A multi-objective evolutionary approach for planning and optimal condition restoration of secondary distribution networks

Abstract

A secondary distribution network (SDN), corresponding to the final user low voltage distribution circuit, is continuously growing due to a persistent increase in load demand. Consequently, the performance of any optimized design will inevitably degrade over time. To avoid the associated repercussions such as faults, congestion, voltage drops, and other major quality issues, we are eventually prompted to redesign this part of the grid. To do so, we propose a Two-Stage Multi-Objective Evolutionary Approach (TS-MOEAP), which is able to find a new optimal network configuration, circumventing the associated quality issues. The proposed approach is oriented to improve the performance of SDNs by combining the concepts of network reconfiguration (NR) and optimal placement of distribution transformers (DTs). Due to the large and complex topology of SDNs, we deal with a hard combinatorial, non-convex, and nonlinear optimization problem. Consequently, to facilitate the resolution of the problem, the proposal is divided into two stages: (1) optimal placement and sizing of distribution transformers, as well as conductor sizing and branch routing, and (2) optimal network reconfiguration. For the first stage, an improved particle swarm optimization technique (IPSO) combined with a greedy algorithm is used, and for the second stage, an improved nondominated sorting genetic algorithm with a heuristic mutation operator (NSGA-HO) is implemented. The approach redesigns SDNs by minimizing total power loss and investment costs while satisfying quality issues and technical constraints. The proposed approach is validated by improving a real-life SDN with critical quality and technical issues. We also compare the results with respect to other state-of-the-art algorithms.