Research On Application Ant Colony Algorithm To Distribution Network Planning

Distribution network planning is an important component of power system planning. The great economic and social benefits will be achieved by the aid of scientific planning and optimal power network investment decision. In order to supply abundant and high-qualified power to customers, low-voltage (LV) biomass distribution network planning needs to provide a powerful and flexible scheme based on the substation capacity and the load capacity. To sum up, distribution network planning is a nonlinear, multi-objective, multi-constraint optimal problem, and is associated with the radiate power line routing, network-loss and power supply reliability etc.The traditional Ant Colony Algorithm is an algorithm that simulates evolution, used to solve complex linear and nonlinear optimization heuristic algorithm. It was originally obtained the solution in good results applying to the traveling salesman problem, quadratic assignment problem and shop scheduling problem. Now ant colony optimization algorithm has been successful in the motor optimal design, function optimization problem and the areas of integrated circuit routing problem because of its good performance.In this paper, first we investigate the research status of the basic ant colony algorithm and study the principle and mathematical model of ant colony algorithm in depth. The mathematical model of distribution network planning and Constraints and objective function was obtained based analyzing the status of distribution network planning in and abroad. After analyzing the radiate and connective characters of distribution network and the state transfer principles and the pheromone initialization and pheromone update, this paper reformulates the existing Improved Ant Colony Optimization Algorithm (IACOA), on which a new approach for the LV distribution network planning applied in real streets is created. The reformulated method can obtain a satisfied solution in extending searching scope and better computing speed for distribution network Planning.Comparing IACOA with typical problems genetic algorithms and ant colony system algorithm used TSP of 51 cities, the experimental results show that the IACOA algorithm is better than this genetic algorithm and ant colony system algorithm in global search ability and fast operation to avoid falling local optimal value, The IACOA algorithm is more suitable for solving un-linear programming problems showing a strong optimization properties. IACOA algorithm is applied to the biomass power plant low-voltage distribution network planning problem of 72 load points and six power station, the real tests prove the practicability and validity of this paper method.