A Mathematical Programming Solution for Automatic Generation of Synthetic Power Flow Cases

Schweitzer, Eran; Scaglione, Anna

doi:10.1109/tpwrs.2018.2863266

Cited by 10 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Protection & Control N/A [66], [67] [34], [48], [62], [68], [69] length needed to traverse from nodes i to j, calculated using Dijkstra's algorithm [71].…”

Section: Operationalmentioning

confidence: 99%

“…Another metric is the line loading defined as the ratio of apparent power flow with respect to the MVA rating at surge impedance loading 1 , which followed an exponential decay for EI and WI [48], [63]. The power flows across all branches were statistically modeled and used in [62].…”

Section: ) Power Qualitymentioning

confidence: 99%

“…The EU DSO report [54] provides statistical distributions of SAIDI and SAIFI indicators for long unplanned interruptions. To demonstrate economic operation, locational marginal pricing was considered to compare optimal power flows of synthetic grids with the Polish 2383-bus system [62]. The range can indicate the realism of a grid's operation; negative values occurred for congested lines and a cheap marginal cost generator could not operate at its maximum limit.…”

Section: ) Operation and Reliabilitymentioning

confidence: 99%

“…Simulated frequency responses of EI, WI and TI after a sudden loss of generation were reported in [67] and used as reference for the grid modeling. Also, the voltage angle difference across a branch, which is proportional to the active power transfer, was modeled in [48], [62] for synthetic transmission grids.…”

Section: ) Protection and Controlmentioning

confidence: 99%

“…Moving onto heuristic algorithms, an optimization procedure was developed in [62] to create synthetic power flow cases. The following constraints were considered in the formulation: nodal energy balance, linearized branch flows, node permutations, power flow limits, and minimum losses.…”

Section: Transmission Systemsmentioning

confidence: 99%

See 4 more Smart Citations

Synthetic Benchmarks for Power Systems

Mohammadi

Saleh

2021

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…Protection & Control N/A [66], [67] [34], [48], [62], [68], [69] length needed to traverse from nodes i to j, calculated using Dijkstra's algorithm [71].…”

Section: Operationalmentioning

confidence: 99%

Section: ) Power Qualitymentioning

confidence: 99%

Section: ) Operation and Reliabilitymentioning

confidence: 99%

Section: ) Protection and Controlmentioning

confidence: 99%

Section: Transmission Systemsmentioning

confidence: 99%

See 3 more Smart Citations

Synthetic Benchmarks for Power Systems

Mohammadi

Saleh

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

Generation of synthetic multi‐resolution time series load data

2023

View full text Add to dashboard Cite

The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. The authors designed an end‐to‐end generative framework for the creation of synthetic bus‐level time‐series load data for transmission networks. The model is trained on a real dataset of over 70 Terabytes of synchrophasor measurements spanning multiple years. Leveraging a combination of principal component analysis and conditional generative adversarial network models, the developed scheme allows for the generation of data at varying sampling rates (up to a maximum of 30 samples per second) and ranging in length from seconds to years. The generative models are tested extensively to verify that they correctly capture the diverse characteristics of real loads. Finally, an opensource tool called LoadGAN is developed which gives researchers access to the fully trained generative models via a graphical interface.

show abstract