Integrated Transmission Network Planning by Considering Wind Power's Uncertainty and Disasters

被引:1
|
作者
Shi, Yishan [1 ]
Guo, Ruipeng [1 ]
Tang, Yuchen [2 ]
Lin, Yi [2 ]
Yang, Zhanxin [1 ]
机构
[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China
[2] State Grid Fujian Econ Res Inst, Fuzhou, Peoples R China
关键词
chance constraint; convex relaxation constraint; bi-level planning model; skeleton network planning; Gaussian mixture model; wind power's uncertainty; RECONFIGURATION; MODEL; OPTIMIZATION; VARIABLES; ALGORITHM; LOAD;
D O I
10.3390/en16145336
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The penetration of wind turbines and other power sources with strong uncertainty into the grid has increased in recent years. It has brought significant technical challenges to power systems' operation. The volatility and intermittency of wind power increase the risk of insufficient transmission capacity of the lines. Therefore, the traditional deterministic planning methods for transmission grids are no longer fully applicable. On the other hand, the frequent disasters in recent years have posed a great threat to the power system, especially for the transmission grid. This requires the design of transmission lines with high design standards, such as skeleton networks, to withstand disasters. With the aim to address these problems, a bi-level integrated network planning model for the transmission grid is developed by considering wind power's uncertainty and load guarantee under disasters. Chance constraints are used in the model to characterize wind power's uncertainty, and a skeleton network is adopted to cope with disasters. Moreover, based on a convex relaxation method, the chance constraints are converted into the probabilistic inequalities to be solved. The proposed method is simulated in the IEEE 118 bus system, and the obtained network planning scheme is further analyzed in the scenario tests. And the result of the tests proves the validity and reasonableness of the proposed method.
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收藏
页数:25
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