Self-stabilizing energy management in power electronics power distribution systems
Abstract
This research project intends to provide computationally tractable descriptions for distributed protocols that minimize the time-sca,le disparity between static optimization and real-time control of uncertain loads, and jointly attain network-wide optimality and dy,namic stability, for power electronics power distribution systems (PEPDS). To help expand the distribution network beyond a simple i,mpedance grid to include interfacing converters, control protocols should be stabilized with respect to uncertain loads. Treatment o,f sensor-intensive converters as power bots enables their collective control. The distributed nature of proposed solutions makes the,m scalable, and bridging the temporal gap between dynamic stability, real-time control, and network optimality leverages the low-ine,rtia nature of PEPDS. Synthesizing contemporary insights from power electronics systems, networked control, optimization theory, and, machine learning, the following symbiotic research thrusts will be pursued: i) Robust self-optimizing real time control: Energy man,agement mechanisms usually focus on the operational constraints in the steady state, and the setpoints they provide to real-time con,trollers could fail to account for uncertain loads. Most investigations into connections between operation and control layers use de,terministic formulations of optimization problems. In lieu of optimizing the PEPDS for separate time epochs, we pursue a real-time a,pproach that does not deviate from optimality. We aim to bridge the temporal gap between the optimization and control layers, and ac,commodate load uncertainty using computationally-tractable robust optimization mechanisms. ii) Self-stabilizing optimal power flow:,Separate treatment of static energy management, slowvarying operational constraints, and dynamic stability criteria is quite common,in conventional power system analysis, but this antiquated approach would not be practical for low-inertia PEPDS supporting dynamic,loads. We will embed the stability criteria in energy management, and then significantly accelerate the optimization protocol, to ac,hieve near temporal parity with dynamic stability, or design stabilizing feedback controllers that arrive at the optimal solutions f,or the energy management problem. iii) Digital twin and high-fidelity controller/hardware-in-the-loop validation: Control-theoretic,solutions will be experimentally validated in a digital twin environment using a CHIL enterprise to help calibrate theoretical hypot,hesis, guarantee performance, and outline alternative directions. Proposed efforts contribute to models, algorithms, and development, environments for PEPDS, and help maintain real-time optimal and stable control continuity in the face of topological or operational, changes. The University of Texas at Arlington is designated as a Carnegie R-1, Hispanic-serving, Asian American Native American Pac,ific Islander-serving, and Texas Tier One institution. Given award-winning work, extensive experimental capabilities, and research t,rack records of the research team, they are positioned to address pressing research challenges impeding the vision of PEPDS.Publicly, Releasable.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 08, 2022
- Source ID
- N000142212524
Entities
People
- Ali Davoudi
Organizations
- Office of Naval Research
- United States Navy
- University of Texas at Arlington