The function of infrastructure networks — such as transportation, supply or communication — strongly depends on reliable transmission flows. Rapidly increasing or reshaping the network structure can strengthen its overall stability, but it can also induce traffic jams or network collapses. One example of such a counterintuitive observation is known as Braess’s paradox, which was first observed in traffic networks: adding new roads to a road network can in fact hinder the overall traffic flow. Theoretical studies in the past have suggested possible occurrences of this scenario in electrical power grids and biological systems too. Especially for power networks, a substantial increase in transmission capacities due to the extension of renewable power sources can lead to new threats and risks. While previous studies have reported Braess’s paradox in simulated data, its experimental confirmation, an analysis of the underlying processes leading to its appearance, as well as possible tools for mitigation of its negative effects, are still lacking. Until now.
In a recent work, Benjamin Schäfer, Marc Timme and colleagues demonstrated that Braess’s paradox exists in real-world power grids by means of lab experiments with synchronous and virtual-synchronous machines, which offer a functional analogy to large-scale power grids. Next, the authors introduced a method, based on the topological properties of the network, for detecting Braessian edges — network edges where power flow damages may emerge after grid extension, thus reducing the overall grid performance. Generally speaking, an edge is Braessian if increasing its capacity induces an increase of load at the maximally loaded edge of the grid. The authors then applied their method to analyze a German high-voltage grid, where they detected two potential planned lines that could induce Braess’s paradox. The proposed method paves the way for analyzing the potential impact of Braess’s paradox on the planned extensions of power transmission grids, which ultimately improves the understanding of network changes that may strengthen network performance and avoid damages.
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