A real-time distributed post-disaster restoration planning strategy for distribution networks

TL;DR

This paper introduces a real-time, centralized-distributed bi-level optimization approach for post-disaster restoration of distribution networks, combining genetic algorithms and distributed model predictive control to enhance decision speed and accuracy.

Contribution

It presents a novel Aitken-DMPC solver that accelerates convergence, enabling effective real-time restoration planning in distribution networks after disasters.

Findings

Aitken-DMPC significantly outperforms standard DMPC in convergence speed.

The proposed method effectively handles real-time changes during restoration.

Case study demonstrates improved restoration efficiency on IEEE 123-bus system.

Abstract

After disasters, distribution networks have to be restored by repair, reconfiguration, and power dispatch. During the restoration process, changes can occur in real time that deviate from the situations considered in pre-designed planning strategies. That may result in the pre-designed plan to become far from optimal or even unimplementable. This paper proposes a centralized-distributed bi-level optimization method to solve the real-time restoration planning problem. The first level determines integer variables related to routing of the crews and the status of the switches using a genetic algorithm (GA), while the second level determines the dispatch of active/reactive power by using distributed model predictive control (DMPC). A novel Aitken- DMPC solver is proposed to accelerate convergence and to make the method suitable for real-time decision making. A case study based on the IEEE 123-bus system is considered, and the acceleration performance of the proposed Aitken-DMPC solver is evaluated and compared with the standard DMPC method.