ResearchFor people in the power systems field, my research is best described as ‘‘datadriven distribution network optimization. The tools I have developed include parameter, topology, and state estimation techniques, as well as feedback optimization and distributed phasor feedback control, which adjust power output/load in real time in response to distribution network conditions. In addition to distribution network automation, I am also interested in electricity markets, transmission network applications, and inverter control for low inertia grids.’’ For people in the learning, control, and optimization fields, my research is best described as ‘‘online, datadriven control and optimization methods for stochastic network systems with a focus on power system applications, with additional interests in hybrid systems, market design, unsupervised learning, reinforcement learning, and graph theory.’’ For general audiences, my research is best described as ‘‘developing mathematical techniques which use new technologies such as satellitesynchronized grid sensors to help with our transition from fossil fuels to intermittent renewable energy resources by keeping track of what's happening on the grid and coordinating gridconnected batteries, electric vehicle charging, and electric home heaters/AC units.’’ Below is a list of some of the projects that I have worked on over the course of my Ph.D. and a short statement on my long term research interests. Voltage Phasor Control
Relevant publications:K. Moffat and A. Von Meier. Using voltage phasor control to avoid distribution network constraint violations. In 2022 IEEE Power Systems Computation Conference (PSCC), Submitted, pages 1–6. IEEE, 2022. Preprint Link. K. Moffat and A. von Meier. Linear quadratic phasor control of unbalanced distribution networks. In 2021 IEEE Madrid PowerTech, pages 1–6. IEEE, 2021. Paper link, PowerTech 2021 presentation. K. Moffat Local powervoltage sensitivity and thevenin impedance estimation from phasor measurements. In 2021 IEEE Madrid PowerTech, pages 1–6. IEEE, 2021. Paper link, PowerTech2021 presentation. K. Moffat, J. Pakshong, L. Chu, G. Fierro, Baudette J. Gehbauer C. Swartz, J., and A. von Meier. Phasor based control with the distributed, extensible grid control platform. In 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pages 1–5. IEEE, 2021. Paper link. G. Fierro, K. Moffat, J. Pakshong, and A. von Meier. An extensible software and communication platform for distributed energy resource management. In 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pages 1–6. IEEE, 2020. Paper link. M. Baudette, J. Swartz, K. Moffat, J. Pakshong, C. Gehbauer, and A. von Meier. Hardwareintheloop benchmarking setup for phasor based control validation. 2021. Paper link. Grid Parameter Estimation
Relevant publications:K. Moffat, M. Bariya, and A. Von Meier. Unsupervised impedance and topology estimation of distribution networks—limitations and tools. IEEE Transactions on Smart Grid, 11(1):846–856, 2019. Paper link. K. Moffat, M. Bariya, and A. von Meier, Network Impedance Estimation For Microgrid Control Using Noisy Synchrophasor Measurements, 2018 IEEE 19th Workshop on Control and Modeling for Power Electronics (COMPEL), 2018, pp. 16 Paper link. K. Moffat, M. Bariya, and A. Von Meier. Real time effective impedance estimation for power system state estimation. In 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pages 1–5. IEEE, 2020. Paper link. J.S. Brouillon, E. Fabbiani, P. Nahata, K. Moffat, F. Dorfler, and G. FerrariTrecate. Bayesian errorinvariables models for the identification of distribution grids. IEEE Transactions on Power Systems, In Revision, 2022. Preprint link. State and Topology Estimation
Relevant publications:K. Moffat and C. Tomlin. The multiple model adaptive power system state estimator. In 2021 IEEE Conference on Decision and Control (CDC), pages 1–6. IEEE, 2021. CDC 2021 presentation. M. Bariya, K. Moffat, and A. Von Meier. Empirical noise estimation in distribution synchrophasor measurements. In 2019 International Conference on Smart Grid Synchronized Measurements and Analytics, pages 1–7. IEEE, 2019. Paper link. Feedback Optimization for EV Charging Control
Longer Term PerspectiveEngineers, computer scientists, and mathematicians are grappling with how to use massive data, computing power, and communication to improve automated decisions for network systems such as electric grids. Datadriven control methods such as (deep) reinforcement learning have demonstrated impressive results in simulation, but applying datadriven control to safetycritical systems remains an open problem. For safetycritical systems, the guarantees that come with principled mathematical approaches are paramount. I am interested in developing theory and tools that bridge the gap between simulation and realworld implementation. In particular, I am interested in:
