High-voltage Transmission Network Planning Method And Algorithm Research

Transmission grid is an intermediate part of power system. It generally consists of the highest level in the power system voltage and the secondary voltage of network, and also charges with electricity from power generation to the distribution side. Transmission grid has a very important position in the power system. As countries advocate energy conservation and emissions reduction and the development of new energy strategy, it is very urgent to the elimination of backward production and the construction of strong smart grid, and also puts forward higher requirements on the optimal allocation of grid which is transmission grid planning. This paper analysis the simulated annealing method and the niche genetic algorithm based on the genetic algorithm to overcome the problem of genetic algorithm. The main work is as follows:This paper expounds some possible problems in depth in transmission grid planning and puts forward some planning models which consider different factors, and the parameters in details. Finally adopt the most basic static programming model. This model considers the minimum investment and the network loss as objective function. The constraints is load requirement, generator output, the numbers of new lines and the maximum transmission power.By studying traditional and modern grid planning grid method, this paper proposes the niche genetic algorithm based on genetic algorithm and niche theory. By constructing small habitat environment, this enhances the performance and convergence speed. But in order to better solve the premature convergence and local convergence of poor performance shortcoming, this paper introduces the simulated annealing mechanism and puts forward the simulated annealing-niche genetic algorithm.Through calculation program written using Matlab7.0, and using IEEE-6 nodes and 18 nodes example system to calculation and analysis, it is concluded that the simulated annealing-niche genetic algorithm is more superior than genetic algorithm on the convergence speed and search ability, proves the feasibility of the hybrid algorithm in the power grid planning.