Application Of New Ant Colony Algorithm In Transmission Network Expansion Planning

Power industry is a vital economic sector and a crucial driving force of modern social development, with the rapid development of the national economy and rising living standards, the electricity demand of society is increasing considerably. Meanwhile, the construction of power industry involves a large number of primary energy consumption and large investment and reasonable power system planning can gain a great economic and social benefits. In contrast, errors in power system planning can cost irreparable loss of national construction, which makes power system planning an urgent objection.Power distribution network planning is a very complex and combinatorial optimization problem, which is demanding in calculation. The traditional heuristic methods and mathematical optimization method is applied to solve the problem of network planning, although some breakthroughs have been made in practical work, the fact that contains disaster of dimensionality, locally optimization, objective function and constraints, and difficulty to deal with target function still exist. Artificial ant colony algorithm is an artificial intelligence, which is characterized by positive feedback, distributed computing, and full of constructive use of the greedy heuristic search. Ant colony optimization is particularly suitable for solving the integer variables, which leads a new solution of power distribution network planning. Until recently, ant colony algorithm and its various improved method have been applied in solving power distribution network expansion planning problem, which has achieved good results. And it also showed some advantages, which is a promising method.Considering its tendency of falling into stagnation and local optimum drawbacks when adopting traditional method, ant colony algorithm is improved in this paper. By constructing the new pheromone release function and speeding up the optimum path of pheromones in positive feedback process, search efficiency of ant colony algorithm is improved. In application stage, with single-stage distribution network model, calculating power flow trend by using DC load flow equations and improvement on line encoding regarding to its characteristics have prevented its tendency of falling into stagnation. In the end of this paper, with three simulation samples, the test results validate the new algorithm for its effectiveness on distribution network planning by using MATLAB as a platform.