Coordinating Multiple Sources for Service Restoration to Enhance Resilience of Distribution Systems

TL;DR

This paper introduces a two-stage decision-making method for optimizing the restoration of critical loads in distribution systems after outages, utilizing multiple sources to improve resilience and operational efficiency.

Contribution

It presents a novel two-stage optimization approach combining topology decision and load restoration, employing mixed-integer semidefinite programming for enhanced accuracy and efficiency.

Findings

Method effectively restores critical loads in test feeders

Achieves near-global optimal solutions efficiently

Enhances distribution system resilience after outages

Abstract

When a major outage occurs on a distribution system due to extreme events, microgrids, distributed generators, and other local resources can be used to restore critical loads and enhance resiliency. This paper proposes a decision-making method to determine the optimal restoration strategy coordinating multiple sources to serve critical loads after blackouts. The critical load restoration problem is solved by a two-stage method with the first stage deciding the post-restoration topology and the second stage determining the set of loads to be restored and the outputs of sources. In the second stage, the problem is formulated as a mixed-integer semidefinite program. The objective is maximizing the number of loads restored, weighted by their priority. The unbalanced three-phase power flow constraint and operational constraints are considered. An iterative algorithm is proposed to deal with integer variables and can attain the global optimum of the critical load restoration problem by solving a few semidefinite programs under two conditions. The effectiveness of the proposed method is validated by numerical simulation with the modified IEEE 13-node test feeder and the modified IEEE 123-node test feeder under plenty of scenarios. The results indicate that the optimal restoration strategy can be determined efficiently in most scenarios.