Model Predictive Control for Smart Energy Systems

Rasmus Halvgaard

PhD Student, Department of Informatics and Mathematical Modeling, Technical University of Denmark

Abstract

Integrating large amounts of renewable energy sources like wind and solar in the power system introduces large fluctuations in the power production. In Denmark 25% of the power production comes from wind and by 2020 it is expected to cover 50%. Consuming all of this green energy as it is produced requires very flexible and controllable power consumption. This talk addresses my recent work on modeling and applying Model Predictive Control (MPC) to some of the potential flexible loads like thermal storage in buildings, auxiliary heating in solar collector storage tanks, and charging of electric vehicles. When introducing a large number of these loads in a Smart Grid they could potentially help to absorb the power fluctuations at different time scales. A strategy for managing these large scale systems using MPC is also proposed.

Biography

Rasmus Halvgaard was born in Denmark in 1984. He received his MSc degree in Electrical Engineering from the Technical University of Denmark (DTU) in 2010 where he is currently employed as a PhD student in the Department of Informatics and Mathematical Modeling. His primary research interests are within control theory and in particular Model Predictive Control (MPC) with applications to smart energy systems.