[1]Wang Kewen,Liu Kai,Liu Yanhong.Operation mode optimization of active distribution network considering power prediction error[J].Journal of Zhengzhou University (Engineering Science),2020,41(01):75-82.[doi:10.13705/j.issn.1671-6833.2019.04.008]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41
Number of periods:
2020 01
Page number:
75-82
Column:
Public date:
2020-03-10
- Title:
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Operation mode optimization of active distribution network considering power prediction error
- Author(s):
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Wang Kewen; Liu Kai; Liu Yanhong
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School of Electrical Engineering, Zhengzhou University
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- Keywords:
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active distribution networksecond order correctionprobability power flow trust regioncon-straint condition
- CLC:
-
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- DOI:
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10.13705/j.issn.1671-6833.2019.04.008
- Abstract:
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In the optimization of the operation mode of active distribution network, node power data usually comes from power prediction, and there are prediction errors and corresponding distribution characteristics, which can be described by probabilistic expression. Considering the distribution characteristics of power prediction error, taking the average of comprehensive operating costs as the objective function, the node power balance equation as an equality constraint, and the operating range of variables such as node voltage and branch power constituting inequality constraints, the operating mode of active distribution network is established. Probabilistic optimization model. By analyzing the characteristics of the optimization formula, the probability description of the second-order power flow expression is adopted, and the variance correction is taken into account in the calculation of the mean value of random variables to improve the accuracy of the mean value calculation. In solving the optimization model, according to the actual characteristics of variables, different processing methods are used for discrete variables and continuous variables, and trust region management technology is used to process continuous variables. The feasibility and practicability of the algorithm are illustrated through the calculation and analysis of a 118-node example.