Graph-Theoretic Ant Colony Optimization for Dynamic Distribution Network Reconfiguration with High-Penetration Renewable Energy Sources and Battery Energy Storage Systems

High-penetration integration of Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs) has transformed Distribution Network Reconfiguration (DNR) from a static topology optimization task into a complex combinatorial problem with strong coupling between discrete switch decisions and dynamic power flow constraints. This evolution requires that optimization algorithms provide two capabilities: native adaptability to discrete decision variables without approximation, and real-time responsiveness to dynamic operating conditions. Ant Colony Optimization (ACO), as the most widely applied discrete-native Swarm Intelligence (SI) algorithm, faces three critical bottlenecks in DNR due to its Traveling Salesman Problem (TSP)-oriented design: framework incompatibility, ambiguous heuristic formulation, and ineffective pheromone strategies. To address these limitations, this study proposes a Graph-Theoretic Ant Colony Optimization (GTACO) algorithm. Multi-scenario experiments on IEEE 33-bus and PG&E 69-bus systems demonstrate that GTACO outperforms state-of-the-art algorithms in core metrics including loss reduction rate, voltage stability, convergence efficiency, and economic–environmental benefits. This research overcomes the TSP-centric limitations of conventional ACO, establishes a methodological foundation for extending the ACO framework to complex non-TSP discrete optimization tasks, and provides a practical solution for dynamic DNR under high-penetration RES and BESS integration.