基于卷积深度网络的含分布式电源配电网可靠性评价

潘凯岩; 赵瑞锋; 刘海信; 卢建刚; 余志文; 刘宏达

doi:10.19912/j.0254-0096.tynxb.2023-1396

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (1) : 412-419. DOI: 10.19912/j.0254-0096.tynxb.2023-1396

基于卷积深度网络的含分布式电源配电网可靠性评价

潘凯岩^1,3, 赵瑞锋², 刘海信³, 卢建刚², 余志文², 刘宏达¹

作者信息 +

RELIABILITY EVALUATION OF DISTRIBUTION NETWORKS CONTAINING DISTRIBUTED POWER SOURCES BASED ON CONVOLUTIONAL DEEP NETWORK

Pan Kaiyan, Zhao Ruifeng², Liu Haixin³, Lu Jiangang², Yu Zhiwen², Liu Hongda¹

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摘要

现代电网维度和不确定性的增加,使传统可靠性评价方法在计算性能上遇到瓶颈。该文提出一种基于卷积深度网络结合蒙特卡洛仿真的可靠性评价方法来提高含分布式电源复合电力系统的计算效率。该方法以分布式发电机组、输电容量和总负荷组成的样本作为输入,利用卷积特性对纳入电力系统的拓扑结构进行特征提取。采用重要性采样降低由分布式光伏与风电机组输出功率不确定性引发的可靠性评价过程波动。通过网络预测出采样系统状态下的负荷削减,以最小负荷削减量评估系统缺电概率和缺电频率组成的可靠性指数。在IEEE RTS可靠性测试系统上的实验结果表明,所提出的方法在计算综合系统可靠性指标方面是有效的。

Abstract

With the increase of dimensionality and uncertainty in modern power grids, the traditional reliability evaluation methods have bottlenecks in computational performance due to the need to repeatedly solve the optimal system tides. In this paper, a reliability evaluation method based on convolutional deep network combined with importance sampling is proposed to improve the computational efficiency of a composite power system containing distributed power sources. The method takes samples consisting of distributed generating units, transmission capacity and total load as inputs, and utilizes the convolutional property for feature extraction of the topology incorporated into the power system. Importance sampling is used to reduce the fluctuation of the reliability evaluation process triggered by the uncertainty in the output power of distributed PV and wind turbines. The convolutional neural network is used to predict load shedding in the sampled system states, and the reliability index consisting of the expected system power shortage supply, probability of power shortage and frequency of power shortage is evaluated with the minimum load shedding. The experimental results on the IEEE RTS reliability test system show that the proposed method is effective in calculating the integrated system reliability index.

导出引用

潘凯岩, 赵瑞锋, 刘海信, 卢建刚, 余志文, 刘宏达. 基于卷积深度网络的含分布式电源配电网可靠性评价[J]. 太阳能学报. 2025, 46(1): 412-419 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1396

Pan Kaiyan, Zhao Ruifeng, Liu Haixin, Lu Jiangang, Yu Zhiwen, Liu Hongda. RELIABILITY EVALUATION OF DISTRIBUTION NETWORKS CONTAINING DISTRIBUTED POWER SOURCES BASED ON CONVOLUTIONAL DEEP NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 412-419 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1396

中图分类号： TM73

参考文献

[1] ZHANG X H, TOMSOVIC K, DIMITROVSKI A.Security constrained multi-stage transmission expansion planning considering a continuously variable series reactor[J]. IEEE transactions on power systems, 2017, 32(6): 4442-4450.
[2] ZHANG X H, SHI D, WANG Z W, et al.Optimal allocation of static var compensator via mixed integer conic programming[C]//2017 IEEE Power & Energy Society General Meeting. Chicago, IL, USA, 2017: 1-5.
[3] XU Y X, SINGH C.Power system reliability impact of energy storage integration with intelligent operation strategy[J]. IEEE transactions on smart grid, 2014, 5(2): 1129-1137.
[4] GHOSH B, CHAKRABORTY A K, BHOWMIK A R.Remodelling of an electric distribution network through optimal placement of auto-reclosers to enhance system reliability and efficiency[J]. Arabian journal for science and engineering, 2022, 47(3): 3619-3631.
[5] BROWN R E.Reliability Metrices and Indices[M]. New York, CRC Press, 2009.
[6] ERASMUS E, NAIDOO R.Smart distribution and optimisation of the number and position of reclosers to minimise equipment damage[C]//2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). Turin, Italy, 2017: 1-6.
[7] SAVIER J S, DAS D.Impact of network reconfiguration on loss allocation of radial distribution systems[J]. IEEE transactions on power delivery, 2007, 22(4): 2473-2480.
[8] SULTAN H, ANSARI S J, ALAM A, et al.Reliability improvement of a radial distribution system with recloser placement[C]//2019 International Conference on Computing, Power and Communication Technologies(GUCON). India: New Delhi, 2019: 736-741.
[9] BHUSAL N, GAUTAM M, BENIDRIS M.Sizing of movable energy resources for service restoration and reliability enhancement[C]//2020 IEEE Power & Energy Society General Meeting (PESGM). Montreal, QC, Canada, 2020: 1-5.
[10] GHORBANI-JUYBARI M Z, GHOLIZADE-NARM H, DAMCHI Y. Optimal recloser placement in distribution system considering maneuver points, practical limitations, and recloser malfunction[J]. International transactions on electrical energy systems, 2022, 2022(1): 5062350.
[11] 魏恩伟, 阳浩, 薛荣, 等. 基于离散猴群算法的多目标配电网重构方法[J]. 电力系统及其自动化学报, 2023, 35(1): 30-35.
WEI E W, YANG H, XUE R, et al.Multi-objective distribution network reconfiguration method based on discrete monkey algorithm[J]. Proceedings of the CSU-EPSA, 2023, 35(1): 30-35.
[12] 李虹, 林兰心, 赵小军. 基于需求侧用户响应分析的电-气-热综合能源系统低碳经济调度[J]. 太阳能学报, 2023, 44(5): 97-105.
LI H, LIN L X, ZHAO X J.Low carbon economic scheduling of electricity-gas-heat integrated energy system based on demand-side user response analysis[J]. Acta energiae solaris sinica, 2023, 44(5): 97-105.
[13] 师阳, 李宏伟, 陈继开, 等. 计及激励型需求响应的热电互联虚拟电厂优化调度[J]. 太阳能学报, 2023, 44(4): 349-358.
SHI Y, LI H W, CHEN J K, et al.Optimal scheduling of thermoelectric interconnection virtual power plant considerign incentive demand response[J]. Acta energiae solaris sinica, 2023, 44(4): 349-358.
[14] 王秀丽, 闫璐, 刘豹, 等. 基于合作-非合作博弈的光储荷网协同运行策略[J]. 太阳能学报, 2023, 44(5): 128-138.
WANG X L, YAN L, LIU B, et al.Collaborative operation strategy of optical storage and loading network based on cooperative non cooperative game[J]. Journal of Solar Energy, 2023, 44(5): 128-138.
[15] SINGH C,JIRUTITIJAROEN P,MITRA J.Electric power grid reliability evaluation: models and methods[M]. Hoboken: John Wiley & Sons, 2018.
[16] 丁津津, 徐晨, 孙辉, 等. 线路纵差分布式自动测试技术研究与应用[J]. 哈尔滨理工大学学报, 2022, 27(3): 111-118.
DING J J, XU C, SUN H, et al.Research and application of distributed automatic test technology for line longitudinal difference[J]. Journal of Harbin University of Science and Technology, 2022, 27(3): 111-118.
[17] BENIDRIS M, ELSAIAH S, MITRA J.Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method[J]. IET generation, transmission & distribution, 2015, 9(14): 1865-1873.
[18] BENIDRIS M, MITRA J.Composite power system reliability assessment using maximum capacity flow and directed Binary Particle Swarm Optimization[C]//2013 North American Power Symposium (NAPS). Manhattan, KS, USA, 2013: 1-6.
[19] URGUN D, SINGH C.A hybrid Monte Carlo simulation and multi label classification method for composite system reliability evaluation[J]. IEEE transactions on power systems, 2019, 34(2): 908-917.
[20] KHUNTIA S R, RUEDA J L, VAN DER MEIJDEN M A M M. Mutual information based Bayesian analysis of power system reliability[C]//2015 IEEE Eindhoven Power Tech. Eindhoven, Netherlands, 2015: 1-6.
[21] URGUN D, SINGH C.Composite system reliability analysis using deep learning enhanced by transfer learning[C]//2020 International Conference on Probabilistic Methods Applied to Power Systems(PMAPS). Liege, Belgium, 2020: 1-6.
[22] MUDGAL S, GUPTA P K, YADAV A K, et al.Artificial neural network for reliability evaluation of power system network with renewable energy[C]//2020 21st National Power Systems Conference (NPSC). Gandhinagar, India, 2020: 1-6.
[23] 白浩, 王钰山, 周长城, 等. 市场环境下含新能源配电网供电能力评估及提升策略[J]. 哈尔滨理工大学学报, 2021, 26(5): 114-123.
BAI H, WANG Y S, ZHOU C C, et al.The evaluation and promotion strategy of TSC for distribution system with new energy under power market environment[J]. Journal of Harbin University of Science and Technology, 2021, 26(5): 114-123.
[24] WEN Y F, GUO C X, KIRSCHEN D S, et al.Enhanced security-constrained OPF with distributed battery energy storage[J]. IEEE transactions on power systems, 2015, 30(1): 98-108.