Operation

Today’s electricity grid is a highly interconnected system—arguably the most complex ever created by humans—where electrical power is generated, transmitted, and consumed. For the system operator to ensure stable and efficient grid operation, both generation and consumption must be in equilibrium at all times. While electricity consumption is stochastic by nature, integrating renewable energy sources—mainly distributed energy resources (DER) to distribution grids—adds further uncertainty and complexity to grid operation. Therefore, the lab’s study on grid operation focuses on the task of always keeping up this equilibrium with the given resources and infrastructure.

Achieving this first requires a deep understanding of the characteristics of resources and infrastructure, as developing models that capture these features is fundamental to efficient decision-making. Examples of such modelling studies from the lab include (i) quantifying the probability distributions of large-scale renewables on the generation side, (ii) modelling power flow behaviour on the transmission and distribution sides, and (iii) quantifying flexibility on the demand side, among others.

Once we gain knowledge of the assets, controlling them for optimal operation becomes possible. Building on this, the lab focuses on developing algorithms for controlling the newly integrated DER and electric loads, such as heat pumps and electric vehicles. With the digitalization of the grid, system operators or other entities can leverage the flexibility of these devices for different services, such as congestion management in low-voltage grids, providing ancillary services to the transmission grid or shifting demand to hours of high renewable production by participating in the wholesale market. While classical control and optimization theory are established tools in power system research, we also investigate using data-driven methods. In addition to the theoretical work, our group is also involved in different pilot projects, where our methods are investigated using today’s smart grid infrastructure and digital twins. We want to close the gap between fundamental research and application, identifying possible perspectives and limitations.