CAREER: Holistic Distributed Resource Management and Discovery via Augmented Learning and Robust Optimization

Arizona State University, Scottsdale AZ

Investigators

Abstract

This NSF CAREER project aims to develop energy engineering solutions that embody the preferences and needs of varied residential electricity consumers. The research will enable co-management of utility-owned and customer-owned assets and bring transformative changes to how distributed energy resources (e.g., rooftop solar) and power distribution systems are operated. This goal will be achieved by leveraging artificial intelligence algorithms to analyze the human-in-the-loop component of energy systems along with consideration of differences in preferences and energy needs. The intellectual merits of the project include characterizing end users’ consumption behavior and enabling design of behavior-aware smart grid solutions without modeling the behavior itself. The project proposes meticulous methodologies to advance grid-edge resource management while accounting for critical factors. The broader impacts of the project include enhancing energy resilience for all specifically for low-income communities. Experiential learning modules will be designed to educate the public on smart grid technologies and benefits of advanced co-management of utility and consumer-owned assets. It will also initiate the design and development of an interdisciplinary research and educational program focused on sustainable energy engineering solutions. The project will develop ground-up approaches to overcome multiple hurdles for active management of grid-edge resources and power distribution systems. Innovative methodologies will be developed to identify appliance utilization from smart meter data to enable non-intrusive load discovery. Novel artificial intelligence algorithms (for example, causal conditional hidden semi-Markov model) will be developed for behavior-aware non-intrusive load forecasting. An approach based on combined deep-shallow neural networks will be developed for efficient aggregation of mix and match of distributed energy resources with complementary control capabilities. A novel unbalanced AC optimal power flow will be enhanced to facilitate inverter-based distributed energy resources scheduling while identifying coordinated optimal inverter control modes and their settings. 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.