ECE Seminar Series: Enabling resilient control of power systems with distributed energy storage

ECE title

ECE Seminar Series Fall 2015

Monday November 16th 2-3 PM, ITEB 336

Enabling resilient control of power systems with distributed energy storage

Mads R. Almassalkhi

University of Vermont

Abstract: In 2003, the National Academy of Engineering named the electric grid the Greatest Engineering Achievement of the 20th century, however, just a few months later, US and Canada experienced their largest ever black-out. Year-to-year increases in the number of large blackouts suggest that power systems today are operated closer and closer to their limits. To aid human control-room operators overcome this challenge, increased sensing and actuation is becoming available in the control room, including PMUs, FACTS devices, and fast-acting demand and energy storage. However, this added system complexity makes it more difficult for human operators to determine an appropriate response to unanticipated events. At a minimum, decision-support tools are needed to guide human decision-making. In fact, closed-loop feedback processes will become indispensable. As such, we present resilient model predictive control (MPC) schemes that mitigate the effects of overloads in transmission and distribution systems. Resilient control is achieved through a receding-horizon model predictive control (MPC) strategy which alleviates temperature-based overloads on transmission lines and distribution-level transformers and, therefore, prevents large outages. Both centralized and distributed optimization-based schemes will be presented with numerical case studies.


Short Bio: Mads R. Almassalkhi is an Assistant Professor at School of Engineering at the University of Vermont. His research interests lie at the intersection of power systems, optimization, and controls and focus on developing novel feedback and optimization algorithms that improve responsiveness and resilience of power systems, which is increasingly more important as power systems are operating closer and closer to their limits. His past work includes model predictive control of bulk power systems, distributed control of multi-agent systems, applications of optimization and systems theory to electric and multi-energy power systems. Prior to joining the University of Vermont, he was lead systems engineer at Root3 Technologies. He received his MS and PhD from the University of Michigan in Electrical Engineering: Systems and a dual-degrees in Electrical Engineering and Applied Mathematics from the University of Cincinnati, Ohio.