About the project

Objective
Soil carbon sequestration in croplands has tremendous potential to help mitigate climate change; however, it is challenging to develop optimal management practices to maximise the sequestered carbon and crop yield. This project aims to develop an intelligent agricultural management system using deep reinforcement learning (RL) and large-scale soil and crop simulations. To achieve this, we propose to build a simulator to model and simulate the complex soil-water-plant-atmosphere interactions, which will run on high-performance computing platforms.

Background
Massive simulations using such platforms allow the evaluation of the effects of various management practices under different weather and soil conditions in a timely and cost-effective manner. By formulating the management decision as an RL problem, we can leverage the state-of-the-art algorithms to train management policies, which are expected to maximise the stored organic carbon while maximising the crop yield. The whole system will be tested using data on soil and crops in both the mid-west of the United States and the Mediterranean region. The proposed research has great potential to impact climate change and food security, two of humanity’s most significant challenges.

Crossdisciplinary collaboration
This project is a collaboration between the University of Illinois at Urbana-Champaign, KTH Department of Sustainable Development and Stockholm University, Department of Physical Geography.

About the project

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.