Adaptive-linearized Probabilistic Power Flow Calculation for Power Grid Integrated With High Proportion Wind Power in Source-load Interactive Environment

被引:0
|
作者
Liu Z. [1 ]
Wei Z. [1 ]
Gao S. [2 ]
Cheng L. [2 ]
Tan K. [2 ]
Sun G. [1 ]
Zang H. [1 ]
机构
[1] College of Energy and Electrical Engineering, Hohai University, Nanjing, 211100, Jiangsu
[2] Nanjing Power SupplyCompany, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210019, Jiangsu
来源
基金
中国国家自然科学基金;
关键词
Adaptive linearization; Cumulant; Gaussian mixture model; High randomness of source-load; Probabilistic power flow; Regional correlation;
D O I
10.13335/j.1000-3673.pst.2018.2352
中图分类号
学科分类号
摘要
Large-scale fluctuation of high proportion wind power and random response of flexible load aggravate uncertainty of power system. It is difficult to use conventional cumulant method based probabilistic power flow (PPF) to deal with high randomness and correlation simultaneously, resulting in large error. An adaptive-linearized cumulant based PPF method considering high randomness is proposed. Considering the stochastic response of source-load interaction, a source-load uncertainty model is accurately established using Gaussian mixture model. An adaptive linearization method for PPF is proposed to deal with high randomness. The sub-regions are updated iteratively to realize adaptive multi-region division of the fluctuation range and linearization within the region to reduce the global linearization error of power flow. Meanwhile, considering the regional correlation of wind power, cumulant after integration is obtained using adaptive linearized cumulant method. Finally, C-type Gram-Charlier series is used to estimate probability distribution of state variables. Accuracy and effectiveness of the algorithm is verified with simulation of modified IEEE 30-bus system, and the impact of source-load disturbance on system operation is quantitatively assessed. © 2019, Power System Technology Press. All right reserved.
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收藏
页码:3926 / 3934
页数:8
相关论文
共 20 条
  • [1] Kang C., Yao L., Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy, Automation of Electric Power Systems, 41, 9, pp. 2-11, (2017)
  • [2] Lu Z., Li H., Qiao Y., Flexibility evaluation and supply/demand balance principles of power system with high penetration renewable electricity, Proceedings of the CSEE, 37, 1, pp. 9-19, (2017)
  • [3] Borkowska B., Probabilistic load flow, IEEE Transactions on Power Apparatus and Systems, 93, 3, pp. 752-759, (1974)
  • [4] Chen Y., Wen J., Cheng S., Probabilistic load flow analysis considering dependencies among input random variables, Proceedings of the CSEE, 31, 22, pp. 80-87, (2011)
  • [5] Morales J.M., Baringo L., Conejo A.J., Et al., Probabilistic power flow with correlated wind sources, IET Proceedings: Generation, Transmission and Distribution, 4, 5, pp. 641-651, (2010)
  • [6] Shi D., Cai D., Chen J., Et al., Probabilistic load flow calculation based on cumulant method considering correlation between input variables, Proceedings of the CSEE, 32, 28, pp. 104-113, (2012)
  • [7] Liu Z., Wei Z., Sun G., Et al., Probabilistic power flow calculation of power system with wind farms considering fuzzy parameters, Power System Technology, 41, 7, pp. 2308-2315, (2017)
  • [8] Liu J., Hao X., Cheng P., Et al., Probabilistic power flow method combining M-Copula theory and cumulants, Power System Technology, 42, 2, pp. 578-584, (2018)
  • [9] Ma R., Zhang Q., Wu X., Et al., Random fuzzy uncertain model for daily wind speed, Proceedings of the CSEE, 35, 24, pp. 6351-6358, (2015)
  • [10] Ren Z., Li W., Billinton R., Probabilistic power flow analysis based on the stochastic response surface method, IEEE Transactions on Power System, 31, 3, pp. 2307-2315, (2016)