Nicholas Coleman, a Ph.D. student in ECE, has earned the 2018 Outstanding Dissertation Award by Drexel University's Graduate College. The award comes with a $500 prize and will be presented to Nick during an award ceremony on Thursday, May 31.
According to the Graduate College, "the Outstanding Dissertation Awards are presented to PhD/doctoral students who have written original, innovative dissertations that reflect great research and have or are likely to be disseminated widely and have significant impact on the field and society."
Nick Coleman is advised by Dr. Karen Miu of the ECE Department.
The abstract for Nick's dissertation is below. Our congratulations to Nick on this great accomplishment!
Historically, electric power distribution systems were considered passive subsystems served by the larger transmission grid. Recently, smart grid initiatives have driven the evolution of distribution systems into active systems with market-aware customers and distributed power generation. Along with more diverse and complex injections, contemporary distribution systems are equipped with additional sensing equipment, two-way communications networks, and advanced metering infrastructure (AMI). These are essential technologies that enable several core functions of a smart grid, including real-time monitoring and online control.
This thesis presents several tools for the online analysis and control of modern electric power distribution systems. “Online" refers to a control framework that can react to changing system conditions in order to maintain static security and meet different operating objectives. Specifically, the objective of this research is to integrate temporal information (i.e., forecasts) into distribution system analysis tools while maintaining fundamental engineering requirements by re-examining classical problems through a contemporary lens.
Connected by an underlying injection forecast model, three research topics are explored: 1) distribution load capability, 2) analytical time window selection for quasi-static time series (QSTS) analysis, and 3) distribution state estimation with explicit consideration of non-synchronized measurements. The work proposed here is a necessary step towards distribution system optimization in an online setting with uncertain and/or bidirectional power flows.