A Distributionally Robust Optimization Scheduling Model Considering Higher-Order Uncertainty of Wind Power

被引：0

作者：

Xia P. ^{[1
]}

Liu W. ^{[1
]}

Zhang Y. ^{[1
]}

Wang W. ^{[2
]}

Zhang B. ^{[2
]}

机构：

[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing

[2] State Grid Gansu Electric Power Company, Lanzhou

来源：

Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2020年 / 35卷 / 01期

关键词：

Cloud model; Distributionally robust optimization; Higher-order uncertainty; Optimal scheduling; Wind power;

D O I：

10.19595/j.cnki.1000-6753.tces.190809

中图分类号：

学科分类号：

摘要：

Higher-order uncertainty of wind power probability distribution is ignored in traditional optimal dispatching methods considering wind power uncertainty, and there is further optimization space for operation cost and wind power consumption capacity. Therefore, a distributionally robust optimization scheduling model considering higher-order uncertainty of wind power was proposed in this paper. Firstly, a wind power higher-order uncertainty model was established by introducing cloud model theory, which could simultaneously describe the uncertainty of wind power and its probability distribution. On this basis, combined with distributionally robust optimization theory, taking the lowest comprehensive operation cost as optimal target, a distributionally robust optimization scheduling model was established. And then, multi-dimensional sequential operation theory was introduced to discretize the higher-order uncertainty cloud model of wind power, so as to transform distributionally robust optimization model into a two-stage nonlinear optimization model and simplified its solution. Finally the effectiveness of proposed model in improving optimal dispatching effect of wind power system are verified. © 2020, Electrical Technology Press Co. Ltd. All right reserved.

引用

页码：189 / 200

页数：11

共 25 条

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