An efficient genetic algorithm for optimal large-scale power distribution network planning

This paper presents a new efficient genetic algorithm for optimal large-scale power distribution network planning. The algorithm finds the best location and size of substations and feeders to minimize a cost function of the network, which represents investment (fixed cost) and operational costs (nonlinear variable costs). The main advantage of the algorithm over other genetic algorithms is its capability to overcome the problems of low heritability and topological infeasibility, resulting in reduced solution times. An effective representation of the candidate solutions was used and specialized genetic operators were introduced. The algorithm was tested on three networks and the results were compared with the results from other methods. From this comparison, we concluded that the proposed genetic algorithm is more efficient than several methods presented before and it is suitable to resolve the problem of real large-scale power distribution network planning.