Low-carbon distributionally robust optimal scheduling for AC／DC distribution network considering wind power uncertainty

被引：0
|
作者：
Xi J. [1 ]
Tong X. [1 ]
Li Z. [1 ]
Dong X. [1 ]
Yang M. [2 ]
Liu F. [2 ]
机构：
[1] School of Electrical Engineering, Southwest Jiaotong University, Chengdu
[2] Jiaozuo Power Supply Company, State Grid Henan Electric Power Company, Jiaozuo
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2023年 / 43卷 / 11期
关键词：
AC／DC distribution network; carbon trading; Copula function; decentralized coordination; distributionally robust scheduling; K-L divergence; wind power uncertainty;
D O I：
10.16081/j.epae.202302026
中图分类号：
学科分类号：
摘要：
In order to increase the wind power absorption capacity of the distribution network and reduce the carbon emission，a low-carbon distributionally robust optimal scheduling model for AC／DC distribution network is established. The positive correlation between historical data of wind power forecast error and wind power forecast output is analyzed. The mixed Copula function is used to establish the joint probability distribution between them，and the conditional probability distribution of wind power forecast error is obtained. The AC／DC distribution network is decoupled as AC and DC subnets，taking the minimum comprehensive operating cost of each subnet as the optimization objective，and the carbon trading mechanism is introduced in the AC subnet optimization model，so that the decentralized coordinated optimization model of AC／DC distribution network is established. Taking the obtained conditional probability distribution of wind power forecast error as the reference，the distributionally robust ambiguous set based on K-L divergence is con⁃ structed. The proposed optimization model is converted into a single-layer optimization objective model by using Lagrange dualism theory and the alternating direction method of multipliers is used for decentralized coordination optimal solution. Simulative results based on the modified 33-bus AC／DC distribution network model show that the proposed model can effectively reduce the carbon emission at distribution side and significantly improve the consumption capacity of wind power. © 2023 Electric Power Automation Equipment Press. All rights reserved.
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页码：59 / 66
页数：7
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