Resilient Networks

Permanent URI for this collectionhttps://hdl.handle.net/10125/112471

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Sensitivity Analyses of Synthetic Power Grid Modeling Techniques in a Global Context: A Case Study in Ghana
(2026-01-06) Asiamah, Richard; Ji, Karen; Watson, Jean-Paul; Molzahn, Daniel
Synthetic power grid models play a pivotal role in algorithmic benchmarking and techno-economic policy analysis. Existing synthetic grid research has primarily focused on regions in the United States and Europe using methods based on these regions' characteristics. This paper examines these methods' suitability for creating representative models for other regions. Differing data availability, power consumption behaviors, and applicability of various modeling assumptions challenge the suitability of existing synthetic grid methods for non-Western countries. Our analysis focuses on the West African country of Ghana. We evaluate methods for estimating electric demand and transmission network topologies by benchmarking them against a representation of Ghana developed in our previous work that is based on an accurate network topology and public reports. Our results indicate that existing population-based demand assumptions may be inapplicable. Transmission topology methods can yield reasonable results when aggregate characteristics match those of the real system, but they do not capture the centralization of Ghana's grid.
Using Machine Learning to Validate Firewall Business Rule Justifications
(2026-01-06) Dunn, Kegan; Haque, Khandaker Akramul; Maehl, Andrew; Wlazlo, Patrick; Goulart, Ana; Davis, Katherine
Many independent power grid operators use firewalls to segment their networks, ensuring that only authorized users can remotely access/operate these critical systems. These firewalls are configured with thousands of rules in a firewall. In order for an auditor or a corporate employee to easily understand what the rule does and its purpose, these rules need to have a business justification. In this paper, we investigate how machine learning models can be used to verify whether the firewalls' business justifications accurately reflect their corresponding firewall rules and include appropriate business language. Our case study uses industry data and Random Forest machine learning model combined with Label Powerset Random Oversampling that can identify different types of inconsistencies in a firewall rule justification.
Climate Data for Power Systems Applications: Lessons in Reusing Wildfire Smoke Data for Solar PV Studies
(2026-01-06) Salinas, Arleth; Sohail, Irtaza; Pascucci, Valerio; Stefanakis, Pantelis; Amjad, Saud; Panta, Aashish; Schigas, Roland; Chui, Timothy Chun-Yiu; Duboc, Nicolas; Farrokhabadi, Mostafa; Stull, Roland
Data reuse is using data for a purpose distinct from its original intent. As data sharing becomes more prevalent in science, enabling effective data reuse is increasingly important. In this paper, we present a power systems case study of data repurposing for enabling data reuse. We define data repurposing as the process of transforming data to fit a new research purpose. In our case study, we repurpose a geospatial wildfire smoke forecast dataset into a historical dataset. We analyze its efficacy toward analyzing wildfire smoke impact on solar photovoltaic energy production. We also provide documentation and interactive demos for using the repurposed dataset. We identify key enablers of data reuse including metadata standardization, contextual documentation, and communication between data creators and reusers. We also identify obstacles to data reuse such as risk of misinterpretation and barriers to efficient data access. Through an iterative approach to data repurposing, we demonstrate how leveraging and expanding knowledge transfer infrastructures like online documentation, interactive visualizations, and data streaming directly address these obstacles. The findings facilitate big data use from other domains for power systems applications, grid resiliency.
Modeling Electric Grid Topology with Spatially-Aware Degree-Corrected Stochastic Block Model
(2026-01-06) Kunkolienkar, Sanjana; Snodgrass, Jonathan; Birchfield, Adam; Overbye, Thomas
The goal of this paper is to facilitate the development of synthetic electric grid topologies that replicate the structural properties of real-world power systems. This paper demonstrates that the topology of the North American transmission grid can be modeled using a Spatially-Aware Degree-Corrected Stochastic Block Model (SA-DCSBM), which captures three key features in real grids: modularity, heterogeneous node degree distributions, and distance-constrained connectivity. Once the model is fitted to the North American transmission network data, synthetic topologies (excluding electrical phenomena) are generated to demonstrate that they accurately reproduce real grid statistics across multiple structural dimensions, including modularity, edge length distribution, degree heterogeneity, and spectral robustness. The SA-DCSBM thus offers a modeling framework for creating high-fidelity synthetic electric grid topologies that preserve spatial and structural realism.
Using Spectral Graph Wavelets to Analyze Large Power System Oscillation Modes
(2026-01-06) Lowery, Luke; Baek, Jongoh; Birchfield, Adam
This paper presents a novel method for modal analysis to extract the spatial-temporal characteristics of oscillations in large electrical networks. A vector-fitted approximation of the Spectral Graph Wavelet Transformation (SGWT) and the inverse SGWT are derived to identify intra-network oscillations within a system response. This method scales linearly with the number of branches and leverages sparse solution techniques to develop a fast, low-memory estimation of modal frequency, shape, and damping. A case study on synthetic networks (2k-80k buses) with full dynamic modeling demonstrates consistent sub-second performance of modal estimation. Compared to existing methods, the SGWT approach can estimate modes with fewer channels and a shorter time-domain window. This presents a fast, general method for identifying true multiscale network behavior and localized oscillation sources, marking a novel application of graph-based signal processing.
Solving Three-phase AC Infeasibility Analysis to Near-zero Optimality Gap
(2026-01-06) Panthee, Bikram; Pandey, Amritanshu
Recent works have shown the use of equivalent circuit-based infeasibility analysis to identify weak locations in distribution power grids. For three-phase power flow problems, when the power flow solver diverges, three-phase infeasibility analysis (TPIA) can converge and identify weak locations. The original TPIA problem is non-convex, and local minima and saddle points are possible. This can result in grid upgrades that are sub-optimal.To address this issue, we reformulate the original non-convex nonlinear program (NLP) as an exact non-convex bilinear program (BLP). Subsequently, we apply the spatial branch-and-bound (sBnB) algorithm to compute a solution with near-zero optimality gap. To improve sBnB performance, we introduce a bound tightening algorithm with variable filtering and decomposition, which tightens bounds on bilinear variables. We demonstrate that SBT significantly improves the efficiency and accuracy of Gurobi's sBnB algorithm. Our results show that the proposed method can solve large-scale three-phase infeasibility analysis problems with >5k nodes, achieving an optimality gap of less than 10e-4. Furthermore, we demonstrate that by utilizing the developed presolve routine for bounding, we can reduce the runtime of sBnB by up to 97%.
Introduction to the Minitrack on Resilient Networks
(2026-01-06) Davis, Katherine; Newman, David

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