Transmission Network Planning Based on Multi-objective Evolutionary Algorithm of Transportation Theory

  • Authors:

  • Huang Ping;Zhang Yao;Li Pengcheng;Li Kangshun

  • Affiliations:

  • Department of Mathematics, South China University of Technology, Guangzhou 510640 and School of Electric Power, South China University of Technology, Guangzhou 510640;School of Electric Power, South China University of Technology, Guangzhou 510640;Department of Mathematics, South China University of Technology, Guangzhou 510640;College of Information, South China Agricultural University, Guangzhou 510640

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
  • Year:
  • 2009

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Abstract

Power network planning is a discrete, nonlinear and multi-object mixed integer program problem, and is quite difficult to solve. In this paper, a Multi-objective Problem Evolutionary Algorithm, MOPEA, for solving power network planning is presented according to the principle of particle trajectories, minimum energy principle and the law of entropy increasing in phase space of particles based on transportation theory and this algorithm can solve complex optimization problems to obtain the global optimal solution. By means of a DC load flow model, the network takes into account of construction cost, operation cost and cost of losses. After running a simulation computation of Garver-6 node system, the results are: Compared with the results of single objective genetic algorithm and NSGA-II algorithm, MOPEA obtains the lowest costs of total planning scheme, and the planning schemes can highly improve the economic efficiency of power transmission network planning.