Research On Intelligent Distribution Network Planning With Ant Colony Algorithm

Distribution network planning is an important component of power system planning. Thegreat economic and social benefits will be achieved by the aid of scientific planning andoptimal power network investment decision. Distribution network planning is a nonlinear,multi-objective, multi-constraint optimal problem, and is associated with flow calculation ofthe line routing, reactive power compensation, network-loss and power reliability etc.Using traditional grid optimization method to solve distribution network planningproblem, will make the algorithm into a local optimum solution. In recent years, modernheuristic algorithms are widely used in various fields and have achieved good results. Thesealgorithms have the characteristics of global optimization ability and versatility. In this paper,use improved ant colony algorithm for distribution network planning with the basic idea ofthe ant colony algorithm. Studies show that the algorithm is effective for distribution networkplanning.Based on the specific characteristics of the urban distribution network planning problem,this paper reformulates the basic ant colony algorithm including the state transfer probability,the pheromone initialization and pheromone update. The reformulated method can improvethe algorithm speed and convergence rate. Using real streets as example of distributionnetwork model, the distribution network is subject to radiate restriction. In this strategy,directive factor is introduced when the ant choices a street path that can improve theefficiency of distribution network planning. This proposed distribution network planningmethod has much more meanings for actual planning work.Finally, Programming examples using Matlab software, planning results are obtained.The results show that the improved ant colony algorithm of this paper is reliable and useful inthe distribution network planning. Under the premise of ensuring the calculation speed, thismethod can completed the task of distribution network planning, thereby saving the cost ofdistribution network planning.