IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3112-d562925.html
   My bibliography  Save this article

Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization

Author

Listed:
  • Donghyeon Lee

    (Electrical Engineering, Inha University, Incheon 22212, Korea)

  • Seungwan Son

    (Electrical Engineering, Inha University, Incheon 22212, Korea)

  • Insu Kim

    (Electrical Engineering, Inha University, Incheon 22212, Korea)

Abstract

Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG.

Suggested Citation

  • Donghyeon Lee & Seungwan Son & Insu Kim, 2021. "Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization," Energies, MDPI, vol. 14(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3112-:d:562925
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3112/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/11/3112/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, Insu, 2018. "Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method," Energy, Elsevier, vol. 150(C), pages 642-652.
    2. Ana Cabrera-Tobar & Eduard Bullich-Massagué & Mònica Aragüés-Peñalba & Oriol Gomis-Bellmunt, 2019. "Active and Reactive Power Control of a PV Generator for Grid Code Compliance," Energies, MDPI, vol. 12(20), pages 1-25, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mengjun Liao & Lin Zhu & Yonghao Hu & Yang Liu & Yue Wu & Leke Chen, 2023. "Dynamic Equivalent Modeling of a Large Renewable Power Plant Using a Data-Driven Degree of Similarity Method," Energies, MDPI, vol. 16(19), pages 1-20, October.
    2. Jaemin Park & Haesung Jo & Insu Kim, 2021. "The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers," Energies, MDPI, vol. 14(18), pages 1-25, September.
    3. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    4. Qianlong Zhu & Jun Tao & Tianbai Deng & Mingxing Zhu, 2022. "A General Equivalent Modeling Method for DFIG Wind Farms Based on Data-Driven Modeling," Energies, MDPI, vol. 15(19), pages 1-14, September.

    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. Merad, Faycel & Labar, Hocine & Samira KELAIAIA, Mounia & Necaibia, Salah & Djelailia, Okba, 2019. "A maximum power control based on flexible collector applied to concentrator solar power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 315-331.
    2. Hrvoje Bulat & Dubravko Franković & Saša Vlahinić, 2021. "Enhanced Contingency Analysis—A Power System Operator Tool," Energies, MDPI, vol. 14(4), pages 1-21, February.
    3. Dong Zhang & GM Shafiullah & Choton Kanti Das & Kok Wai Wong, 2023. "Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks," Energies, MDPI, vol. 16(20), pages 1-35, October.
    4. Barukčić, M. & Hederić, Ž. & Hadžiselimović, M. & Seme, S., 2018. "A simple stochastic method for modelling the uncertainty of photovoltaic power production based on measured data," Energy, Elsevier, vol. 165(PB), pages 246-256.
    5. Wang, Chutong & Zhang, Xiaoyan & Wang, Yucui & Xiong, Houbo & Ding, Xi & Guo, Chuangxin, 2023. "Pricing method of electric-thermal heterogeneous shared energy storage service," Energy, Elsevier, vol. 281(C).
    6. Jaemin Park & Haesung Jo & Insu Kim, 2021. "The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers," Energies, MDPI, vol. 14(18), pages 1-25, September.
    7. Mahdavi, Sajad & Hemmati, Reza & Jirdehi, Mehdi Ahmadi, 2018. "Two-level planning for coordination of energy storage systems and wind-solar-diesel units in active distribution networks," Energy, Elsevier, vol. 151(C), pages 954-965.
    8. Jose Miguel Riquelme-Dominguez & Sergio Martinez, 2020. "A Photovoltaic Power Curtailment Method for Operation on Both Sides of the Power-Voltage Curve," Energies, MDPI, vol. 13(15), pages 1-17, July.
    9. Joseph Oyekale & Mario Petrollese & Vittorio Tola & Giorgio Cau, 2020. "Impacts of Renewable Energy Resources on Effectiveness of Grid-Integrated Systems: Succinct Review of Current Challenges and Potential Solution Strategies," Energies, MDPI, vol. 13(18), pages 1-48, September.
    10. Mahmoud Aref & Almoataz Y. Abdelaziz & Zong Woo Geem & Junhee Hong & Farag K. Abo-Elyousr, 2023. "Oscillation Damping Neuro-Based Controllers Augmented Solar Energy Penetration Management of Power System Stability," Energies, MDPI, vol. 16(5), pages 1-21, March.
    11. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    12. Mohammadi Fathabad, Abolhassan & Cheng, Jianqiang & Pan, Kai & Yang, Boshi, 2023. "Asymptotically tight conic approximations for chance-constrained AC optimal power flow," European Journal of Operational Research, Elsevier, vol. 305(2), pages 738-753.

    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:14:y:2021:i:11:p:3112-:d:562925. 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 (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.