About the project
Objective
This research aims to develop learning-to-optimize (L2O) methods to enhance real-time decision-making in modern energy grids. The focus is twofold: improving the efficiency of model predictive control under operational constraints, and accelerating the computation of Nash equilibria in competitive multi-agent scenarios.
By integrating machine learning with optimization, the project seeks to deliver solvers that are both fast and reliable, capable of generalizing across varying grid conditions. This work supports the broader goal of enabling more adaptive, efficient, and robust energy systems in the face of increasing complexity and renewable integration.
Background
The growing complexity of energy systems, driven by renewable energy, distributed assets, and diverse stakeholders, has created a pressing need for real-time, efficient decision-making tools. Traditional optimization methods often fall short under tight time constraints and non-convex, large-scale settings. However, many of these problems exhibit structural similarities across instances—such as similar dynamics or constraint patterns—which can be leveraged by learning-based methods. This repeated structure makes energy systems a natural fit for L2O approaches, which can learn from past problem instances to accelerate future optimization while maintaining feasibility and convergence.
About the Digital Futures Postdoc Fellow
Andrea Martin completed his PhD in Robotics, Control, and Intelligent Systems at EPFL, Switzerland, in 2025, with a thesis on optimal control and decision-making under uncertainty. His research interests include control theory, optimization, and machine learning. Prior to that, he earned master’s degrees in Automation Engineering from the University of Padua, Italy, and in Automatic Control and Robotics from the Polytechnic University of Catalonia, Spain, in 2020. He received his bachelor’s degree in Information Engineering from the University of Padua in 2017.
Main supervisor
Giuseppe Belgioioso, Division of Decision and Control Systems, KTH.
Co-supervisor
Mikael Johansson, Division of Decision and Control Systems, KTH.
