Unlocking Innate Computing Abilities in Electric Grids
For the energy and computing sectors, this work proposes a novel paradigm where existing power grids can serve as sustainable computational substrates, potentially reducing the energy footprint of data centers.
The authors demonstrate that electric power grids can perform information processing tasks, such as affine transformations, by encoding data into operational setpoints of power electronic converters, without modifying grid architectures. This reveals inherent computational capabilities in grid physics.
Electric power grids are engineered energy systems whose forward electrical responses embody high-dimensional and memory-bearing transformations of input signals. In this work, we reveal that these transformations-inherent in electric circuit elements, power flows and network topologies-can be conveniently harnessed for computation without modifying physical grid architectures. By encoding structured input data into the operational setpoints of power electronic converters inside grids, we demonstrate how forward grid dynamics are interpreted into physical representations comprising system variables by showcasing through an affine transformation example implemented on a direct-current (DC) grid, which justifies the capability of grids performing information processing tasks concurrently alongside normal power flows. Our work not only underscores the computation capability intrinsic to grid physics, but also opens a new perspective on how energy networks can function as sustainable computational substrate. This positions them as flexible assets where several computing tasks from data centers can be sustainably outsourced.