8 citations · 18 across the 28 of their papers we have counts for
12 papers · 1 filter
Efficient Network Reconfiguration by Randomized Switching
Samuel Talkington, Dmitrii M. Ostrovskii, Daniel K. Molzahn
We present an algorithm that efficiently computes nearly-optimal solutions to a class of combinatorial reconfiguration problems on weighted, undirected graphs. Inspired by societal…
Fair Cost Allocation in Energy Communities: A DLMP-based Bilevel Optimization with a Shapley Value Approach
Hyeongon Park, Kyuhyeong Kwag, Daniel K. Molzahn +1
Energy communities (ECs) are emerging as a promising decentralized model for managing cooperative distributed energy resources (DERs). As these communities expand and their operati…
Differentiating Through Power Flow Solutions for Admittance and Topology Control
Samuel Talkington, Daniel Turizo, Sergio A. Dorado-Rojas +2
The power flow equations relate bus voltage phasors to power injections via the network admittance matrix. These equations are central to the key operational and protection functio…
Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk
Weimin Huang, Ryan Piansky, Bistra Dilkina +1
To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes line energization statuses to man…
Admittance Matrix Concentration Inequalities for Understanding Uncertain Power Networks
Samuel Talkington, Cameron Khanpour, Rahul K. Gupta +5
This paper presents conservative probabilistic bounds for the spectrum of the admittance matrix and classical linear power flow models under uncertain network parameters; for examp…
Certifying the Nonexistence of Feasible Path Between Power System Operating Points
Mohammad Rasoul Narimani, Katherine R. Davis, Daniel K. Molzahn
By providing the optimal operating point that satisfies both the power flow equations and engineering limits, the optimal power flow (OPF) problem is central to power systems opera…
Error Bounds for Radial Network Topology Learning from Quantized Measurements
Samuel Talkington, Aditya Rangarajan, Pedro A. de Alcântara +3
We probabilistically bound the error of a solution to a radial network topology learning problem where both connectivity and line parameters are estimated. In our model, data error…
VArsity: Can Large Language Models Keep Power Engineering Students in Phase?
Samuel Talkington, Daniel K. Molzahn
This paper provides an educational case study regarding our experience in deploying ChatGPT Large Language Models (LLMs) in the Spring 2025 and Fall 2023 offerings of ECE 4320: Pow…
Optimizing Battery and Line Undergrounding Investments for Transmission Systems under Wildfire Risk Scenarios: A Benders Decomposition Approach
Ryan Piansky, Rahul K. Gupta, Daniel K. Molzahn
With electric power infrastructure posing an increasing risk of igniting wildfires under continuing climate change, utilities are frequently de-energizing power lines to mitigate w…
Evaluating Undergrounding Decisions for Wildfire Ignition Risk Mitigation across Multiple Hazards
Ryan Piansky, Daniel K. Molzahn, Nicole D. Jackson +1
With electric power infrastructure increasingly susceptible to impacts from climate-driven natural disasters, there is an increasing need for optimization algorithms that determine…
Sample-Based Piecewise Linear Power Flow Approximations Using Second-Order Sensitivities
Paprapee Buason, Sidhant Misra, Daniel K. Molzahn
The inherent nonlinearity of the power flow equations poses significant challenges in accurately modeling power systems, particularly when employing linearized approximations. Alth…
Optimizing Phase Allocation in Unbalanced Power Distribution Networks using a Linearized DistFlow Formulation
Rahul K. Gupta, Daniel K. Molzahn
Power distribution networks, especially in North America, are often unbalanced but are designed to keep unbalance levels within the limits specified by IEEE, IEC, and NEMA standard…