Sijia Geng, “Scalable Optimization and Analysis Methods in Energy and Electrified Transportation Systems”
Please join us on Tuesday, October 31, 2023 at 12:00pm in Clark Hall Room 110 and on ZOOM for the CIS & MINDS Seminar Series:
Guest: Sijia Geng
Assistant Professor
Johns Hopkins University, Electrical and Computer Engineering
Topic: “Scalable Optimization and Analysis Methods in Energy and Electrified Transportation Systems”
Join Zoom Meeting: https://wse.zoom.us/j/95386212146
Passcode: cis&minds
If you would like to meet with Sijia Geng, please sign up at this link: TBA
Sijia Geng
Assistant Professor
Johns Hopkins University, Electrical and Computer Engineering
“Scalable Optimization and Analysis Methods in Energy and Electrified Transportation Systems”
Abstract: Electric energy systems are currently experiencing a profound transformation, including a considerable degree of uncertainty driven by renewable energy sources, new forms of dynamics due to inverter-based resources (IBRs), and large-scale integration of electric vehicles (EVs) that integrates various energy sectors and transportation.
In the first part of the talk, I will propose a novel “integer-clustering” approach to model a large number of EVs that manages vehicle charging and energy at the fleet level yet maintain individual trip dispatch. The model is then used to develop a spatially and temporally-resolved decision-making tool for optimally planning and/or operating EV fleets and energy infrastructure (e.g., fast-charging, hydrogen-fueling, and distributed energy resources). The tool comprises a two-stage framework where a tractable disaggregation step follows the integer-clustering problem to recover an individually feasible solution. We establish theoretical lower and upper bounds on the true individual formulation which underpins a guaranteed performance of the proposed method. The optimality accuracy and computational efficiency of the integer-clustering formulation are numerically validated on a real-world case study of Boston’s public transit network. Substantial speedups with minimal loss in solution quality are demonstrated. By using a real geospatial timetable dataset for bus schedule and renewable generation, we provide insights into different pathways for decarbonizing heavy-duty EV fleets and their impacts on energy systems.
In the second part of the talk, I will focus on an important problem of voltage stability in power systems. The problem is related to finding the singular solution space boundary (SSB) of power flow equations. We propose a novel method rooted in differential geometry to approximate the SSB of power systems under high variability of renewable generation. Conventional methods mostly rely on either expensive numerical continuation at specified directions or numerical optimization. Instead, the proposed approach constructs the Christoffel symbols of the second kind from the Riemannian metric tensors to characterize the complete local geometry which is then extended to the proximity of the SSB with efficient computations. As a result, this approach is suitable to handle high-dimensional variability in operating points. We demonstrate advantages of the proposed method using various case studies and provide additional insights on voltage stability in renewable-rich power systems.
Biography: Sijia Geng is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University. Before joining JHU in January 2023, she was a Postdoctoral Associate at the Laboratory for Information & Decision Systems (LIDS) at MIT in 2022. She received her Ph.D. in Electrical and Computer Engineering from the University of Michigan, Ann Arbor, where she also received the M.S. in Mathematics and M.S. in ECE. Her research integrates methodologies from system and control theory, analysis, and optimization to address pressing and fundamental challenges in complex and networked energy systems. She aims at driving the widespread utilization of renewable energy resources while enhancing the resiliency and efficiency of energy systems through developing rigorous theory and scalable computational tools. She is the recipient of a Best Paper Award at the MIT/Harvard Applied Energy Symposium in 2022 and was named a Barbour Scholar in 2021.