Cloud model-based unconventional risk assessment method for flexible distribution system

Shaotao Guo; Peng Ye; Jinhang Shan; Yi Liang; Zhentao Han; Qixiang Wang; Na Zhang

doi:10.4108/ew.9104

Authors

Shaotao Guo Shenyang Institute of Engineering
Peng Ye Shenyang Institute of Engineering
Jinhang Shan Shenyang Institute of Engineering
Yi Liang State Grid Liaoning Electric Power Co., Ltd. Economic and Technological Research Institute, Shenyang, 110013, China
Zhentao Han State Grid Liaoning Electric Power Co., Ltd. Economic and Technological Research Institute, Shenyang, 110013, China
Qixiang Wang State Grid Liaoning Electric Power Co., Ltd. Economic and Technological Research Institute, Shenyang, 110013, China
Na Zhang State Grid Liaoning Electric Power Co., Ltd. Economic and Technological Research Institute, Shenyang, 110013, China

DOI:

https://doi.org/10.4108/ew.9104

Keywords:

Flexible distribution system, risk assessment, AHP-entropy weight method, cloud model, distributed resources

Abstract

The integration of high-penetration distributed resources has led to increased complexity and uncertainty in the unconventional risks of distribution networks, posing higher demands on the risk assessment of distribution networks. This paper proposes an unconventional risk assessment method for flexible distribution system based on cloud model. Firstly, an unconventional risk assessment system for distribution networks is constructed by considering the probability of unconventional risk occurrence and the severe consequences, and an improved AHP-entropy weight method for index weighting is proposed. Then, the cloud model for risk assessment is used to quantitatively evaluate the risk level of the distribution system. The variable weight cloud model is employed to replace the traditional cloud model to provide risk indicator evaluation information. The inverse cloud generator is used to infer and correct the risk cloud model parameters, and the assessment is completed by comparing with the digital characteristics of the standard cloud model. Finally, the effectiveness of the proposed assessment method is verified through an example analysis of a certain region in China.

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References

[1] Hu Bo, Xie Kaigui, Shao Changzheng, et al. Risk assessment of new power systems under the dual carbon target: characteristics, indicators, and evaluation methods [J]. Power System Automation, 2023,47 (05): 1-15.

[2] Xue Yu, LI Yueqiao, LIU Wenxia, et al. Risk assessment of Chain Failure in Flexible Interconnected Distribution System [J]. Automation of Electric Power Systems, 2019,45(08):112-119.

[3] Liu Wenxia, Wang Rongjie, Liu Xin, et al. Robust Optimization Recovery of Fault Interval in Flexible Distribution Systems [J] Chinese Journal of Electrical Engineering, 2020,40 (12): 3897-3907.

[4] Liu Wenxia, Liu Xin, Wang Rongjie, Huang Yuchen, Yang Yong, and Li Jihong. Reliability evaluation and quantitative analysis of multi terminal interconnected distribution systems with flexible multi state switches [J] High voltage technology. 2020 (04): 1114-1124.

[5] Mi Weiming, Ye Peng, Zhang Mingli, et al. A novel flexibility evaluation method for distribution systems based on cloud models [J/OL]. Grid Technology: 1-9 [2023-10-15].

[6] Huang Wujing, Zhang Ning, Dong Ruibiao, Liu Yongxiao, and Kang Chongqing. Joint planning method for multi energy networks and energy hubs [J]. Chinese Journal of Electrical Engineering, 2018, 38 (18): 5425-5437.

[7] Shen Xinwei, Guo Qinglai, Xu Yinliang, and Sun Hongbin. Robust planning of regional integrated energy systems considering uncertainty of multi energy loads [J]. Power System Automation, 2019, 43 (07): 34-41.

[8] Zhu Huai. Fuzzy comprehensive evaluation of the health status of aqueducts based on AHP entropy weight method [D]. Hunan University, 2014.

[9] Cai Guowei, Zhang Bin, Wang Jianyuan, et al. Application of Cloud Model Theory in Load Frequency Control of Interconnected Power Systems [J]. Chinese Journal of Electrical Engineering, 2015,35 (02): 353-358.

[10] Wang Shouxiang, Liu Dong, Yu Lu, and Zhao Qianyu. A multistage planning method for electric vehicle charging facilities based on data envelopment analysis considering the coupling of transportation and distribution networks [J/OL]. Power Automation Equipment: 1-7 [2022-05-17].

[11] Lu Jiayang. Collaborative Planning of Source Network Load Storage in Regional Multi energy Systems Based on Energy Hubs [D]. Shanghai: Shanghai Electric Power University, 2021.

[12] Fan Hong, Yuan Qianqian, and Deng Jian. A two-stage capacity optimization configuration method for multi-regional integrated energy systems [J]. Modern Electric Power, 2020,37 (05): 441-449.

[13] Chen Minrong and Deng Feiqi. A clustering method based on self-organizing feature mapping network [J]. Systems Engineering and Electronic Technology, 2004 (12): 1864-1866.

[14] Zhang Yating, Guo Liang, Guo Da, Li Qian, and Liu Baoan. Application of Improved Quantum Genetic Algorithm in Distribution Networks with Distributed Power Sources [J/OL]. Electrical Measurement and Instrumentation: 1-7 [2022-11-07].

[15] Tan Xin, Huang Qiyu, Han Hongling, et al. Exploration of risk assessment research methods for offshore wind power projects based on variable weight theory [J]. China Survey and Design, 2022, (S2): 86-90.

[16] Jia Guangyao. Research on the Impact and Risk Assessment of Distributed Power Grid Connection on the Power Grid [D]. Lanzhou University of Technology, 2020.