Ensembles of realistic power distribution networks

Abstract

The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.

A simplicial epidemic model for COVID-19 spread analysis

“…Finally, the higher-order group interactions are to be systematically integrated into the construction of synthetic social contact networks ( 51 – 57 ) and, more generally, be accounted in digital twins of population behavior. Needless to say, these implications and open questions are valid way beyond biosurveillance and apply to a wide range of problems, from information propagation and signal processing ( 58 , 59 ) to financial contagion and fraud detection ( 60 , 61 ) to resiliency of critical infrastructures ( 62 – 64 ).…”

Preparing infrastructure for surprise: fusing synthetic network, interdependency, and cascading failure models

“…In contrast, synthetic network models are realistic but fictional networks. Often used for power infrastructure, synthetic networks are being developed to provide realistic test cases for future network optimization and emerging technologies such as microgrids and decentralized renewable generation (Marcos et al 2017, Meyur et al 2022. Theoretical and real networks were primarily used for interdependency studies and cascading failure simulations, whereas most literature on synthetic networks still focuses on model development (Marcos et al 2017).…”

“…Cascading failure models generally focus on infrastructure vulnerabilities (Mahabadi et al 2021, Meyur 2022), but some studies also focus on recovery from disturbances. (Barrett et al 2010, Holden et al 2013, Shen 2013, Guidotti et al 2016, Nan and Sansavini 2017, Yang et al 2019, Munikoti et al 2021, Sharma and Gardoni 2022.…”

“…In partnership with NREL, Mateo et al (2020) created 'RNM-US,' a methodology to synthesize power distribution networks for large areas in the United States. Meyur et al (2022) created a synthetic distribution model using publicly available data in combination with engineering and economic optimization. The simultaneous emergence of various synthetic transmission and distribution models bodes well for the future of synthetic power research.…”