Description
The Summer School will cover topics centred around the design of learning and optimization methods for renewable power systems. Electric power systems are currently undergoing significant changes as part of the energy transition. New planning and operations methods are required to ensure the safety, reliably, and efficiency of the modern power grid. New numerical methods integrating optimization and machine learning can be specifically designed to tackle these challenges. Topics include: large-scale optimization, optimization under uncertainty, machine learning, reinforcement learning and trustworthy machine learning methods dedicated to power systems, electromagnetic transient simulations, demand response. The Summer School will be held over a one-week period. Evaluations related to the course will take place over a month (one week prior and three weeks after).