Optimal power flow in distribution networks: Reconfiguration and self-healing via Benders’ decomposition

Electric power systems are undergoing rapid evolution driven by increasing loads, widespread renewable energy integration, distributed generation, sector liberalization, and the rise of emerging technologies like electric vehicles. These transformations necessitate intelligent and efficient management of distribution networks, marking the transition to Smart Grids. This study introduces a novel optimization framework utilizing Benders’ Decomposition to tackle network reconfiguration and self-healing challenges in medium-voltage distribution networks during contingency scenarios. The proposed methodology supports decision-making by optimizing network topology and balancing supply-demand dynamics, minimizing operational costs while ensuring system resilience and reliability. Key contributions include the development of a robust tool capable of delivering optimal reconfiguration solutions with low computational latency, adaptable to networks of various sizes and topologies. Simulations on both 13-bus and 180-bus networks demonstrated the model’s scalability and effectiveness, ensuring operational continuity even under severe contingencies. Additionally, this approach accommodates modern network elements such as energy storage systems, electric vehicle charging infrastructure, and distributed renewable generation, enabling a comprehensive Smart Grid framework. The study highlights the potential for integrating this tool into real-time operational systems, ensuring proactive network management and enhanced resilience.