| The smart grid is not only a comprehensive program to solve the global energy, climate, environment, economy and the sustainable development, but the main research and development direction of the grid in the future. The intelligent of transmission network and distribution network is an important part of the strong smart grid. Smart grid planning is an important part of power system planning. If we carry on scientific and reasonable planning, it could bring huge economic benefits and social benefits to the society. It is the important basis of development of smart grid. Power system planning is a nonlinear, multi-objective and multi-constraint problem. It related to constraints such as power flow calculation, reactive power compensation, the network loss, power supply reliability and so on.When used method of traditional power grid optimization to solve the power grid planning, the algorithm easily falls into local optimal solution and can’t find the global optimal solution finally. In recent years, modern heuristic algorithm is widely used in various fields and achieved good results. These algorithms have good global search capability and versatility, etc. But these algorithms produce curse of dimensionality easily. In this thesis, based on the basic ideas of particle swarm optimization (PSO) algorithm, combined with the quantum theory, a new quantum particle swarm optimization algorithm is formed. It is applicated in multi-objective optimization planning of transmission network and distributed power contains DG in the smart grid. Studies show that the proposed algorithm is effective for smart grid with multi-objective optimization planning.According to the power grid planning of multi-objective, select the appropriate target as a mathematical model of power grid planning in this thesis. To improve the quantum particle swarm optimization (QPSO) algorithm, make it can be used to solve the problems in the discrete. The improved algorithm improves the running speed and convergence speed. Take18nodes system expansion planning and8nodes system of distribution network planning containing DG as example, to verify the effectiveness and high efficiency of the algorithm to solve the multi-objective programming. For the structure of multi-objective Pareto optimal solution set, the crowded distance sorting method is used.At last, Matlab programming is applicated to get corresponding planning results of examples. Results show that the application of the QPSO algorithm is reliable and useful in multi-objective planning of smart grid. It can complete the task and save the cost of power grid planning in the premise of guarantee calculation speed. |
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