Robert Mieth of Rutgers University

The stochasticity of power injections from weather-dependent renewable energy resources challenges power system operations and increases the risk-exposure of electricity market participants. Defining safety regions for which any operative or market decision must be viable can be an effective and tractable tool to overcoming these challenges. Yet, designing such safety regions from historical data with respect to their impact on the quality of the decision risk is tricky. This seminar discusses two proposals for data-driven robust decision tools in renewable-dominant electric power systems that explicitly internalize probabilistic performance metrics of their solution. First, we discuss an approach to learn prescriptive safety regions for wind power generation. We leverage recent results from differentiable programming to create individual safety intervals for each wind generator that adapt to current grid situations such that they optimize an operator-defined risk target. Second, we discuss a method to internalize adversarial uncertainty sets in electricity market clearing such that consumer risk is minimized. We define these sets using a factor stressing approach inspired from financial engineering and show that consumer risk in terms of excess payments can be effectively reduced at a low overall system cost.

Biography:

Robert Mieth is an Assistant Professor in the Industrial and Systems Engineering Department at Rutgers University. He is the founder and PI of the Reliability, Operation, and Planning of Power and Energy Systems (ROPES) Lab. Before joining Rutgers in fall 2023, Robert was a Leopoldina Postdoctoral Fellow in the Electrical and Computer Engineering Department at Princeton University. From 2021 to 2022 he was a Postdoc in the Department of Electrical and Computer Engineering of New York University’s Tandon School of Engineering as part of the ARPA-E funded PERFOM project. Robert received the Doctorate in Engineering (Dr.-Ing.) degree from the Technical University of Berlin in cooperation with NYU, where he was a visiting researcher from 2017 through 2020. His research and academic trajectory have been supported by prestigious fellowships including the German National Academic Foundation, the Rainer Lemoine-Foundation, and the German Academy of Sciences (Leopoldina). Robert’s research interests include risk analysis, stochastic optimization, and data methods for low-emission power systems and electricity markets.