Balanced Graph Partition for Resilient Load Restoration Against Natural Disasters

Microgrids (MGs) constitute a promising solution to enhance power distribution system (PDS) resilience against natural disasters. Yet, in the existing research works, the scale of MGs, which can be quantified by the number of nodes covered by MGs, has not been optimized. An MG containing too many nodes can certainly prolong the restoration process, because loads are typically picked up step by step for transient stability consideration. To this end, this paper proposes a load restoration strategy based on balanced graph partition. The problem is formulated as a multi-objective optimization problem. One objective is to minimize the total weighted load shed, while the other one is to consider the scale of MGs by minimizing the variance of the scale of MGs. To speed up the computation, a decomposition-based objective approximation is presented. The optimality gap is also derived, and proven to be bounded. Lastly, the proposed strategy is performed on the IEEE 37-Node and 123-Node Test Feeders. The simulation results demonstrate that by minimizing the variance of the scale of MGs, a more resilient load restoration can be achieved.