Multi-stage Resilience Management of Smart Power Distribution Systems: A Stochastic Robust Optimization Model

TL;DR

This paper introduces a multi-stage stochastic robust optimization model for enhancing the resilience of smart power distribution systems against extreme weather events, integrating operational measures with hardening strategies under uncertainty.

Contribution

It develops a novel SRO model that jointly optimizes hardening and operational measures considering uncertainties from weather and recovery times.

Findings

Optimal hardening decisions are significantly affected by operational measures.

The model effectively captures uncertainties from hurricanes and repair times.

Application to IEEE systems demonstrates practical resilience improvements.

Abstract

Significant outages from weather and climate extremes have highlighted the critical need for resilience-centered risk management of the grid. This paper proposes a multi-stage stochastic robust optimization (SRO) model that advances the existing planning frameworks on two main fronts. First, it captures interactions of operational measures with hardening decisions. Second, it properly treats the multitude of uncertainties in planning. The SRO model coordinates hardening and system operational measures for smart power distribution systems equipped with distributed generation units and switches. To capture the uncertainty in the incurred damage by extreme events, an uncertainty set is developed by integrating probabilistic information of hurricanes with the performance of overhead structures. A novel probabilistic model for the repair time of damaged lines is derived to account for the uncertainty in the recovery process. A solution strategy based on the integration of a differential evolution algorithm and a mixed-integer solver is designed to solve the resilience maximization model. The proposed approach is applied to a modified IEEE 33-bus system with 485 utility poles and a 118-bus system with 1841 poles. The systems are mapped on the Harris County, TX, U.S. Results reveal that optimal hardening decisions can be significantly influenced by resilience operational measures.