optimizer for Optimal Placement of Distributed Generators in Distribution Systems considering Harmonic Distortion Limits," Complexity, John Wiley & Sons, vol. 2022(1).
    7. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    8. Xiao, Hao & Pei, Wei & Deng, Wei & Ma, Tengfei & Zhang, Shizhong & Kong, Li, 2021. "Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection," Applied Energy, Elsevier, vol. 284(C).
    9. Mahmoud M. Sayed & Mohamed Y. Mahdy & Shady H. E. Abdel Aleem & Hosam K. M. Youssef & Tarek A. Boghdady, 2022. "Simultaneous Distribution Network Reconfiguration and Optimal Allocation of Renewable-Based Distributed Generators and Shunt Capacitors under Uncertain Conditions," Energies, MDPI, vol. 15(6), pages 1-27, March.
    10. O. D. Montoya & W. Gil-González & J. C. Hernández & D. A. Giral-Ramírez & A. Medina-Quesada, 2020. "A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders," Energies, MDPI, vol. 13(17), pages 1-22, August.
    11. Teketay Mulu Beza & Yen-Chih Huang & Cheng-Chien Kuo, 2020. "A Hybrid Optimization Approach for Power Loss Reduction and DG Penetration Level Increment in Electrical Distribution Network," Energies, MDPI, vol. 13(22), pages 1-17, November.
    12. Hossein Azarinfar & Mohsen Khosravi & Kiomars Sabzevari & Maciej Dzikuć, 2024. "Stochastic Economic–Resilience Management of Combined Cooling, Heat, and Power-Based Microgrids in a Multi-Objective Approach," Sustainability, MDPI, vol. 16(3), pages 1-27, January.
    13. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    14. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    15. Kola Sampangi Sambaiah & T. Jayabarathi, 2020. "Optimal Reconfiguration of Distribution Network in Presence of D-STATCOM and Photovoltaic Array using a Metaheuristic Algorithm," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(5), September.
    16. 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.
    17. Ahmadi, Bahman & Ceylan, Oguzhan & Ozdemir, Aydogan & Fotuhi-Firuzabad, Mahmoud, 2022. "A multi-objective framework for distributed energy resources planning and storage management," Applied Energy, Elsevier, vol. 314(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2238-:d:1389295. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.