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This paper proposed an entropy weight optimum seeking method (EWOSM) based on the typical scenarios partitioning and load distribution matching, to solve the reactive power optimization problem in distribution network under the background of big data. Firstly, the mathematic model of reactive power optimization is provided to analyze the relationship between the data source and the optimization schemes in distribution network, which illustrate the feasibility of using large amount of historical data to solve reactive power optimization. Then, the typical scenarios partitioning method and load distribution matching method are presented, which can select out some loads that have the same or similar distributions with the load to be optimized from historical database rapidly, and the corresponding historical optimization schemes are used as the alternatives. As the reactive power optimization is a multi-objective problem, the multi-attribute decision making method based on entropy weight method is used to select out the optimal scheme from the alternatives. The objective weights of evaluation indexes are determined by entropy weight method, and then the multi-attribute decision making problem is transformed to a single attribute decision making problem. Finally, the proposed method is tested on several systems with different scales and compared with existing methods to prove the validity and superiority.

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