IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i8p1233-d108917.html
   My bibliography  Save this article

A Metric-Based Validation Process to Assess the Realism of Synthetic Power Grids

Author

Listed:
  • Adam B. Birchfield

    (Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA)

  • Eran Schweitzer

    (School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA)

  • Mir Hadi Athari

    (Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • Ti Xu

    (Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA)

  • Thomas J. Overbye

    (Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA)

  • Anna Scaglione

    (School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA)

  • Zhifang Wang

    (Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA)

Abstract

Public power system test cases that are of high quality benefit the power systems research community with expanded resources for testing, demonstrating, and cross-validating new innovations. Building synthetic grid models for this purpose is a relatively new problem, for which a challenge is to show that created cases are sufficiently realistic. This paper puts forth a validation process based on a set of metrics observed from actual power system cases. These metrics follow the structure, proportions, and parameters of key power system elements, which can be used in assessing and validating the quality of synthetic power grids. Though wide diversity exists in the characteristics of power systems, the paper focuses on an initial set of common quantitative metrics to capture the distribution of typical values from real power systems. The process is applied to two new public test cases, which are shown to meet the criteria specified in the metrics of this paper.

Suggested Citation

  • Adam B. Birchfield & Eran Schweitzer & Mir Hadi Athari & Ti Xu & Thomas J. Overbye & Anna Scaglione & Zhifang Wang, 2017. "A Metric-Based Validation Process to Assess the Realism of Synthetic Power Grids," Energies, MDPI, vol. 10(8), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1233-:d:108917
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/8/1233/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/8/1233/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pagani, Giuliano Andrea & Aiello, Marco, 2013. "The Power Grid as a complex network: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(11), pages 2688-2700.
    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. Stover, Oliver & Karve, Pranav & Mahadevan, Sankaran, 2023. "Reliability and risk metrics to assess operational adequacy and flexibility of power grids," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Mirna Grzanic & Marco Giacomo Flammini & Giuseppe Prettico, 2019. "Distribution Network Model Platform: A First Case Study," Energies, MDPI, vol. 12(21), pages 1-17, October.
    3. Zhang, Xiaodong & Patino-Echeverri, Dalia & Li, Mingquan & Wu, Libo, 2022. "A review of publicly available data sources for models to study renewables integration in China's power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).

    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. Tio, Adonis E. & Hill, David J. & Ma, Jin, 2020. "Can graph properties determine future grid adequacy for power injection diversity?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    2. Lu Pang & Cheng Hu & Juan Yu & Haijun Jiang, 2022. "Fixed-Time Synchronization for Fuzzy-Based Impulsive Complex Networks," Mathematics, MDPI, vol. 10(9), pages 1-16, May.
    3. Pandey, Pradumn Kumar & Badarla, Venkataramana, 2018. "Reconstruction of network topology using status-time-series data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 573-583.
    4. Gianluca Fulli & Marcelo Masera & Catalin Felix Covrig & Francesco Profumo & Ettore Bompard & Tao Huang, 2017. "The EU Electricity Security Decision-Analytic Framework: Status and Perspective Developments," Energies, MDPI, vol. 10(4), pages 1-20, March.
    5. Ma, Li & Wang, Lingfeng & Liu, Zhaoxi, 2021. "Multi-level trading community formation and hybrid trading network construction in local energy market," Applied Energy, Elsevier, vol. 285(C).
    6. Ghafory-Ashtiany, Mohsen & Arghavani, Mahban, 2022. "Physical performance of power grids against earthquakes: from framework to implementation," International Journal of Critical Infrastructure Protection, Elsevier, vol. 39(C).
    7. Ahmad, Nasir & Derrible, Sybil, 2018. "An information theory based robustness analysis of energy mix in US States," Energy Policy, Elsevier, vol. 120(C), pages 167-174.
    8. Azzolin, Alberto & Dueñas-Osorio, Leonardo & Cadini, Francesco & Zio, Enrico, 2018. "Electrical and topological drivers of the cascading failure dynamics in power transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 196-206.
    9. Claudio M. Rocco & Kash Barker & Jose Moronta, 2022. "Determining the best algorithm to detect community structures in networks: application to power systems," Environment Systems and Decisions, Springer, vol. 42(2), pages 251-264, June.
    10. Gangwal, Utkarsh & Singh, Mayank & Pandey, Pradumn Kumar & Kamboj, Deepak & Chatterjee, Samrat & Bhatia, Udit, 2022. "Identifying early-warning indicators of onset of sudden collapse in networked infrastructure systems against sequential disruptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    11. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    12. Goddet, Etienne & Retière, Nicolas & Stojanović, Vojislav & Dieudonné, Anca & Genoulaz, Jérôme & Guichon, Jean-Michel, 2019. "Maximizing the algebraic connectivity of meshed electrical pathways used as current return network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 18-31.
    13. Xiao, Guanping & Zheng, Zheng & Wang, Haoqin, 2017. "Evolution of Linux operating system network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 249-258.
    14. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2016. "International business cycle synchronization since the 1870s: Evidence from a novel network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 286-296.
    15. Espejo, Rafael & Lumbreras, Sara & Ramos, Andres, 2018. "Analysis of transmission-power-grid topology and scalability, the European case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 383-395.
    16. Hu, Jianqiang & Yu, Jie & Cao, Jinde & Ni, Ming & Yu, Wenjie, 2014. "Topological interactive analysis of power system and its communication module: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 99-111.
    17. Wang, Shuliang & Lv, Wenzhuo & Zhang, Jianhua & Luan, Shengyang & Chen, Chen & Gu, Xifeng, 2021. "Method of power network critical nodes identification and robustness enhancement based on a cooperative framework," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    18. Kashyap, G. & Ambika, G., 2019. "Link deletion in directed complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 631-643.
    19. Kashin Sugishita & Yasuo Asakura, 2021. "Vulnerability studies in the fields of transportation and complex networks: a citation network analysis," Public Transport, Springer, vol. 13(1), pages 1-34, March.
    20. Ioannis Dassios & Andrew Keane & Paul Cuffe, 2019. "Calculating Nodal Voltages Using the Admittance Matrix Spectrum of an Electrical Network," Mathematics, MDPI, vol. 7(1), pages 1-6, January.

    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:10:y:2017:i:8:p:1233-:d:108917. 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.