Distribution networks are being transformed by the growing penetration of distributed generation (DG), which changes power flows, voltage profiles, and the optimal operating point of the feeder. This study proposes a hybrid technique that combines the Gray Wolf Optimizer (GWO) with a neural network (NN) surrogate model to solve the distribution network reconfiguration (DNR) problem. The method minimizes active power losses while improving voltage regulation and preserving radial operation under operational constraints. The GWO performs global exploration of discrete switch configurations, whereas the NN accelerates local refinement by screening candidates before exact AC power flow validation. This manuscript presents benchmark results for the IEEE 33-bus and IEEE 69-bus distribution test systems. For the IEEE 33-bus benchmark, DG units are installed at buses 14, 25, and 30. For the IEEE 33-bus case, losses are reduced from 282.94 kW in the base case to 120.65 kW with DG and to 87.08 kW after hybrid reconfiguration, while the minimum voltage magnitude improves from 0.8829 p.u. to 0.9587 p.u. For the IEEE 69-bus case, total active losses decrease from 224.95 kW to 82.22 kW with DG and to 29.92 kW after reconfiguration while concurrently improving the voltage profile and line loading. From a sustainability perspective, the main contribution of the proposed workflow is to reduce technical losses at the distribution level, thereby improving energy efficiency for a given demand. Overall, the results show that the combined use of DG and surrogate-assisted reconfiguration can yield substantial efficiency gains across benchmark feeders of varying sizes, while broader multi-feeder validation and more detailed surrogate error quantification remain necessary before claiming general applicability.
Corrales et al. (2026) studied this question.