IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i16p5974-d1217096.html
   My bibliography  Save this article

Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods

Author

Listed:
  • Cheng Yang

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Yupeng Sun

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Yujie Zou

    (Shanghai Zhabei Power Plant of State Grid Corporation of China, Shanghai 200432, China)

  • Fei Zheng

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Shuangyu Liu

    (Shanghai Guoyun Information Technology Co., Ltd., Shanghai 201210, China)

  • Bochao Zhao

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Ming Wu

    (China Electric Power Research Institute, State Grid Corporation of China, Beijing 100192, China)

  • Haoyang Cui

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

Abstract

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the “Dual-Carbon” target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations.

Suggested Citation

  • Cheng Yang & Yupeng Sun & Yujie Zou & Fei Zheng & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods," Energies, MDPI, vol. 16(16), pages 1-42, August.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5974-:d:1217096
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/16/5974/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/16/5974/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wentao Yang & Fushuan Wen & Ke Wang & Yuchun Huang & Md. Abdus Salam, 2018. "Modeling of a District Heating System and Optimal Heat-Power Flow," Energies, MDPI, vol. 11(4), pages 1-19, April.
    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. Alberto Flores & Rafael Zárate-Miñano & Miguel Carrión, 2023. "Capability Curve Modeling for Hydro-Power Generators in Optimal Power Flow Problems," Sustainability, MDPI, vol. 15(24), pages 1-9, December.

    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. Yu Huang & Kai Yang & Weiting Zhang & Kwang Y. Lee, 2018. "Hierarchical Energy Management for the MultiEnergy Carriers System with Different Interest Bodies," Energies, MDPI, vol. 11(10), pages 1-18, October.
    2. Ramon Abritta & Frederico Panoeiro & Leonardo Honório & Ivo Silva Junior & André Marcato & Anapaula Guimarães, 2020. "Hydroelectric Operation Optimization and Unexpected Spillage Indications," Energies, MDPI, vol. 13(20), pages 1-20, October.
    3. Dorota Anna Krawczyk & Tomasz Janusz Teleszewski, 2019. "Reduction of Heat Losses in a Pre-Insulated Network Located in Central Poland by Lowering the Operating Temperature of the Water and the Use of Egg-shaped Thermal Insulation: A Case Study," Energies, MDPI, vol. 12(11), pages 1-12, June.
    4. Dorota Anna Krawczyk & Tomasz Janusz Teleszewski, 2019. "Optimization of Geometric Parameters of Thermal Insulation of Pre-Insulated Double Pipes," Energies, MDPI, vol. 12(6), pages 1-11, March.
    5. Birol Kılkış & Malik Çağlar & Mert Şengül, 2021. "Energy Benefits of Heat Pipe Technology for Achieving 100% Renewable Heating and Cooling for Fifth-Generation, Low-Temperature District Heating Systems," Energies, MDPI, vol. 14(17), pages 1-54, August.
    6. Steinegger, Josef & Wallner, Stefan & Greiml, Matthias & Kienberger, Thomas, 2023. "A new quasi-dynamic load flow calculation for district heating networks," Energy, Elsevier, vol. 266(C).
    7. Tomasz Janusz Teleszewski & Dorota Anna Krawczyk & Antonio Rodero, 2019. "Reduction of Heat Losses Using Quadruple Heating Pre-Insulated Networks: A Case Study," Energies, MDPI, vol. 12(24), pages 1-12, December.

    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:16:y:2023:i:16:p:5974-:d:1217096. 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.