CAREER: Universal Modeling of Real and Virtual Energy Storage with Connected Power Electronics

Oregon State University, Corvallis OR

Investigators

Abstract

Energy storage supports the uninterruptable operation of electricity, mechanics, heat, etc. Modern highly-dynamic electrical systems, such as renewable-dominated grids, electric vehicles and aircraft, require energy storage to provide resilience, mitigating power variations at much smaller timescales. Multiple “real” energy storage physics exist, such as batteries, supercapacitors, fuel cells, but are limited in energy or power bandwidth. Recently, modulated load control, particularly through flexible thermal loads, such as building HVAC systems or water heaters, has gained understanding and is regarded as “virtual” storage, given its inherent existence as an energy buffer around a nominal load. A typical design of an energy storage system considers one or multiple storage physics and aggregates their respective electric models for control and energy management; this way overlooks the system-level picture and handles the complexity after it is created. This research explores a top-down co-design process by utilizing a universal energy storage electric model, combining real and virtual energy storage and connected power electronics. The proposed process directly yields optimal energy storage requirements among a cluster of energy storage options, and allows for most resiliency while reducing the complex energy management burden and sustaining an extended lifetime. The work will benefit many stakeholders in transportation electrification, utilities, and building sectors. Further, the project will enhance undergraduate and graduate curriculum by combining the advances in power electronics tied energy storage systems. The project will work with management consulting and international educational partners to train STEM students toward leadership and global vision. In addition, the project will reach out to local small businesses and Native American tribes to provide education in emerging energy storage technologies, thereby enabling pursuit of prosperous careers in the field. This project develops a novel universal modeling and design framework for energy storage systems. Conventional heuristic selection of energy storage is suboptimal, dynamic interaction of real and virtual storage is lacking, the power electronics side of virtual energy storage is not well understood, and the lifetime impact of combined storage is not known. Fundamental research in modeling and design emphasizes bandwidth selections using an enhanced multi-timescale equivalent impedance network, followed by a co-design of connected power electronics, multi-objective optimization, dynamic energy management, and reliability analysis. Several machine learning-based methods in intelligent search, deep reinforcement learning, and big-data-driven control will also play a role in the work. Scientific knowledge will be gained through in-depth electrical-thermal-mechanical physics, modern power electronic converters and controls, emerging data science and artificial intelligence, and a hardware testbed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.