4.7 Article

Resilience of electric grid to extreme wind: Considering local details at national scale

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ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2022.109070

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Distribution grid; Resilience; Windstorm; Fragility functions

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A small number of strong windstorms can cause major power interruptions and potentially shift the strategic direction of energy system development. Existing analysis tools do not effectively capture the effects on distribution grids over large areas with diverse environments. This paper presents a national-scale power disruption model that integrates synthetically generated power grids, consumption profiles, fragility functions, and windstorm severity dependent fixing times to accurately reproduce historical outage data.
A small number of the strongest windstorms can account for a major share of power interruptions. In addition to large direct costs, such windstorms may shift the strategic direction of the energy system development. Thus, future system planning may benefit from a better understanding of the energy system resilience against the largest historical and historically unprecedented, but meteorologically plausible windstorms. Present analysis tools handling power disruptions tend to focus either on transmission grids or small distribution grids. However, neither focus captures well impacts concentrated on the distribution level over large area that includes high variety of grids and environments. This paper presents a fragility-based power disruption model against wind-storms on a national scale with details on the local medium voltage grid. The model integrates synthetically generated power grids and consumption profiles with fragility functions and windstorm severity dependent fixing times. Grids are generated by spatial mapping of the grid component totals for each distribution grid operator onto municipalities and assuming symmetrical grid topologies within the municipalities. The model is applied to reproduce the national lost load profile for year 2011 winter windstorms in Finland. The modeled profile reproduces the historical reference data with a RMSE of 9% of the outage peak.

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