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

Objective
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-efficient control policies that 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 learn to optimize their interactions with the complex grid environment automatically and achieve a smooth integration of renewables without voltage security violations, thus promoting a climate-neutral society.

Background
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 rise in voltage 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. We believe 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 the Digitalize in Stockholm 2023 event: