Forward-Backward Sweep Method for Radial Distribution Network Power Flow with Algorithm Implementations

The radial distribution network power flow program based on the forward-backward sweep method is a commonly used power system analysis tool. This program simulates current and power flow in electrical systems to evaluate and optimize distribution network performance. In our implementation, we utilize ant colony optimization (ACO) to solve power flow problems, where the algorithm mimics ant foraging behavior through pheromone trail updates and probabilistic path selection to find optimal solutions. The particle swarm optimization toolbox (PSOt) is integrated to enhance computational efficiency through velocity and position updates of particles in the solution space. Additionally, genetic algorithm (GA) is employed for reactive power optimization, implementing selection, crossover, and mutation operations to simulate natural evolution processes for optimal parameter tuning. Finally, we incorporate a multi-swarm chaotic ant colony algorithm to solve unit commitment problems, where chaotic sequences introduce randomness to improve global search capability while multiple ant colonies enhance solution diversity. The program source code provides comprehensive implementations of these optimization techniques including ACO-based power flow calculation, PSOt-accelerated performance optimization, GA-driven reactive power control, and multi-swarm chaotic optimization for unit commitment scheduling.