Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust...

Area of Science:

• Electrical Engineering
• Operations Research
• Climate Science

Background:

• Increasing frequency of natural hazards causes power grid disruptions, leading to infrastructure damage and cascading failures.
• Enhancing power system resilience is crucial, requiring advanced technology and optimization methods.
• Typhoons and other natural hazards pose significant threats to grid stability.

Purpose of the Study:

• To propose a data-driven spatial distributionally robust optimization (DS-DRO) model for improving power system resilience.
• To optimize the installation and dispatch of distributed energy resources (DERs) to withstand natural hazard impacts.
• To develop a strategy for resilience enhancement against uncertain natural hazard effects, specifically typhoons.

Main Methods:

• Development of an accurate spatial model to assess component failure probability based on wind speed.
• Construction of a moment-based ambiguity set for failure distribution using historical typhoon data.
• Formulation of a two-stage DS-DRO model solved using dual reformulation and a column-and-constraints generation algorithm.

Main Results:

• The proposed DS-DRO model effectively plans the optimal placement and dispatch of DERs for resilience.
• The spatial model accurately predicts component failure probabilities under varying wind speeds.
• The methodology was validated on a modified IEEE 13-node reliability test system in the Hong Kong region.

Conclusions:

• The data-driven spatial distributionally robust optimization approach significantly enhances power system resilience against natural hazards.
• Optimized deployment of distributed energy resources is a key strategy for mitigating typhoon-induced grid disruptions.
• The employed optimization algorithms effectively solve complex resilience enhancement problems.