Optimal Flow-Based Distribution Network Reconfiguration
A distribution network reconfiguration program using optimal flow algorithms, implementing power flow calculations with forward-backward sweep method
Integration of Multi-Agent Learning, Coordination Strategies, and Particle Swarm Optimization
By integrating multi-agent learning, coordination strategies, and particle swarm optimization, we propose a novel distribution network reconfiguration method based on multi-agent particle swarm optimization. This approach leverages the topological structure of particle swarm algorithms to construct a multi-agent system architecture, where each particle functions as an agent that competes and cooperates with neighboring agents. The methodology enables faster and more precise convergence to global optimal solutions. The update rules for particles reduce the generation of infeasible solutions and enhance algorithm efficiency. Experimental results demonstrate that this method achieves high search efficiency and optimization performance. The implementation involves designing agent interactions, defining fitness functions, and optimizing velocity update mechanisms.
Reconfiguration for Distribution Networks
Distribution Network Reconfiguration Using Genetic Algorithms