1 citations · 2 across the 14 of their papers we have counts for
6 papers · 1 filter
Learning in Inverse Games: Tractable Training with Probabilistic Guarantees
Arghya Mallick, Reza Rahimi Baghbadorani, Peyman Mohajerin Esfahani +1
Inverse game theory seeks to learn agents' unknown objectives from observed equilibrium behavior. Existing residual-based approaches can lead to non-convex problems and need not en…
Hybrid Sequential Feedback Optimization for Wind Farm Power Maximization
Shijie Huang, Sergio Grammatico
This paper considers feedback optimization for optimal steady-state operation of nonlinear discrete-time systems when the steady-state input-output map and its sensitivity are expe…
Benchmarking Sequential Feedback Optimization for Wind Farm Power Maximization
Shijie Huang, Sergio Grammatico
This paper benchmarks sequential feedback optimization (SFO) for wind farm power maximization using a medium-fidelity dynamic flow model. We compare SFO with two well-established a…
User-centric Vehicle-to-Grid Optimization with an Input Convex Neural Network-based Battery Degradation Model
Arghya Mallick, Georgios Pantazis, Mohammad Khosravi +2
We propose a data-driven, user-centric vehicle-to-grid (V2G) methodology based on multi-objective optimization to balance battery degradation and V2G revenue according to EV user p…
A User-centric Game for Balancing V2G Benefits with Battery Degradation of Electric Vehicles
Arghya Mallick, Georgios Pantazis, Peyman Mohajerin Esfahani +1
We present a novel user-centric vehicle-to-grid (V2G) framework that enables electric vehicle (EV) users to balance the trade-off between financial benefits from V2G and battery he…
A Douglas-Rachford Splitting Method for Solving Monotone Variational Inequalities in Linear-quadratic Dynamic Games
Reza Rahimi Baghbadorani, Emilio Benenati, Sergio Grammatico
This paper considers constrained linear dynamic games with quadratic objective functions, which can be cast as affine variational inequalities. By leveraging the problem structure,…