Speaker: Robert Mieth, Leopoldina Postdoctoral Fellow, Department of Electrical and Computer Engineering, Princeton University
Title: From Bug to Feature: Rethinking Uncertainty in Modern Power Systems
Modern power systems play a central role in creating a carbon-neutral economy. On the one hand, they distribute electric power from new emission-free generation sources such as wind and solar. On the other hand, they enable the ongoing electrification of previously non-electric loads, such as cars and building heating, as well as industry processes. However, a timely adoption of required technology and business models is obstructed by the established paradigms of power system operation and planning as they struggle to accommodate the uncertainty introduced by weather- and behavior-dependent energy resources. While numerous methods and technologies have been proposed to either reduce uncertainty itself (e.g., via improved forecasting) or reduce the impact of uncertainty (e.g., via more robust decision and control), it remains a nuisance attached to emerging energy systems and hinders investments. In this talk I will explore pathways to rethink the models and practices that define power system operations and electricity markets such that uncertainty turns from a challenge into a feature that achieves synergies with modern control technologies and financing solutions. To this end, I will discuss risk models that combine statistical information and data analysis with physics-informed decision-making processes to compute uncertainty-aware electricity prices, demand-side flexibility, and reserve allocations. I will outline a novel model for quantifying the value of data in the context of its quality and the decision-making process it is used in and discuss the potential trajectory of a data-fueled future power system.
Bio: Robert Mieth is currently a Leopoldina Postdoctoral Fellow at the Department of Electrical and Computer Engineering at Princeton University. He received the Doctorate in Engineering (Dr.-Ing.) from the Technical University of Berlin, Germany, in 2021. From 2018 to 2020 he was a Visiting Scholar and, from 2021 to 2022, a Postdoctoral Researcher at the Department of Electrical and Computer Engineering of New York University's Tandon School of Engineering. His research interests include risk analysis, stochastic optimization, data methods, and machine learning for modern power system operations and electricity markets.