Abstract
Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such 'what-if' scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k 'trigger' components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.
| Original language | English |
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| Title of host publication | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
| Editors | Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1964-1973 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781665439022 |
| DOIs | |
| State | Published - 2021 |
| Event | 2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021 |
Publication series
| Name | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
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Conference
| Conference | 2021 IEEE International Conference on Big Data, Big Data 2021 |
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| Country/Territory | United States |
| City | Virtual, Online |
| Period | 12/15/21 → 12/18/21 |
Funding
§Most work is done as part of PhD research at Virginia Tech This document has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). There are several interesting directions to further study and guide CA through DIHeN. First, the network construction may play an important role in DIHeN performance. E.g., while our performance was promising in all the datasets, the gain of our model in ERCOT was not as high as the others. This might be due to the sparsity of the network across critical components (e.g., fewer high voltage nodes). Investigating this aspect across regional grids more in detail would be interesting. Next, although we use multiple criticality criteria for evaluating S, there are several user-defined and domain-specific criteria that can be incorporated to tailor the methodology to specific analysis needs. Using such new criticality criteria can be more beneficial for selective CA and hence is an interesting direction of study. As a part of future work, we plan to analyze DIHeN quantitatively with the ground-truth trigger components collected by real CA simulations. Acknowledgements: This paper is based on work partially supported by NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, Medium IIS-2106961, CCF-2115126, NRT DGE-1545362), CDC MInD program, ORNL, faculty award from Facebook, and funds/computing resources from Georgia Tech. We thank Nikhil Muralidhar for his thoughtful comments during initial time of the research.