Come out and support OR Ph.D. Students as they practice their talks for the upcoming INFORMS Annual Conference.
https://ncsu.zoom.us/j/93445582296?pwd=FykTWtJLHlZkhaVn6aV4AZrwVmMrF7.1
Meeting ID: 934 4558 2296
Passcode: 051656
Title: Estimating Access Risk During Blackouts Due to Vehicle Electrification in North Carolina
Abstract: Vehicle electrification is a key component of sustainable development goals, yet the mass adoption of electric vehicles can lead to unintended consequences for community mobility during natural hazards. Electric vehicles pose a challenge to owners during natural hazards that lead to power outages, as a lack of home charging limits the mobility of EV owners. Due to the direct relation between driving distance to essential services and vehicle battery consumption, changes in mobility will be impacted by geographic and technological factors. Geography determines the driving distance to essential services, which will be translated to electricity consumption devoted to transportation, and technology determines the size of electric vehicle batteries and vehicle efficiency. The linkage between mobility, electric power availability and quality of life has broad implications for community resilience as equitable access to essential services has been identified as the most important aspect of community resilience. We present the results of a computational framework for estimating the risk of losing access to essential services during a prolonged blackout in North Carolina, assuming complete adoption of electric vehicles. We leverage an open-source routing engine to compute driving distance to services, census data to identify driving requirements (such as school enrollment and commuting by private car) and a database of previous blackouts to estimate county-level blackout risk. Additionally, we incorporate a heuristic to approximate distance driven on commuting using isochrone curves. Finally, we explore the sensitivity of our results to vehicle battery capacity, which is tightly linked to vehicle affordability.
Biography: Yamil Essus is a Ph.D. student in the Industrial and Systems Engineering Department working in modeling community systems to understand and quantify the impacts of technological and social changes on the ability of communities to withstand natural hazards using statistical and data visualization tools.
Title: Comparative Analysis of Distance Metrics in Distributionally Robust Optimization for Queuing Systems: Wasserstein vs. Kingman
Abstract: This study examines the effectiveness of different metrics in constructing ambiguity sets for Distributionally Robust Optimization (DRO). Two main approaches for building ambiguity sets are the moment- and the discrepancy-based approaches. The latter is more widely adopted because it incorporates a broader range of distributional information beyond moments. Among discrepancy-based metrics, the Wasserstein distance is often preferred for its advantageous properties over ϕ-divergence. In this study, we propose a moment-based Kingman distance, an approximation of mean waiting time in G/G/1 queues, to determine the ambiguity set. We demonstrate that the Kingman distance provides a straightforward and efficient method for identifying worst-case scenarios for simple queue settings. In contrast, the Wasserstein distance requires exhaustive exploration of the entire ambiguity set to pinpoint the worst-case distributions. These findings suggest that the Kingman distance could offer a practical and effective alternative for DRO applications in some cases.
Biography: Hyungkhee (HK) is a second-year Ph.D. student in the ISE department at North Carolina State University. He received his B.S. degree in Civil Engineering from Handong Global University, and M.S. degree in Development Policy from KDI School of Public Policy and Management, Korea. HK’s research interests include stochastic simulation and optimization. His current research focuses on bias correction and novel techniques for rapid identification of worst-case performance in distributionally robust optimization.
Title: Automated Vessel Selection and Combat Load Planning
Abstract: In large-scale combat operations, the U.S. military must move and maneuver its forces through intratheater and intertheater modes of transportation. Threat forces exacerbate these demanding requirements through efforts to hinder the flow of friendly forces. Contested landing zones, whether they be fixed ports or beaches, are the starting point for a landing force’s ground combat operations. It is imperative that the landing force expeditiously off-loads in the prescribed order of priority to support the planned scheme of maneuver upon off-loading. We first discuss current and historical methods of load planning while emphasizing the need for more detailed and automated methods driven by the expected nature of future conflicts against near-peer threats. We then present a model that optimizes vessel selection, sequencing, and combat load configurations of a large military force by considering multiple levels of priority and group unity, which enables efficient off-loading into desired tactical formations for follow-on objectives.
Biography: Will Kirschenman has served 14 years in the U.S. Army, first as an Engineer and now as an FA49 Officer or ORSA (Operations Research / Systems Analyst). He received his B.S. in Mechanical Engineering from the United States Military Academy at West Point in 2010 and his M.S. in Operations Research (OR) from George Mason University in 2020. He is in his second year of the OR Ph.D. program and will graduate in Spring 2026. His research involves combinatorial optimization in vessel selection and combat load planning in the military, where multiple levels of priority and group unity must be incorporated. He has a wife and three kids and enjoys fitness, the outdoors, and spending time with his family.
Title: Derivation and Generation of Path-Based Valid Inequalities for Transmission Expansion Planning with New Bus Integration
Abstract: This research tackles the intricacies of DC OPF-based transmission expansion planning (DC-TEP), accommodating the integration of new buses into the power grid. To handle the general intractability of this problem, the primary computational strategy involves deriving Valid Inequalities (VIs). The proposed approach utilizes problem structure and graph-based methodologies to generate highly effective VIs. More specifically, these VIs are generated by identifying relevant power flow paths connecting buses within the existing and expanded network before solving the dispatch problem. While in the associated theorems identifying the longest paths is deemed essential to preserve integer solutions, the proposed approach efficiently circumvents such an inefficient procedure.
Biography: Behnam Jabbari is a Ph.D. student in Industrial and Systems Engineering and a Master’s student in Operations Research at NC State University. His research focuses on Systems Analytics and Optimization, with a particular emphasis on developing theoretical methodologies for solving discrete optimization problems. Specifically, Behnam is working to improve computational methods for Mixed-Integer Linear Programming (MILP) in power systems optimization, aiming to design efficient algorithms with robust guarantees.
Veronica Diaz Pacheco
Title:Assessing the Vulnerability of Power Systems in Decarbonized Power Grids: A Network Interdiction Study
Abstract: Decarbonizing electric power systems will likely involve greater reliance on variable renewable energy, especially wind, and solar, which provide lower electricity output per land area than fossil fuel and nuclear-based power. As a result, a distinct feature of future bulk power systems may be more numerous, lower-capacity generators spread more evenly throughout the network. The impacts of this new configuration on the vulnerability of system operations are still unclear. Motivated by recent physical attacks on grid infrastructure, this study examines how decarbonization through variable renewables could alter the vulnerability of the power system to intentional attacks. We explore this question as a network interdiction problem, a two-player sequential game model where an adversary aims to maximally damage a cost-minimizing system operator by destroying grid components (generators, transmission lines, substations, etc.). While network interdiction has previously been used to assess the vulnerability of power systems, its application in the context of system-wide decarbonization is novel. We use a clustering procedure on annual load and renewable generation data from a real-world 662-bus regional transmission system to generate representative operating scenarios. We then evaluate how an attacker’s optimal strategies change throughout a typical operating year, contrasting the vulnerabilities of the current and future (decarbonized) grid configuration, including the capacity mix and its distribution throughout the regional transmission network.
Biography: Veronica is a fourth-year doctoral student and researcher in the Operations Research program at North Carolina State University. After experiencing the devastating impacts of Hurricane Maria in her homeland Puerto Rico during September 2017, Veronica became deeply interested in the field of decision-making under disruptions and emergency management. Under the supervision of her advisor, Dr. Jordan Kern, Veronica is exploring the use of bi-level programming to assess and enhance the resilience of bulk power system operations, particularly with the integration of renewable energy sources. Veronica’s research interests include mathematical modeling and programming, emergency management, simulation, networks, and energy systems.
