Optimal power flow calculation including power flow router in distribution network

被引：0
|
作者：
Li C. [1 ]
Wang C. [1 ]
Yin F. [1 ,2 ]
Wang T. [1 ]
机构：
[1] Department of Electrical and Automatic Engineering, Nanchang University, Nanchang
[2] College of Physical Science and Technology Engineering, Yichun University, Yichun
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2019年 / 47卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Distribution network; Latin hypercube sampling; Optimal power flow; Power flow router; Second-order cone relaxation;
D O I：
10.7667/PSPC180348
中图分类号：
学科分类号：
摘要：
In order to solve the problem of limited control range of control elements and scattered control pattern of distribution networks, Power Flow Router (PFR) is applied in distribution networks. An improved branch flow model with power flow routers in distribution networks is proposed and an optimal power flow model including PFR is constructed. Second-order cone relaxation is used to relax the model and the relaxed model is solved by using the commercial algorithm package such as Gurobi. Furthermore, a method, which considers the uncertainty of load and distributed generation's output, is presented based on the combination of Latin hypercube sampling and second-order cone programming. The revised IEEE 33-bus system verifies the effectiveness of the proposed method. © 2019, Power System Protection and Control Press. All right reserved.
引用
收藏
页码：1 / 8
页数：7
相关论文
共 24 条
  • [11] Han Y., Chen L., Wang Z., Et al., Distributed optimal power flow in direct current distribution network based on alternative direction method of multipliers with dynamic step size, Transactions of China Electrotechnical Society, 32, 11, pp. 26-37, (2017)
  • [12] Ding M., Li S., Huang K., Probabilistic load flow analysis based on Monte-Carlo simulation, Power System Technology, 25, 11, pp. 10-14, (2001)
  • [13] Morales J.M., Perez-Ruiz J., Point estimate schemes to solve the probabilistic power flow, IEEE Transactions on Power Systems, 22, 4, pp. 1594-1601, (2007)
  • [14] Zou B., Xiao Q., Solving probabilistic optimal power flow problem using quasi Monte Carlo method and ninth-order polynomial normal transformation, IEEE Transactions on Power Systems, 29, 1, pp. 300-307, (2014)
  • [15] Pan W., Liu W., Yang Y., Point estimate method for probabilistically optimal power flow computation, Proceedings of the CSEE, 28, 16, pp. 28-33, (2008)
  • [16] Dai J., Bai X., Bao H., Probabilistic optimal power flow by considering the uncertainty of source and demand side, Modern Electric Power, 33, 1, pp. 34-40, (2016)
  • [17] Yu H., Zhong Z., Huang J., Et al., Digital substation communication network simulation based on epochs, Automation of Electric Power Systems, 33, 21, pp. 32-35, (2009)
  • [18] Chen H., Iyer A., Harle Y.R., Et al., Dynamic grid power routing using controllable network transformers (CNT) with decoupled closed-loop controller, IEEE Transactions on Industry Applications, 51, 3, pp. 2361-2372, (2015)
  • [19] Nguyen P., Kling W., Ribeiro P., Smart power router: a flexible agent-based converter interface in active distribution networks, IEEE Transactions on Smart Grid, 2, 3, pp. 487-495, (2011)
  • [20] Sanchez-Squella A., Ortega R., Grino R., Et al., Dynamic energy router, IEEE Control System, 30, 6, pp. 72-80, (2010)
123