Reconfiguration for Distribution Networks

Distribution network reconfiguration serves as a crucial technical approach for optimizing power system operations by adjusting network topology to reduce line losses, enhance power supply reliability, or balance loads. Genetic algorithm-based reconfiguration methods effectively handle such complex combinatorial optimization problems.

Genetic algorithms simulate natural selection and genetic mechanisms from biological evolution, making them particularly suitable for solving distribution network reconfiguration problems characterized by numerous discrete variables and nonlinear constraints. The algorithm encodes network topology as chromosomes, with the initial population randomly generating multiple feasible solutions. During iteration, a fitness function (typically targeting minimal power loss) evaluates solution quality, preserving superior individuals while performing crossover and mutation operations to produce new generations.

MATLAB provides convenient genetic algorithm toolbox support for implementation, allowing customization of encoding methods, fitness calculations, and genetic operations. For distribution network applications, constraint handling requires special attention - including radial structure maintenance and voltage limits - typically incorporated into fitness functions through penalty function methods or repair strategies. The optimal topology output after algorithm convergence constitutes the reconfiguration scheme.

Compared to traditional mathematical programming methods, genetic algorithms demonstrate stronger global search capabilities without relying on gradient information, though they require balancing computational efficiency and solution quality. Practical applications often combine heuristic rules or local search for improvements. This intelligent optimization approach provides an effective tool for dynamic reconfiguration of complex distribution systems.