Oscillatory spreading and inertia in power grids

Abstract

The increase in variable renewable generators (VRGs) in power systems has altered the dynamics from a historical experience. VRGs introduce new sources of power oscillations, and the stabilizing response provided by synchronous generators (SGs, e.g., natural gas, coal, etc.), which help avoid some power fluctuations, will lessen as VRGs replace SGs. These changes have led to the need for new methods and metrics to quickly assess the likely oscillatory behavior for a particular network without performing computationally expensive simulations. This work studies the impact of a critical dynamical parameter—the inertia value—on the rest of a power system’s oscillatory response to representative VRG perturbations. We use a known localization metric in a novel way to quantify the number of nodes responding to a perturbation and the magnitude of those responses. This metric allows us to relate the spread and severity of a system’s power oscillations with inertia. We find that as inertia increases, the system response to node perturbations transitions from localized (only a few close nodes respond) to delocalized (many nodes across the network respond). We introduce a heuristic computed from the network Laplacian to relate this oscillatory transition to the network structure. We show that our heuristic accurately describes the spread of oscillations for a realistic power-system test case. Using a heuristic to determine the likely oscillatory behavior of a system given a set of parameters has wide applicability in power systems, and it could decrease the computational workload of planning and operation.

Time-varying synergy/redundancy dominance in the human cerebral cortex

“…Relatedly, large subset sizes ( ≥ 55) heavily incorporated the limbic system, a system known to have poor signal to noise ratio in BOLD data. This may indicate that the local O-information is highly sensitive to noise, a possibility which has been previously reported in the literature for the time-averaged form of the O-information [66]. We suggest that the local O-information’s sensitivity to noise may be exaggerated when it is used on subsets that are larger than the true scale of interaction for the system.…”