Automated Generation Algorithm for Synthetic Medium Voltage Radial Distribution Systems

Schweitzer, Eran; Scaglione, Anna; Monti, Antonello; Pagani, Giuliano Andrea

doi:10.1109/jetcas.2017.2682934

Cited by 36 publications

(34 citation statements)

References 30 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%

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%

Synthetic Benchmarks for Power Systems

Mohammadi

Saleh

2021

IEEE Access

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“…Recent research has shown that synthetic feeders can be useful for obtaining general results that are appropriate for being used at a high-level decision process [22][23][24][25][26][27][28][29]. A representation of the synthetic feeder used in this paper is shown in Figure 1.…”

Section: Synthetic Feedermentioning

confidence: 99%

Distribution Reliability Optimization Using Synthetic Feeders

Brown

Pinkerton²

2019

Energies

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Historical distribution system reliability optimization approaches have relied on the creation of detailed feeder reliability models, which is extremely labor-intensive. This paper presents a reliability optimization approach using synthetic feeders that does not require creating a topologically-precise model, but can still perform an overall system assessment considering a wide range of reliability improvement options for far less modeling effort. The synthetic feeder approach is then applied to Hawaiian Electric’s Oʻahu distribution system, to identify (1) the lowest cost required to achieve various levels of reliability improvement, and (2) an approximate reliability project portfolio for each level of spending.

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“…Compared to [11] that proposed a methodology for automatically synthesizing varied sets of medium voltage distribution feeders, this paper and our previous works [8]- [10] focus on large-scale high-voltage transmission systems. Bus type entropies and other metrics obtained using statistics from currently available cases were developed in [12] and [13] to improve the modeling of synthetic power grids.…”

Section: Introductionmentioning

confidence: 99%

“…However, work [16] generated only a graph that matches the topological properties of actual grids, without considering any generation and demand data. Synthetic cases in [11]- [15], [17] were developed for steady-state power flow analysis. The research goal of this paper is to extend synthetic network base cases (for power flow studies) with appropriate generator dynamic models, that are able to reproduce similar responses as the actual grids.…”

Section: Introductionmentioning

confidence: 99%

Modeling, Tuning, and Validating System Dynamics in Synthetic Electric Grids

Birchfield

Overbye

2018

IEEE Trans. Power Syst.

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A synthetic network modeling methodology has been developed to generate completely fictitious power system models with capability to represent characteristic features of actual power grids. Without revealing any confidential information, synthetic network models can be shared freely for teaching, training, and research purposes. Additional complexities can be added into synthetic models to widen their applications. Thus, this paper aims to extend synthetic network base cases for transient stability studies. An automated algorithm is proposed to assign appropriate models and parameters to each synthetic generator, according to fuel type, generation capacity, and statistics summarized from actual system cases. A two-stage model tuning procedure is also proposed to improve synthetic dynamic models. Several transient stability metrics are developed to validate the created synthetic network dynamic cases. The construction and validation of dynamics for a 2000-bus synthetic test case is provided as an example. Simulation results are presented to verify that the created test case is able to satisfy the transient stability metrics and produce dynamic responses similar to those of actual system cases.

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Automated Generation Algorithm for Synthetic Medium Voltage Radial Distribution Systems

Cited by 36 publications

References 30 publications

Synthetic Benchmarks for Power Systems

Synthetic Benchmarks for Power Systems

Distribution Reliability Optimization Using Synthetic Feeders

Modeling, Tuning, and Validating System Dynamics in Synthetic Electric Grids

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