Determination of Optimal Size and Number of Movable Energy Resources for Distribution System Resilience Enhancement

TL;DR

This paper introduces a graph theory and combinatorial enumeration-based method to optimally size and position movable energy resources for enhancing distribution system resilience after extreme events.

Contribution

It presents a novel approach combining scenario reduction, network reconfiguration, and combinatorial analysis to determine optimal MER deployment for resilience improvement.

Findings

The method effectively identifies optimal MER configurations with minimal load curtailment.

Validation on a 33-node system demonstrates the approach's practical applicability.

The approach improves recovery speed and resilience of distribution systems.

Abstract

This paper proposes an approach based on graph theory and combinatorial enumeration for sizing of movable energy resources (MERs) to improve the resilience of the electric power supply. The proposed approach determines the size and number of MERs to be deployed in a distribution system to ensure the quickest possible recovery of the distribution system following an extreme event. The proposed approach starts by generating multiple line outage scenarios based on fragility curves of distribution lines. The generated scenarios are reduced using the k-means method. The distribution network is modeled as a graph where distribution network reconfiguration is performed for each reduced line outage scenario. The combinatorial enumeration technique is used to compute all combinations of total MER by size and number. The expected load curtailment (ELC) corresponding to each locational combination of MERs is determined. The minimum ELCs of all combinations of total MER are used to construct a minimum ELC matrix, which is later utilized to determine optimal size and number of MERs. The proposed approach is validated through a case study performed on a 33-node distribution test system.