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.
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Creating Realistic Power Distribution Networks using Interdependent Road Infrastructure
Abstract—It is well known that physical interdependencies exist
between networked civil infrastructures such as transportation
and power system networks. In order to analyze complex nonlinear
correlations between such networks, datasets pertaining to
such real infrastructures are required. However, such data are
not readily available due to their proprietary nature. This work
proposes a methodology to generate realistic synthetic power
distribution networks for a given geographical region. A network
generated in this manner is not the actual distribution system, but
its functionality is very similar to the real distribution network.
The synthetic network connects high voltage substations to
individual residential consumers through primary and secondary
distribution networks. Here, the distribution network is generated
by solving an optimization problem which minimizes the overall
length of the network subject to structural and power flow
constraints. This work also incorporates identification of long
high voltage feeders originating from substations and connecting
remotely situated customers in rural geographic locations while
maintaining voltage regulation within acceptable limits. The
proposed methodology is applied to the state of Virginia and
creates synthetic distribution networks which are validated by
comparing them to actual power distribution networks at the
same location.
Index Terms—synthetic distribution networks, radial networks,
Mixed Integer Linear Programming
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- NSF-PAR ID:
- 10253373
- Date Published:
- Journal Name:
- IEEE International Conference on Big Data
- Page Range / eLocation ID:
- 1226 to 1235
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData50022.2020.9377959
