IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2555-d1052746.html
   My bibliography  Save this article

Steady-State Risk Prediction Analysis of Power System Based on Power Digital Twinning

Author

Listed:
  • Qiang Li

    (State Grid Information & Telecommunication Group Co., Ltd., Beijing 102211, China)

  • Feng Zhao

    (State Grid Information & Telecommunication Group Co., Ltd., Beijing 102211, China)

  • Li Zhuang

    (Fujian Yirong Information Technology Co., Ltd., Fuzhou 350001, China)

  • Qiulin Wang

    (Fujian Yirong Information Technology Co., Ltd., Fuzhou 350001, China)

  • Chenzhou Wu

    (Fujian Yirong Information Technology Co., Ltd., Fuzhou 350001, China)

Abstract

In essence, electric digital twinning uses artificial intelligence technology to model complex electric power systems, and is the development and supplement of electric power modeling technology. This paper intends to predict and analyze the steady-state risks of complex power systems based on the power digital twin. Firstly, power flow calculation and optimization are carried out for complex large power grid systems. Based on sparse matrix storage and node coding optimization, the power flow calculation speed is improved and the memory usage is reduced. The accuracy and timeliness of the continuous power flow calculation when obtaining the node power and voltage are improved by using the unit processing tangent prediction vector and the internal machine of the prediction vector to determine the prediction direction. Secondly, according to the optimization results of the power flow calculation, the multi-objective optimization problem of power system simulation is solved by using the advantages of neural network modeling, such as self-learning, self-adaptation, fault tolerance, and parallelism. Finally, the power flow calculation optimization and neural network analysis are applied to the integrated stability program of the United States Western Combined Power Grid (WSCC) power system’s nine-node model; this is in order to simulate the regional power grid for simulation analysis. Different risks in the power system under steady–state conditions are predicted and analyzed, the voltage drop in the transient voltage is reduced under multiple working conditions, and the relative power angle is improved, improving the overall stability of the power system.

Suggested Citation

  • Qiang Li & Feng Zhao & Li Zhuang & Qiulin Wang & Chenzhou Wu, 2023. "Steady-State Risk Prediction Analysis of Power System Based on Power Digital Twinning," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2555-:d:1052746
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2555/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2555/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ning Wang & Fei Zhao, 2020. "An Assessment of the Condition of Distribution Network Equipment Based on Large Data Fuzzy Decision-Making," Energies, MDPI, vol. 13(1), pages 1-13, January.
    2. Janko, Samantha & Johnson, Nathan G., 2020. "Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks," Applied Energy, Elsevier, vol. 277(C).
    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. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    2. Dan Liu & Yiqun Kang & Heng Luo & Xiaotong Ji & Kan Cao & Hengrui Ma, 2023. "A Grid Status Analysis Method with Large-Scale Wind Power Access Using Big Data," Energies, MDPI, vol. 16(12), pages 1-12, June.
    3. Zhou, Dezhi & Wu, Chuantao & Sui, Quan & Lin, Xiangning & Li, Zhengtian, 2022. "A novel all-electric-ship-integrated energy cooperation coalition for multi-island microgrids," Applied Energy, Elsevier, vol. 320(C).
    4. Pascual, Julio & Arcos-Aviles, Diego & Ursúa, Alfredo & Sanchis, Pablo & Marroyo, Luis, 2021. "Energy management for an electro-thermal renewable–based residential microgrid with energy balance forecasting and demand side management," Applied Energy, Elsevier, vol. 295(C).
    5. Akhyurna Swain & Elmouatamid Abdellatif & Ahmed Mousa & Philip W. T. Pong, 2022. "Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments," Energies, MDPI, vol. 15(19), pages 1-37, October.
    6. Ronaldo Silveira Junior, Jose & Conrado, Bruna R.P. & Matheus dos Santos Alonso, Augusto & Iglesias Brandao, Danilo, 2023. "Interoperability of single-controllable clusters: Aggregate response of low-voltage microgrids," Applied Energy, Elsevier, vol. 340(C).
    7. 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.
    8. Renata Rodrigues Lautert & Wagner da Silva Brignol & Luciane Neves Canha & Olatunji Matthew Adeyanju & Vinícius Jacques Garcia, 2022. "A Flexible-Reliable Operation Model of Storage and Distributed Generation in a Biogas Power Plant," Energies, MDPI, vol. 15(9), pages 1-21, April.
    9. Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).

    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:jsusta:v:15:y:2023:i:3:p:2555-:d:1052746. 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.