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Chinese Journal of Management Science ›› 2014, Vol. 22 ›› Issue (3): 68-73.

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Power Economic Dispatch of Microgrid Based on Genetic Algorithm

ZHOU Kai-le1,2, SHEN Chao1,2, DING Shuai1,2, YANG Shan-lin1,2   

  1. 1. School of Management, Hefei University of Technology, Hefei 230009, China;
    2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China
  • Received:2012-01-19 Revised:2012-11-04 Online:2014-03-20 Published:2014-03-19

Abstract: Power economic dispatch is an important kind of optimization problem in the power systems, that is, achieving the lowest tatal cost of power generation while meet many kinds of system constraints. In order to promote the optimal operation of microgrid, the power economic dispatch problem is studied in this paper of the microgrid with diesel generator, microturbine, photovoltaic power generator and wind turbine. At first, the generation characteristics and cost functions of the micro power generators are analyzed. Then the power economic dispatch models under islanded mode and grid-connected mode are constructed respectively. The objective function of the optimization model under islanded mode is the total costs which include the fuel costs and operation maintance costs, and the constraint conditions include the constraint of power generation and the constraint of system power balance. While the income and expenses of the transaction between microgrid and the large power grids are added to the objective function of the optimization model under grid-connected model based on the model under islanded mode, and the electricity transaction constraint are added to the constraint conditions. Finally, the optimization models under both modes are solved by genetic algorithm. The results show that the power economic dispatch method proposed in this paper can effectively reduce the operating costs and promote the optimal operation of microgrid.

Key words: economic load dispatch, optimization models, genetic algorithm, microgrid

CLC Number: