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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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).
    3. 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).

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