Application Of The Genetic Algorithm Based On Simulated Annealing In Substation Locating

In this paper, methods of optimal substation positioning are studied. Main works as following. As modern power system is becoming more highly complex and paying more attention on its performance as a whole, more reasonable power planning is required to achieve more secure and economic power network operation, and to ensure power network construction along a scientific and reasonable direction. The location of substations will influence network structure. So in this paper optimal substation positioning is studied. The problem of optimal substation position is finding the best geographical coordinates of substation to be built to optimize the objective function with physical constraints, such as load demand, line's capacity, substation's capacity etc. As we know, the problem is a nonlinear objective function, and in order to solve the problem, the improved genetic simulated annealing algorithm is used in network reconfiguration. Taking a modified Simulated Annealing algorithm as a genetic operator realized the combination of the local searching ability of SA and global searching ability of GA. A new hybrid algorithm of Genetic Simulated Annealing had been designed with dynamic probability of crossover and mutation, and tested by a nonlinear function optimization, The results indicated the hybrid algorithm can improve significantly the efficiency of GA for solving nonlinear optimization.