Regression Nash Equilibrium in Electricity markets

Abstract: We argue that the economic efficiency of renewable-dominated electricity markets improves if stochastic producers (e.g., wind power producers) coordinate on their forecast models. To support this argument, we introduce the regression equilibrium—a state of the electricity market where no profit-seeking stochastic producer benefits by unilaterally deviating from their respective forecast model. While the equilibrium aim is to maximize the private welfare of each producer, i.e., the profit of each market participant, it also aligns with the socially optimal solution that minimizes the average electricity cost across the day-ahead and real-time stages, thus improving temporal market coordination. We base the equilibrium analysis on the theory of variational inequalities, providing results on the existence and uniqueness of such an equilibrium in energy-only markets, and deriving two methods for equilibrium computation.

Bio: Vladimir Dvorkin is an Assistant Professor in the Electrical Engineering and Computer Science Department at the University of Michigan. He has held positions as a postdoctoral fellow at the Massachusetts Institute of Technology’s Energy Initiative and LIDS from 2021–2023, and as a visiting researcher at Georgia Tech’s School of Industrial and Systems Engineering. He received his Ph.D. in Electrical Engineering from the Technical University of Denmark in 2021. His research focuses on the energy transition towards a renewable-dominant and low-carbon energy supply, viewed through the lenses of optimization and machine learning, energy economics, and algorithmic data privacy. His work has received numerous recognitions, including the Marie Skłodowska-Curie Actions and Iberdrola Group postdoctoral fellowship and the IEEE Transactions on Power Systems Best Paper Award.

*** This Event will take place in a hybrid format. The location for in-person attendance will be room 1303 EECS. Attendance will also be available via Zoom.

Join Zoom Meeting: https://umich.zoom.us/j/96731875637

Meeting ID: 967 3187 5637

Passcode: XXXXXX (Will be sent via e-mail to attendees)

Zoom Passcode information is also available upon request to Kristi Rieger([email protected])