Resilient Networks

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https://hdl.handle.net/10125/109914

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Power Flow Modeling of the Impacts of Weather and Other Resiliency Hazards With a Focus on Transmission Planning
(2025-01-07) Safdarian, Farnaz; Cook, Jordan; Zhgun, Kseniia; Overbye, Thomas; Snodgrass, Jonathan
This paper presents an approach for modeling weather and other environmental inputs (ENIs) in the power flow and related tools with a focus on electric grid transmission planning. Such work is needed because of the rapidly growing dependence of electric grids on the weather and the need to consider the impact of more severe resiliency events. The paper presents a modeling approach, and then demonstrates it using several large-scale electric grids. Validation is also considered. A key contribution is to show that environmental inputs can be directly integrated into existing power flow and related tools such as contingency analysis and optimal power flow.
CyberDep: Enhanced Generation of Bayesian Networks through the Inclusion of Bidirectional Data Flow Dependencies in Cyber-Physical Power Systems
(2025-01-07) Al Homoud, Leen; Davis, Katherine; Hossain-Mckenzie, Shamina; Jacobs, Nicholas
Power systems have been analyzed and studied as purely physical systems for a long time. Such efforts were critical to the establishment of the power grid as it is today. However, with the increased interest in the integration of renewable energy, the grid is experiencing more vulnerabilities to its operation, stability, and resiliency from the cyber realm. As such, it is crucial to understand the cyber-physical power system interdependencies. In this paper, we advance a Bayesian Network generation algorithm, called CyberDep. CyberDep quantifies cyber-physical interdependencies through conditional probability calculations and aids in analyzing bidirectional data flow dependencies and n-to-1 nodal connections between elements. CyberDep is implemented on a dataset of the cyber-physical emulation of the WSCC 9-bus system, which includes running physical, cyber, and cyber-physical disturbances on the system. The results showcase an improved interdependency quantification and visualization of the n-to-1 probabilistic relationships between the physical and cyber system components.
Quantifying Metrics for Wildfire Ignition Risk from Geographic Data in Power Shutoff Decision-Making
(2025-01-07) Piansky, Ryan; Rhodes, Noah; Taylor, Sofia; Roald, Line; Molzahn, Daniel; Watson, Jean-Paul
Faults on power lines and other electric equipment are known to cause wildfire ignitions. To mitigate the threat of wildfire ignitions from electric power infrastructure, many utilities preemptively de-energize power lines, which may result in power shutoffs. Data regarding wildfire ignition risks are key inputs for effective planning of power line de-energizations. However, there are multiple ways to formulate risk metrics that spatially aggregate wildfire risk map data, and there are different ways of leveraging this data to make decisions. The key contribution of this paper is to define and compare the results of employing six metrics for quantifying the wildfire ignition risks of power lines from risk maps, considering both threshold- and optimization-based methods for planning power line de-energizations. The numeric results use the California Test System (CATS), a large-scale synthetic grid model with power line corridors accurately representing California infrastructure, in combination with real Wildland Fire Potential Index data for a full year. This is the first application of optimal power shutoff planning on such a large and realistic test case. Our results show that the choice of risk metric significantly impacts the lines that are de-energized and the resulting load shed. We find that the optimization-based method results in significantly less load shed than the threshold-based method while achieving the same risk
A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources
(2025-01-07) Liu, Shaohui; Cai, Weiqian; Zhu, Hao; Johnson, Brian
The growing prevalence of inverter-based resources (IBRs) for renewable energy integration and electrification greatly challenges power system dynamic analysis. To account for both synchronous generators (SGs) and IBRs, this work presents an approach for learning the model of an individual dynamic component. The recurrent neural network (RNN) model is used to match the recursive structure in predicting the key dynamical states of a component from its terminal bus voltage and set-point input. To deal with the fast transients especially due to IBRs, we develop a Stable Integral (SI-)RNN to mimic high-order integral methods that can enhance the stability and accuracy for the dynamic learning task. We demonstrate that the proposed SI-RNN model not only can successfully predict the component's dynamic behaviors, but also offers the possibility of efficiently computing the dynamic sensitivity relative to a set-point change. These capabilities have been numerically validated based on full-order Electromagnetic Transient (EMT) simulations on a small test system with both SGs and IBRs, particularly for predicting the dynamics of grid-forming inverters.
GPU-Accelerated DCOPF using Gradient-Based Optimization
(2025-01-07) Rafiei, Seide Saba; Chevalier, Samuel
Seide Saba Rafiei University of Vermont srafiei@uvm.edu Samuel Chevalier University of Vermont schevali@uvm.edu Abstract DC Optimal Power Flow (DCOPF) is a key operational tool for power system operators, and it is embedded as a subproblem in many challenging optimization problems (e.g., line switching). However, traditional CPU-based solve routines (e.g., simplex) have saturated in speed and are hard to parallelize. This paper focuses on solving DCOPF problems using gradient-based routines on Graphics Processing Units (GPUs), which have massive parallelization capability. To formulate these problems, we pose a Lagrange dual associated with DCOPF (linear and quadratic cost curves), and then we explicitly solve the inner (primal) minimization problem with a dual norm. The resulting dual problem can be efficiently iterated using projected gradient ascent. After solving the dual problem on both CPUs and GPUs to find tight lower bounds, we benchmark against Gurobi and MOSEK, comparing convergence speed and tightness on the IEEE 2000, 4601, and 10000 bus systems. We provide reliable and tight lower bounds for these problems with, at best, 5.4x speedup over a conventional solver.
Learning a Local Trading Strategy: Deep Reinforcement Learning for Grid-scale Renewable Energy Integration
(2025-01-07) Ju, Caleb; Crozier, Constance
Variable renewable generation increases the challenge of balancing power supply and demand. Grid-scale batteries co-located with generation can help mitigate this misalignment. This paper explores the use of reinforcement learning (RL) for operating grid-scale batteries co-located with solar power. Our results show RL achieves an average of 61% (and up to 96%) of the approximate theoretical optimal (non-causal) operation, outperforming advanced control methods on average. Our findings suggest RL may be preferred when future signals are hard to predict. Moreover, RL has two significant advantages compared to simpler rules-based control: (1) that solar energy is more effectively shifted towards high demand periods, and (2) increased diversity of battery dispatch across different locations, reducing potential ramping issues caused by super-position of many similar actions.
Design for Secure and Resilient Data Exchange Across DistributedCyber-Physical Sensors and Analytics in Decentralized Energy Systems
(2025-01-07) Cordeiro, Patricia; Chavez, Adrian; Reyna, Alex; Hossain-Mckenzie, Shamina; Fragkos, Georgios; Collins, Taylor; Summers, Adam; Haque, Khandaker Akramul; Davis, Katherine
Resilience of power systems requires mutualistically supported survivability characteristics of their cyber, physical, and cyber-physical networks. These networks of electrical and communication exchange, and their successful and reliable available interconnections and interactions underpin core energy delivery functions. Providing comprehensive system resilience across a wide range of time and geographic scales requires actionable intelligence. The challenge lies in deriving this intelligence using data extracted from power systems with heterogeneous sensors, networks and network types. Providing this cyber-physical situational awareness (CPSA) at scale calls for advancements in scientific techniques, system designs, and their implementations. The goal is to provide CPSA data fusion through a secure data exchange implementation designed to be extended and generalized for application across the full range of large-scale grid architectures. Addressing this need requires work to expose and address the challenges of sensor design and implementation architecture for fusing and delivering cyber-physical data from heterogeneous locations throughout these networks. This paper proposes and presents a sensor design and implementation architecture for such a secure data exchange, with the aim of achieving CPSA on the interconnected electric grid at multiple levels with multiple owners involving integration of security technologies. The authors also address the many individual resource and interface requirements posed by the variety of devices involved, offering specific security measures to address them.
Utilizing Regulation to Improve Risk and Equity in Heterogeneous Transmission Grids
(2025-01-07) Newman, David; Carreras, Benjamin; Lenhart, Stephanie; Blumsack, Seth; Hay, Brian
As electric power grid critical infrastructure grows increasingly heterogeneous, a key question is how to encourage and ensure relatively equitable access to the energy it supplies. With diverse socio-economic regions linked to the grid and various generation types, achieving equitable access to clean energy, as outlined in initiatives like DOE’s Justice40, remains an important aspirational goal. Building on previous work, this paper describes our investigation of a heterogenous grid modeled on the Alaska Railbelt, allowing us to explore the intrinsic inequities and possible mechanisms to enhance the equity across regions. We apply risk and equity metrics to different regions to examine how penalties which change the cost can impact the equity as well as the overall grid’s risk and dynamics.
Introduction to the Minitrack on Resilient Networks
(2025-01-07) Davis, Katherine; Newman, David

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