Skip to main content

Data-driven control and coordination of smart converters for sustainable power system using deep reinforcement learning

This project aims to address the voltage instability caused by a high ratio of renewables in sustainable power grids by making the control and coordination of converters of distributed energy resources more intelligent. To that end, we will leverage deep reinforcement learning to train data-driven and communication-efficiently control policies that are able to adapt to the fast fluctuation of renewable energy resources. We will train policies on advanced simulation environments and implement our AI algorithms on real microgrids in our lab at KTH. The developed control policies will allow converters to automatically learn to optimize their interactions with the complex grid environment and achieve a smooth integration of renewables without voltage security violation, thus promoting a climate-neutral society.

Moving towards sustainability and climate security, electric power systems are going through a major paradigm shift with wide integration of distributed energy resources, such as solar PV, wind power, energy storage and electric vehicles. However, today’s grid cannot handle the voltage rise and fast voltage fluctuations from the high penetration of renewables, which will cause a violation of grid security. Power converters of distributed energy resources have full controllability, promising to be utilized to address this challenge. At the same time, it is widely recognized that the lack of adequate control mechanisms to regulate the voltages is a key hindrance. Our belief is that AI and machine learning will play a key role in improving control strategies for converters by making them more adaptive and intelligent to stabilize complex and changing power grids.

Crossdisciplinary collaboration
This project is a collaboration with KTH EECS, Stockholm University and UC Berkeley.

Watch the recorded presentation at Digitalize in Stockholm 2022 event:

Follow us on social media to stay updated with our current research


Picture of Qianwen Xu

Qianwen Xu

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
Picture of Sindri Magnússon

Sindri Magnússon

Associate professor, Department of Computer and Systems Sciences at Stockholm University, Vice Chair Working group Cooperate, Co-PI of research project Decision-making in Critical Societal Infrastructures (DEMOCRITUS), Co-PI of research project Data-Driven Control and Coordination of Smart Converters for Sustainable Power System Using Deep Reinforcement Learning at DTI, Co-Supervisor for postdoc project Distributed Optimization and Federated Learning in Emerging Smart Networks, Digital Futures Faculty

+46 8 16 11 15
Picture of Robert Pilawa-Podgurski

Robert Pilawa-Podgurski

Associate Professor, Electrical Engineering and Computer Sciences University of California, Berkeley