Dirk Lauinger wins the INFORMS ENRE Student Best Paper Award 2022

© 2022 EPFL

© 2022 EPFL

At the INFORMS Annual Meeting 2022 Dirk Lauinger, former PhD student of Daniel Kuhn and now postdoc at MIT, received the INFORMS ENRE Student Best Paper Award for his paper “Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints” (co-authored with his advisors Francois Vuille and Daniel Kuhn).

This award is given annually to the best paper dealing with energy, environmental, or natural resource issues by a student, as judged by a panel of the Energy, Natural Resources, and the Environment Section of INFORMS. With over 12,500 members from around the globe, INFORMS is the leading international association for professionals in operations research, analytics, management science, economics, behavioral science, statistics, artificial intelligence, data science, applied mathematics, and other relevant fields. In his winning paper, Dirk develops a mathematical model of an electric vehicle that sells primary frequency regulation to the grid operator (vehicle-to-grid). This model allows him to examine technical, economic, regulatory, as well as environmental aspects of vehicle-to-grid.


Vehicle-to-grid increases the low utilization rate of privately owned electric vehicles by making their batteries available to electricity grids. We formulate a robust optimization problem that maximizes a vehicle owner’s expected profit from selling primary frequency regulation to the grid and guarantees that market commitments are met at all times for all frequency deviation trajectories in a functional uncertainty set that encodes applicable legislation. Faithfully modeling the energy conversion losses during battery charging and discharging renders this optimization problem non-convex. By exploiting a total unimodularity property of the uncertainty set and an exact linear decision rule reformulation, we prove that this non-convex robust optimization problem with functional uncertainties is equivalent to a tractable linear program. Through extensive numerical experiments using real-world data, we quantify the economic value of vehicle-to-grid and elucidate the financial incentives of vehicle owners, aggregators, equipment manufacturers, and regulators. We find that the prevailing penalties for non-delivery of promised regulation power are too low to incentivize vehicle owners to honor their promises toward grid operators.