Probabilistic Power Flow Calculation of Islanded Microgrid Based on Sparse Polynomial Chaos Expansion

被引：0
|
作者：
He K. [1 ]
Xu X. [1 ]
Yan Z. [1 ]
Wang H. [1 ]
Hong Y. [2 ]
机构：
[1] Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University), Shanghai
[2] East China Branch of State Grid Corporation of China, Shanghai
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 02期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Dependency; Islanded microgrid; Probabilistic power flow; Renewable energy source (RES); Sparse polynomial chaos expansion (SPCE); Uncertainty;
D O I：
10.7500/AEPS20180809005
中图分类号：
学科分类号：
摘要：
Considering the effect of uncertainty of renewable energy source (RES) on the operation of microgrid, it is hard to comprehensively describe operation states and power flow distribution through the traditional deterministic power flow calculation. Based on the randomness and dependency of generators and loads in microgrid, this paper develops a probabilistic power flow model of islanded AC microgrid. The sparse polynomial chaos expansion method is adopted to obtain output response of islanded microgrid quickly and accurately, also it can improve the efficiency of probabilistic power flow calculation of microgrid with high-dimensional input variables. Finally, a 33-node islanded microgrid with several distributed generators is simulated, and the effectiveness and accuracy of the proposed method are verified by comparing with traditional methods. © 2019 Automation of Electric Power Systems Press.
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页码：67 / 75
页数：8
相关论文
共 26 条
  • [21] Haesen E., Bastiaensen C., Driesen J., Et al., A probabilistic formulation of load margins in power systems with stochastic generation, IEEE Transactions on Power Systems, 24, 2, pp. 951-958, (2009)
  • [22] Mukherjee S., Niyogi P., Poggio T., Et al., Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization, Advances in Computational Mathematics, 25, 1-3, pp. 161-193, (2006)
  • [23] Wei D.L., Cui Z.S., Chen J., Uncertainty quantification using polynomial chaos expansion with points of monomial cubature rules, Computers & Structures, 86, 23-24, pp. 2102-2108, (2008)
  • [24] Blatman G., Sudret B., Adaptive sparse polynomial chaos expansion based on least angle regression, Journal of Computational Physics, 230, 6, pp. 2345-2367, (2011)
  • [25] Tibshirani R., Johnstone I., Efron B., Et al., Least angle regression, Annals of Statistics, 32, 2, pp. 407-451, (2004)
  • [26] Marelli S., Sudret B., UQLab: a framework for uncertainty quantification in MATLAB, International Conference on Vulnerability, Risk Analysis and Management, pp. 2554-2563
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