Distribution Network Reconfiguration: A Reproducible Benchmark of Heuristic, Metaheuristic and Mathematical Methods

Distribution network reconfiguration (DNR) has been studied for five decades, yet published algorithms are rarely compared under identical assumptions: authors report results on different test systems, with different power-flow models, and almost never release code. This paper re-implements eight representative DNR algorithms in a single open-source Python framework and benchmarks them on five networks under identical conditions. The algorithms span three of the four established paradigms: heuristics (loop cutting, branch exchange, greedy minimum spanning tree, exhaustive search), metaheuristics (a genetic algorithm and a selective binary particle swarm optimizer) and mathematical programming (a mixed-integer quadratic program built on a convex, simplified DistFlow power-flow model). We report power losses, voltage profile, and, as a hardware-independent cost metric, the number of power-flow evaluations required for convergence. On the 196-bus Simbench urban network, we show that a dynamic DNR policy can save more than 26% of the system losses, weighed against circuit-breaker maintenance cost. All implementations, test cases, and result scripts are released to allow direct extension and replication