Validation of Synthetic U.S. Electric Power Distribution System Data Sets

Krishnan, Venkat; Bugbee, Bruce; Elgindy, Tarek; Mateo, Carlos; Dueñas, Pablo; Postigo, Fernando; Lacroix, Jean-Sebastien; Román, Tomás Gómez San; Palmintier, Bryan

doi:10.1109/tsg.2020.2981077

Cited by 43 publications

(27 citation statements)

References 18 publications

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“…Instead of relying on single-point values such as an average, statistical distributions capture relative occurrences of a metric's values based on sample data and help modeling networks in detail. For example, it is common to use empirical distributions [30], [34] or fit statistical models to the nodal degree, including the exponential [20], [25], [31], [32], Poisson [27], or mixture models [19], [26].…”

Section: ) Node Degreementioning

confidence: 99%

“…Here, D ij is the minimum [23], [31], [32], [37]- [39] [20], [22], [35] Charac. Path Length [17] N/A [29]- [32], [37], [40], [41] [20], [22], [34], [35] Graph Diameter [17] N/A [29], [32], [35], [37] [20], [34], [35] Degree Assortativity N/A [30]- [32], [42] [35] Centrality [17] N/A [28], [32], [40], [43]- [45] [20] Other Measures [19] N/A [21], [31], [42], [46], [47] [19], [36], [39], [48] Topological Geography [49]- [53] N/A Asset Statistics N/A [54] [34], [49], [55]- [58] Line Lengths [17], [19], [33] N/A <...>…”

Section: ) Characteristic Path Lengthmentioning

confidence: 99%

“…A weighted approach can also be used for actual line lengths [40]. For example, [17], [34] computed l of generated synthetic networks in miles and compared with actual distribution feeders. Studied MV distribution networks in China were observed to be 4.5-10.5 km for suburban and 2-4.5 km for urban regions [30].…”

Section: Operationalmentioning

confidence: 99%

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Synthetic Benchmarks for Power Systems

Mohammadi

Saleh

2021

IEEE Access

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Power system benchmarks are transmission and distribution networks used to evaluate novel control algorithms and simulate grid evolution scenarios. These benchmarks range in size, system characteristics, and use cases. Although active working groups have created and published many benchmarks, these networks are not all representative of a given region and may not consider certain aspects such as increased penetration levels of distributed energy resources. To address these issues, synthetic benchmark networks and methodologies for generating them have been developed by various research groups. This paper provides a comprehensive survey of procedures commonly used to generate synthetic networks and a detailed account of the various metrics used to define and validate benchmarks. Existing models are categorized into different approaches, including expert design, anonymized clustering, statistical sampling, and heuristic algorithms. Deep graph generation based techniques are also presented and recommended for the network generation problem. A comparative summary is provided to highlight the different existing works in this area and reveal research gaps, along with a list of published datasets and their characteristics.

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Section: ) Node Degreementioning

confidence: 99%

Section: ) Characteristic Path Lengthmentioning

confidence: 99%

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Synthetic Benchmarks for Power Systems

Mohammadi

Saleh

2021

IEEE Access

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“…This provides a topological geographic information system representation and enables power flow simulations of the planned system. Validation and calibration of these networks was performed by applying a three-pronged approach comprising of (1) statistical validation of characteristic metrics, (2) operation validation of power flow and (3) validation from industry experts [37].…”

Section: E Plan the Medium-voltage System/primariesmentioning

confidence: 99%

“…The base synthetic system was then validated using previously established techniques developed by the authors. Specifically, the distribution system was statistically validated as described in [37]. This includes comparisons of several key structural properties of the network that were compared to comprehensive data obtained from tens of thousands of actual feeders from several U.S. utilities and found to be within statistically comparable ranges.…”

Section: A Validationmentioning

confidence: 99%

Building Highly Detailed Synthetic Electric Grid Data Sets for Combined Transmission and Distribution Systems

Wert

Birchfield

et al. 2020

IEEE Open J. Power Energy

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This paper introduces a methodology for building synthetic electric grid data sets that represent fictitious, yet realistic, combined transmission and distribution (T&D) systems. Such data sets have important applications, such as in the study of the wide-area interactions of distributed energy resources, in the validation of advanced control schemes, and in network resilience to severe events. The data sets created here are geographically located on an actual North American footprint, with the enduser load information estimated from land parcel data. The grid created to serve these fictional but realistic loads is built starting with low-voltage and medium-voltage distribution systems in full detail, connected to distribution and transmission substations. Bulk generation is added, and a high-voltage transmission grid is created. This paper explains the overall process and challenges addressed in making the combined case. An example test case, syn-austin-TDgrid-v03, is shown for a 307,236-customer case located in central Texas, with 140 substations, 448 feeders, and electric line data at voltages ranging from 120 V to 230 kV. Such new combined test cases help to promote high quality in the research on large-scale systems, particularly since much actual power system data are subject to data confidentiality. The highly detailed, combined T&D data set can also facilitate the modeling and analysis of coupled infrastructures.

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