Graph neural networks for power grid operational risk assessment under evolving grid topology Article Swipe
Yadong Zhang
,
Pranav Karve
,
Sankaran Mahadevan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.07343
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.07343
This article investigates the ability of graph neural networks (GNNs) to identify risky conditions in a power grid over the subsequent few hours, without explicit, high-resolution information regarding future generator on/off status (grid topology) or power dispatch decisions. The GNNs are trained using supervised learning, to predict the power grid's aggregated bus-level (either zonal or system-level) or individual branch-level state under different power supply and demand conditions. The variability of the stochastic grid variables (wind/solar generation and load demand), and their statistical correlations, are rigorously considered while generating the inputs for the training data. The outputs in the training data, obtained by solving numerous mixed-integer linear programming (MILP) optimal power flow problems, correspond to system-level, zonal and transmission line-level quantities of interest (QoIs). The QoIs predicted by the GNNs are used to conduct hours-ahead, sampling-based reliability and risk assessment w.r.t. zonal and system-level (load shedding) as well as branch-level (overloading) failure events. The proposed methodology is demonstrated for three synthetic grids with sizes ranging from 118 to 2848 buses. Our results demonstrate that GNNs are capable of providing fast and accurate prediction of QoIs and can be good proxies for computationally expensive MILP algorithms. The excellent accuracy of GNN-based reliability and risk assessment suggests that GNN models can substantially improve situational awareness by quickly providing rigorous reliability and risk estimates.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.07343
- https://arxiv.org/pdf/2405.07343
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396913579
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396913579Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2405.07343Digital Object Identifier
- Title
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Graph neural networks for power grid operational risk assessment under evolving grid topologyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-12Full publication date if available
- Authors
-
Yadong Zhang, Pranav Karve, Sankaran MahadevanList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.07343Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2405.07343Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2405.07343Direct OA link when available
- Concepts
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Grid, Computer science, Topology (electrical circuits), Power grid, Graph, Artificial neural network, Power graph analysis, Distributed computing, Power (physics), Artificial intelligence, Theoretical computer science, Mathematics, Engineering, Electrical engineering, Quantum mechanics, Physics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.suggests | 203 |
| abstract_inverted_index.training | 92, 98 |
| abstract_inverted_index.GNN-based | 198 |
| abstract_inverted_index.awareness | 211 |
| abstract_inverted_index.bus-level | 51 |
| abstract_inverted_index.different | 61 |
| abstract_inverted_index.excellent | 195 |
| abstract_inverted_index.expensive | 191 |
| abstract_inverted_index.explicit, | 24 |
| abstract_inverted_index.generator | 29 |
| abstract_inverted_index.learning, | 44 |
| abstract_inverted_index.predicted | 125 |
| abstract_inverted_index.problems, | 111 |
| abstract_inverted_index.providing | 177, 214 |
| abstract_inverted_index.regarding | 27 |
| abstract_inverted_index.shedding) | 144 |
| abstract_inverted_index.synthetic | 159 |
| abstract_inverted_index.topology) | 33 |
| abstract_inverted_index.variables | 73 |
| abstract_inverted_index.aggregated | 50 |
| abstract_inverted_index.assessment | 138, 202 |
| abstract_inverted_index.conditions | 13 |
| abstract_inverted_index.considered | 85 |
| abstract_inverted_index.correspond | 112 |
| abstract_inverted_index.decisions. | 37 |
| abstract_inverted_index.estimates. | 219 |
| abstract_inverted_index.generating | 87 |
| abstract_inverted_index.generation | 75 |
| abstract_inverted_index.individual | 57 |
| abstract_inverted_index.line-level | 118 |
| abstract_inverted_index.prediction | 181 |
| abstract_inverted_index.quantities | 119 |
| abstract_inverted_index.rigorously | 84 |
| abstract_inverted_index.stochastic | 71 |
| abstract_inverted_index.subsequent | 20 |
| abstract_inverted_index.supervised | 43 |
| abstract_inverted_index.(wind/solar | 74 |
| abstract_inverted_index.algorithms. | 193 |
| abstract_inverted_index.conditions. | 66 |
| abstract_inverted_index.demonstrate | 171 |
| abstract_inverted_index.information | 26 |
| abstract_inverted_index.methodology | 154 |
| abstract_inverted_index.programming | 106 |
| abstract_inverted_index.reliability | 135, 199, 216 |
| abstract_inverted_index.situational | 210 |
| abstract_inverted_index.statistical | 81 |
| abstract_inverted_index.variability | 68 |
| abstract_inverted_index.branch-level | 58, 148 |
| abstract_inverted_index.demonstrated | 156 |
| abstract_inverted_index.hours-ahead, | 133 |
| abstract_inverted_index.investigates | 2 |
| abstract_inverted_index.system-level | 142 |
| abstract_inverted_index.transmission | 117 |
| abstract_inverted_index.(overloading) | 149 |
| abstract_inverted_index.correlations, | 82 |
| abstract_inverted_index.mixed-integer | 104 |
| abstract_inverted_index.substantially | 208 |
| abstract_inverted_index.system-level) | 55 |
| abstract_inverted_index.system-level, | 114 |
| abstract_inverted_index.sampling-based | 134 |
| abstract_inverted_index.computationally | 190 |
| abstract_inverted_index.high-resolution | 25 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |