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Bridging between Load-Flow and Kuramoto-like Power Grid Models: A Flexible Approach to Integrating Electrical Storage Units

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arxiv 1812.01972 v1 pith:YVZPBHMB submitted 2018-12-05 nlin.AO

classification nlin.AO
keywords electricalgridpowerstoragekuramoto-likemodelsbridgingcomponents
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In future power systems, electrical storage will be the key technology for balancing feed-in fluctuations. With increasing share of renewables and reduction of system inertia, the focus of research expands towards short-term grid dynamics and collective phenomena. Against this backdrop, Kuramoto-like power grids have been established as a sound mathematical modeling framework bridging between the simplified models from nonlinear dynamics and the more detailed models used in electrical engineering. However, they have a blind spot concerning grid components, which cannot be modeled by oscillator equations, and hence do not allow to investigate storage-related issues from scratch. We remove this shortcoming by bringing together Kuramoto-like and algebraic load-flow equations. This is a substantial extension of the current Kuramoto framework with arbitrary grid components. Based on this concept, we provide a solid starting point for the integration of flexible storage units enabling to address current problems like smart storage control, optimal siting and rough cost estimations. For demonstration purpose, we here consider a wind power application with realistic feed-in conditions. We show how to implement basic control strategies from electrical engineering, give insights into their potential with respect to frequency quality improvement and point out their limitations by maximum capacity and finite-time response.

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  1. Heterogeneities in electricity grids strongly enhance non-Gaussian features of frequency fluctuations under stochastic power input

    nlin.AO 2019-08 conditional novelty 6.0 of 10

    In a heterogeneous power grid model, stochastic wind injection produces heavy-tailed frequency fluctuations that are strongest at weakly connected nodes and scale linearly with mean injected wind power.

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