Autonomous coordination and control of smart converters for sustainable power systems
This project aims at developing smart autonomous power converters for optimal support of electric power systems. The project will build a new control framework based on digitalization and AI that will provide optimal grid support functions and enable further integration of renewable energy sources. This is achieved by developing a novel combined optimization and control algorithm for coordination and an AI-based scheme for autonomous control of smart converters.
The outcome of this project is expected to enhance the resilience of electrical power systems with large-scale integration of renewables and add significant value to the digitalized electrical power industry.
To achieve the national target for 100% renewables by 2040, renewables are increasingly integrated into electric power systems. Unfortunately, the intermittent renewables increase the risk of grid instability with the voltage fluctuation, frequency deviation and inertia issues, which limit the further integration of renewables. The renewable interface converters offer new promising methods to provide various functions for grid support. To efficiently and successfully utilize the grid support capabilities of the converters requires optimized coordination of a large number of converters. However, optimal coordination is a significant challenge due to limited communication support, multi-timescale operation, various real-time control actions, and computational complexity.
The researchers in the team represent the Department of Electrical Engineering at KTH EECS and the Department of Mathematics at KTH SCI.
Watch the recorded presentation at Digitalize in Stockholm 2022 event:
Assistant Professor at KTH, Co-PI of research project Autonomous coordination and control of smart converters for sustainable power systems, Digital Futures Facultyjankr@kth.se
Assistant Professor at KTH, Co-PI of project Autonomous coordination and control of smart converters for sustainable power systems, PI of project Data-Driven Control and Coordination of Smart Converters for Sustainable Power System Using Deep Reinforcement Learning, Digital Futures Faculty+46 8 790 63 56