Distributed Robust Unit Commitment with Energy Storage Based on Forecasting Error Clustering of Wind Power

被引：0
|
作者：
Shi Y. [1 ]
Wang L. [1 ]
Chen W. [1 ]
Guo C. [1 ]
机构：
[1] College of Electrical Engineering, Zhejiang University, Hangzhou
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 22期
关键词：
Dirichlet process Gaussian mixture model (DPGMM); Distributed robust optimization; Lithium-ion battery energy storage; Non-parametric Bayesian; Operation region; Wind power uncertainty;
D O I：
10.7500/AEPS20190505006
中图分类号：
学科分类号：
摘要：
In order to solve the problem of power system dispatching caused by wind power uncertainty, this paper proposes a distributed robust model for unit commitment with energy storage based on forecasting error clustering of wind power. Firstly, based on the Dirichlet process Gaussian mixture model (DPGMM), the wind power forecasting error is clustered to establish a data-driven fuzzy set of wind power forecasting error. The uncertainty set considering the correlation of wind power forecasting errors between wind farms is further established. Then, a distributed robust unit commitment model considering energy storage is proposed, and an objective function considering the cyclic aging of the energy storage system is established. Then the model with the min-max-max-min structure is decomposed into a two-stage problem. In the first stage, the operation region variable, the climbing event constraint and the energy constraint of energy storage are introduced to eliminate the dynamic constraints at the second stage. The second stage problem is transformed into a single-layer problem by KKT condition, and the two-stage problem is solved by the column and constraint generation (C&CG) algorithm. Finally, the robustness and effectiveness of the proposed model are proved by the example analysis based on IEEE 6-bus and IEEE 118-bus systems. © 2019 Automation of Electric Power Systems Press.
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页码：3 / 12and121
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