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Resilient and Probabilistic reliability management of the transmission grid

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The transmission grid is facing challenges due to increasing integration of variable renewable generation and increasing exchange of power between countries.

In the traditional (deterministic) approach to reliability management, grid operators often have to resort to grid development solutions to maintain an acceptable level of reliability.


However, such solutions may be exceedingly costly and not necessarily socio-economic efficient.


Using a probabilistic approach to long-term grid planning, balancing reliability and costs, the socio-economic optimal solution may be to rely more on actions that can be taken during grid operation:


  • preventive actions to better absorb disruptive events
  • corrective actions to better adapt to disruptive events
  • restorative actions to rapidly recover from disruptive events and restore power supply to end-users.

These are all capabilities associated with resilient grid operation.


The primary objective of the project is to develop methodology for the rapid identification of operational strategies including preventive, corrective and restorative actions that ensures transmission grid resilience. It thus seeks to address outstanding knowledge gaps related to the operational strategies that are a precondition for more probabilistic and resilient reliability management of the transmission grid. 


The project will focus on the rapid identification of near-optimal preventive actions by developing and critically evaluating novel and computationally efficient approximate models (proxy models) for grid operation. Furthermore, the project will also advance the research front in the direction of accounting more accurately for restoration time and restorative actions within a broader probabilistic reliability management framework.


The expected results of the project include methods, models and prototype tools that can be implemented in probabilistic reliability assessments tools to improve their accuracy and computational efficiency.



  • NTNU
  • Statnett
  • NVE
  • SINTEF Digital

This is a KPN-project (Knowledge-building Project for Industry) financed by the Research Council of Norway.


Key Factors

Project duration

2019 - 2023

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