A framework for synthetic power system dynamics

Büttner, Anna; Plietzsch, Anton; Anvari, Mehrnaz; Hellmann, Frank

doi:10.1063/5.0155971

Chaos: An Interdisciplinary Journal of Nonlinear Science

2023

DOI: 10.1063/5.0155971

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A framework for synthetic power system dynamics

Anna Büttner

Anton Plietzsch

Mehrnaz Anvari

et al.

Abstract: We present a modular framework for generating synthetic power grids that consider the heterogeneity of real power grid dynamics but remain simple and tractable. This enables the generation of large sets of synthetic grids for a wide range of applications. For the first time, our synthetic model also includes the major drivers of fluctuations on short-time scales and a set of validators that ensure the resulting system dynamics are plausible. The synthetic grids generated are robust and show good synchronizatio… Show more

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2024

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Cited by 3 publications

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Robustness of coupled networks with multiple support from functional components at different scales

Dong,

Sun,

Yan

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Robustness is an essential component of modern network science. Here, we investigate the robustness of coupled networks where the functionality of a node depends not only on its connectivity, here measured by the size of its connected component in its own network, but also the support provided by at least M links from another network. We here develop a theoretical framework and investigate analytically and numerically the cascading failure process when the system is under attack, deriving expressions for the proportion of functional nodes in the stable state, and the critical threshold when the system collapses. Significantly, our results show an abrupt phase transition and we derive the minimum inner and inter-connectivity density necessary for the system to remain active. We also observe that the system necessitates an increased density of links inside and across networks to prevent collapse, especially when conditions on the coupling between the networks are more stringent. Finally, we discuss the importance of our results in real-world settings and their potential use to aid decision-makers design more resilient infrastructure systems.

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Robustness of coupled networks with multiple support from functional components at different scales

Dong,

Sun,

Yan

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

show abstract

Recent achievements in nonlinear dynamics, synchronization, and networks

Ghosh,

Marwan,

Small

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior in dynamical networks. It targets current progress on issues such as time series analysis and data-driven modeling from real data such as climate, brain, and social dynamics. Predicting and detecting early warning signals of extreme climate conditions, epileptic seizures, or other catastrophic conditions are the primary tasks from real or experimental data. Exploring machine-based learning from real data for the purpose of modeling and prediction is an emerging area. Application of the evolutionary game theory in biological systems (eco-evolutionary game theory) is a developing direction for future research for the purpose of understanding the interactions between species. Recent progress of research on bifurcations, time series analysis, control, and time-delay systems is also discussed.

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Complex Couplings - A Universal, Adaptive, and Bilinear Formulation of Power Grid Dynamics

Büttner,

Hellmann

2024

PRX Energy

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The energy transition introduces new classes of dynamical actors into the power grid. There is, in particular, a growing need for so-called grid-forming inverters (GFIs) that can contribute to dynamic grid stability as the share of synchronous generators decreases. Understanding the collective behavior and stability of future grids, featuring a heterogeneous mix of dynamics, remains an urgent and challenging task. Two recent advances in describing such modern power grid dynamics have made this problem more tractable. First, the normal form for grid-forming actors provides a uniform technology-neutral description of plausible grid dynamics, including grid-forming inverters and synchronous machines. Second, the notion of the complex frequency has been introduced to effortlessly describe how the nodal dynamics influence the power flows in the grid. The major contribution of this paper is to show how the normal-form approach and the complex-frequency dynamics of power grids combine and how they relate naturally to adaptive dynamical networks and control-affine systems. Using the normal form and the complex frequency, we derive a remarkably elementary and universal equation for the collective grid dynamics. Notably, we obtain an elegant equation entirely in terms of a matrix of complex couplings, in which the network topology does not appear explicitly. These complex couplings give rise to new adaptive network formulations of future power grid dynamics. We give a new formulation of the Kuramoto model, with inertia as a special case. Starting from this formulation of the grid dynamics, the question of the optimal design of future grid-forming actors becomes treatable by methods from affine and bilinear control theory. We demonstrate the power of this perspective by deriving a quasilocal control dynamics that can stabilize arbitrary power flows, even if the effective network Laplacian is not positive definite. Published by the American Physical Society 2024

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A framework for synthetic power system dynamics

Cited by 3 publications

References 42 publications

Robustness of coupled networks with multiple support from functional components at different scales

Robustness of coupled networks with multiple support from functional components at different scales

Recent achievements in nonlinear dynamics, synchronization, and networks

Complex Couplings - A Universal, Adaptive, and Bilinear Formulation of Power Grid Dynamics

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