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
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.
引用
收藏
页码:67 / 75
页数:8
相关论文
共 26 条
  • [1] Li Y., Nejabatkhah F., Overview of control, integration and energy management of microgrids, Journal of Modern Power Systems and Clean Energy, 2, 3, pp. 212-222, (2014)
  • [2] Li Z., Shahidehpour M., Aminifar F., Cybersecurity in distributed power systems, Proceedings of the IEEE, 105, 7, pp. 1367-1388, (2017)
  • [3] Abdelaziz M.M., Farag H.E., El-Saadany E.F., Et al., A novel and generalized three-phase power flow algorithm for islanded microgrids using a newton trust region method, IEEE Transactions on Power Systems, 28, 1, pp. 190-201, (2013)
  • [4] Peng H., Cao Y., Huang X., Et al., Continuous power flow for islanding microgrid without balance nodes, Proceedings of the CSEE, 36, 8, pp. 2057-2067, (2016)
  • [5] Wang C., Sun C., Peng K., Et al., Study on AC-DC hybrid power flow algorithm for microgrid, Proceedings of the CSEE, 33, 4, pp. 8-15, (2013)
  • [6] Xu X., Yan Z., Feng D., Et al., Probabilistic load flow calculation based on rank correlation coefficient of input random variables, Automation of Electric Power Systems, 38, 12, pp. 54-61, (2014)
  • [7] Cui Y., Bie Z., Wang X., Study on probabilistic models and algorithms of available transfer capability, Automation of Electric Power Systems, 27, 14, pp. 36-40, (2003)
  • [8] Villanueva D., Luis Pazos J., Feijoo A., Probabilistic load flow including wind power generation, IEEE Transactions on Power Systems, 26, 3, pp. 1659-1667, (2011)
  • [9] Hong H.P., An efficient point estimate method for probabilistic analysis, Reliability Engineering & System Safety, 59, 3, pp. 261-267, (1998)
  • [10] Han D., Ma J., He R., Et al., Uncertainty analysis based on stochastic response surface method in power system simulation, Automation of Electric Power Systems, 35, 24, pp. 12-16, (2011)