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Efficient reconfiguration of distribution networks using extended pruning-grafting operators

Abstract:

Network reconfiguration is a complicated, combinatorial, constrained optimization problem with many candidate switching options as well as structural and operational constraints. Introduction of evolutionary algorithms (EAs) to distribution network operation has opened many new opportunities. However, many applications of these methods suffer from high computational burden. In addition, conventional crossover/mutation operators cannot generally produce radial configurations. Performance of EAs is significantly affected by modeling of the problem and the employed operators. This paper employs a branch-based modeling of a distribution network and proposes two new EA operators that are an extension and redefinition of the preserve ancestor operator (PAO) and change ancestor operator (CAO). They are fast, exclusively produce radial configurations, and remove PAO/CAO operators' limitation. Hence, they can be utilized for a more efficient application of EAs to the network reconfiguration problem. Performance of the new operators is compared to the original PAO/CAO operators, two sets of operators in a binary representation (conventional crossover/mutation operators and an enhanced version of them), and a set of operators in an integer representation (conventional crossover and directed mutation operators). Simulations show the efficiency of the proposed method in terms of convergence speed, response time, and the quality of results.