Abstract
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.
Highlights
Reactive power optimization is an effective means to ensure the safe and economic operation of power system
After the entropy weight of each evaluation index is determined, the multi-attribute decision making is transformed into a single attribute decision making problem; and the optimal scheme can be selected from the alternatives
In practical scenarios that the global optimal solution is necessary, entropy weight optimum seeking method (EWOSM) can be used in combination with Genetic Algorithm (GA) method, neighborhood search method, Sequential Quadratic Programming (SQP) method and other existing methods to speed up the convergence and ensure the global optimization
Summary
Featured Application: This work is a prospective study on reactive power optimization based on the background of big data, which is supported by Science and Technology Project of State Grid. Corporation of China (SGCC) (EPRIPDKJ (2015) 1495), and Beijing Natural Science Foundation (3172039). The research results will be applied in demonstration applications of SGCC in the future
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