Multi-dimensional Time Series Simulation of Large-scale Photovoltaic Power Plant Output Based on Hourly Clear Sky Index

被引：0

作者：

Li G. ^{[1
]}

Li X. ^{[1
]}

Bian J. ^{[1
]}

Niu J. ^{[2
]}

Ding H. ^{[3
]}

Chen W. ^{[3
]}

机构：

[1] Key Laboratory of Modern Electric Power System Simulation and Control & Renewable Energy Technology, Northeast Electric Power University, Ministry of Education, Jilin, 132012, Jilin Province

[2] Zhengzhou Power Supply Company, Zhengzhou, 450006, Henan Province

[3] China Electric Power Research Institute, Nanjing, 210003, Jiangsu Province

来源：

Dianwang Jishu/Power System Technology | 2020年 / 44卷 / 09期

关键词：

Cluster analysis; Hourly clear sky index; Large-scale photovoltaic power generation; Multi-dimensional time series simulation;

D O I：

10.13335/j.1000-3673.pst.2019.2679

中图分类号：

学科分类号：

摘要：

The establishment of an accurate photovoltaic output time series model is of great significance for the planning and research of large-scale photovoltaic power system planning and operation. This paper proposes a multi- dimensional photovoltaic output series generation method based on the hourly sky index series, with which a large-scale photovoltaic power output simulation is performed, considering the spatial correlation characteristics of photovoltaic output between power stations. First, the hourly clear sky index series is calculated, in which the series of the photovoltaic theoretical output during sunrise and sunset is stabilized with the piecewise functions, so that the stabilized clear sky index series is used as an input for the vector auto-regressive (VAR) modeling. Secondly, a multi-step clustering algorithm is used to perform optimal spatial clustering of large-scale photovoltaic power plants on two spatial scales. The typical power plants and the variable ones are screened from the clustering for the multi-dimensional photovoltaic output sequence simulations so that the large-scale photovoltaic simulation time series is obtained. Finally, the actual measured data of the large-scale centralized photovoltaic power plants in Ningxia Hui Autonomous Region are used for simulation testing. The results show that compared with the traditional method, the multi-dimensional photovoltaic power output series generated by the method in this paper can better inherit the statistical characteristics, the probability distribution, and the autocorrelation characteristics of the original sequence, better restore the fluctuation characteristics of the photovoltaic power output, and improve the simulation accuracy for 28.06%, verifying the effectiveness of the method proposed in this paper. © 2020, Power System Technology Press. All right reserved.

引用

页码：3254 / 3262

页数：8

共 25 条

[1] Shu Yinbiao, Zhang Zhigang, Guo Jianbo, Et al., Analysis on the key factors of new energy consumption and research on its solutions [J], Proceedings of the CSEE, 37, 1, pp. 1-9, (2017)
[2] Yao Hongmin, Du Xinhui, Li Tingjun, Et al., Simulation and voltage control strategy of distribution network with high photovoltaic penetration rate, Power System Technology, 43, 2, pp. 462-469, (2019)
[3] Guo Xuyang, Xie Kaigui, Hu Bo, Et al., Simulation of time-series random production of power systems including photovoltaic power generation, Power System Technology, 37, 6, pp. 1499-1505, (2013)
[4] Yang Mao, Wang Shaoshuai, Li Dayong, Et al., Cost-benefit analysis of centralized photovoltaic power generation considering environmental benefits over its entire life cycle, Journal of Northeast Electric Power University, 38, 6, pp. 21-28, (2018)
[5] Majid M, Reza G, Mohsen G., Power system security assessment with high wind penetration using the farms models based on their correlation[J], IET Renewable Power Generation, 12, 8, pp. 893-900, (2018)
[6] Eftekharnejad S, Vittal V, Heydt, Et al., Impact of increased penetration of photovoltaic generation on power systems[J], IEEE Transactions on Power Systems, 28, 2, pp. 893-901, (2013)
[7] Du W, Bi J, Wang T, Et al., Impact of grid connection of large-scale wind farms on power system small-signal angular stability[J], CSEE Journal of Power and Energy Systems, 1, 2, pp. 83-89, (2015)
[8] Zou Jin, Zhu Jizhong, Lai Xu, Et al., Sequence simulation of wind power output based on spatiotemporal autoregressive moving average model, Automation of Electric Power Systems, 43, 3, pp. 101-109, (2019)
[9] Chen P, Pedersen T, Bak-Jensen B, Et al., ARIMA-based time series model of stochastic wind power generation[J], IEEE Transactions on Power Systems, 25, 2, pp. 667-676, (2010)
[10] Silva A M L L D, Sales W S, Manso L A D F, Et al., Long-term probabilistic evaluation of operating reserve requirements with renewable sources[J], IEEE Transactions on Power Systems, 25, 1, pp. 106-116, (2010)

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