Monitoring

Power System Monitoring

Local power grids are dynamic and difficult to predict systems, often lacking detailed information about the network. Traditional methods of monitoring these grids are computationally demanding, especially for large systems. We investigate quantum computer approaches to overcome computational bottlenecks in state-of-the art power system monitoring using dynamic state estimation.

Our approach focuses on two directions: the application side and the quantum computing side. In this way, we will exploit the specific structure of the application problem to develop tailored quantum approaches to improve overall performance.

The changing dynamic of the power system caused by the increased role of distributed resources is highlighting more and more the role of the distribution grid for energy management across all grid levels. In this context, the requirements and possibilities for grid monitoring are increasing.

As distribution grids are organized in numerous substations to deliver power from the transmission grid to the end consumers, the number of nodes is vastly higher than at transmission level. Furthermore, load behavior is more difficult to predict, dynamics are faster, and monitoring and visibility are still very limited, impeding the applicability of traditional monitoring methods widely used at transmission level.

To solve these problems, our approach combines the application side and the quantum computing side, where the structure of the application is being studied to explore the potential and develop tailored quantum algorithms to improve the overall performance.

The overall goal is to study the potential of the quantum methods, to develop modifications serving the purposes of distribution grid monitoring and to outline limitations and challenges in the application of quantum methods for power systems.