Wind Power Time Series Aggregation Approach Based on Affinity Propagation Clustering and MCMC Algorithm

被引：0

作者：

Ye L. ^{[1
]}

Li J. ^{[1
]}

Lu P. ^{[1
]}

Zhai Q. ^{[1
]}

Li P. ^{[2
]}

Wang W. ^{[2
]}

Dong L. ^{[3
]}

机构：

[1] College of Information and Electrical Engineering, China Agricultural University, Haidian District, Beijing

[2] China Electric Power Research Institute, Haidian District, Beijing

[3] Qinghai Electric Power Company, Xining, 810000, Qinghai Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2020年 / 40卷 / 12期

基金：

国家重点研发计划;

关键词：

Affinity propagation clustering; Aggregation sequence; Markov Chain Monte Carlo; Time sequential simulation; Wind power time series data;

D O I：

10.13334/j.0258-8013.pcsee.190788

中图分类号：

学科分类号：

摘要：

In order to reduce the redundant data in wind power time series and improve the computational efficiency of power system simulation. This paper proposed a wind power time series data aggregation method based on affinity propagation clustering and Markov Chain Monte Carlo (AP-MCMC). Firstly, autocorrelation function and fast Fourier transform were used to analyze periodic characteristics in wind power time series data to select the best time scale of scenario samples in time domain and frequency domain. Secondly, affinity propagation clustering was applied to generate multi-class daily scenario clusters based on the Markov Chain Monte Carlo method. A state transition probability matrix of various daily scenario clusters was constructed and the aggregation state sequence of random length was generated. Finally, the day scenarios were sampled in a sequential one-way sequence according to the state sequence and were built into a representative wind power time series aggregation sequence. A provincial power grid was chosen as an example to compare the multiple evaluation index of the AP-MCMC method, typical day method and typical scenario set method. Results show that the aggregation sequence generated by AP-MCMC method can more accurately describe characteristics of wind power to develop the optimization strategy in time sequential simulations, thus improves accuracy and efficiency of simulations with potential engineering application. © 2020 Chin. Soc. for Elec. Eng.

引用

页码：3744 / 3753

页数：9

共 25 条

[1] Liu Zhihang, Wei Zhinong, Sun Guoqiang, Et al., Probabilistic power flow calculation of power system with wind farms considering fuzzy parameters, Power System Technology, 41, 7, pp. 2308-2315, (2017)
[2] Yang Liu, Nair N K C., A two-stage stochastic dynamic economic dispatch model considering wind uncertainty [J], IEEE Transactions on Sustainable Energy, 7, 2, pp. 819-829, (2016)
[3] Xia Shu, Ge Xiaolin, Ji Haihua, Et al., Power supply- demand balancing analysis based on chance-constrained programming, Power System Protection and Control, 45, 18, pp. 102-107, (2017)
[4] Wu Guannan, Zhang Mingli, Xu Jianyuan, Et al., Time series production algorithm for evaluating wind power accommodation capacity, Power System Protection and Control, 45, 23, pp. 151-157, (2017)
[5] Mancarella P., MES(Multi-Energy Systems):an overview of concepts and evaluation models[J], Energy, 65, pp. 1-17, (2014)
[6] Milan C, Stadler M, Cardoso G, Et al., Modeling of non-linear CHP efficiency curves in distributed energy systems[J], Applied Energy, 148, pp. 334-347, (2015)
[7] Bahl B, Kumpel A, Seele H, Et al., Time-series aggregation for synthesis problems by bounding error in the objective function[J], Energy, 135, pp. 900-912, (2017)
[8] Camal S, Teng F, Michiorri A, Et al., Scenario generation of aggregated Wind,Photovoltaics and small Hydro production for power systems applications[J], Applied Energy, 242, pp. 1396-1406, (2019)
[9] Green R, Staffell I, Vasilakos N., Divide and conquer?K- means clustering of demand data allows rapid and accurate simulations of the British electricity system[J], IEEE Transactions on Engineering Management, 61, 2, pp. 251-260, (2014)
[10] Murty V V S N, Kumar A., Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems[J], Protection and Control of Modern Power Systems, 5, 1, pp. 1-20, (2020)

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