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| Static and dynamic integration method on identifying vulnerability nodes considering new energy power |
| DOI：10.7667/PSPC180093 |
| Key Words:cascading failure new energy transient stability uncertainty vulnerability |
| Author Name | Affiliation | | LI Lijuan | College of Information Engineering, Xiangtan University, Xiangtan 411105, China | | WU Jun | College of Information Engineering, Xiangtan University, Xiangtan 411105, China | | LIU Hongliang | Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan 411105, China | | LI Yuan | College of Information Engineering, Xiangtan University, Xiangtan 411105, China | | ZENG Taiyuan | College of Information Engineering, Xiangtan University, Xiangtan 411105, China | | ZHOU Jian | College of Information Engineering, Xiangtan University, Xiangtan 411105, China | | GONG Zheng | College of Information Engineering, Xiangtan University, Xiangtan 411105, China |
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| Abstract:With the rapid growth of new energy power generation, its randomness and volatility bring new challenges to identify the vulnerability nodes in power grid. In this paper, a method of integration of static state and dynamic state on identifying vulnerability nodes considering new energy power is proposed. In the static identification, the interval numbers are used to express the uncertainty of new energy. The interval minimum load shedding model based on interval direct current power flow is proposed to calculate the interval static vulnerability assessment index. In the dynamic identification, the energy margin of each node is calculated by the extension method of Single Machine Equivalence (SIME) method, and the dynamic vulnerability assessment index is obtained according to the positive or negative of energy margin. Finally, the weights of two indexes are obtained by the entropy weight method, and the vulnerability index of each node is evaluated. The simulation results of IEEE 39-bus system show that the proposed method can accurately and quickly identify the vulnerability nodes under the condition of new energy injected into the power grid. Compared with the existing vulnerability identification methods, the proposed method is closer to actual operation of the power system. The results show that the randomness and volatility of new energy has a far greater impact on near nodes, which provides guidance for the planning of the new energy power supply in the smart grid. This work is supported by National Natural Science Foundation of China (No. 51307148) and Sino-US international Science and Technology Cooperation Project (No. 2016YFE0105300). |
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