Stochastic Optimal Power Flow with Wind Generator Based on Stochastic Response Surface Method (SRSM) and Interior Point Methods

被引:0
|
作者
Tan, Ying [1 ]
Ma, Rui [1 ]
机构
[1] Changsha Univ Sci & Technol, Changsha, Hunan, Peoples R China
来源
2015 5TH INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES (DRPT 2015) | 2015年
关键词
probabilistic optimal power flow; wind power generator; stochastic response surface method; interior point methods; PROBABILISTIC LOAD FLOW; SYSTEM; SPEEDS; MODEL;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In order to describe the randomness of the wind speed, analyze its influence to the optimal power flow with wind power generator, and reduce simulation time, this paper proposes a calculation method for stochastic optimal power flow with wind generator based on stochastic response surface method (SRSM) and interior point methods. The stochastic response surface method is applied to transform the problem of probability evaluations of the output power to the deterministic problem and shorten calculation time. The interior point method is used to deal with the randomness of wind speed and solve the deterministic problem stochastic optimal power flow with wind generator. Compared with the Monte Carlo method on IEEE 14-bus systems, the stochastic response surface method requires smaller amount of computation and achieves higher accuracy. The results also indicate that the proposed compound method are more practical and effective in reducing simulation time.
引用
收藏
页码:2079 / 2083
页数:5
相关论文
共 50 条
  • [41] Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions
    Wang, Yuhong
    Zhou, Xu
    Shi, Yunxiang
    Zhou, Chenyu
    Jiang, Qiliang
    Zheng, Zongsheng
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2024, 163
  • [42] Multi-objective mean-variance-skewness model for nonconvex and stochastic optimal power flow considering wind power and, load uncertainties
    Chen, J. J.
    Wu, Q. H.
    Zhang, L. L.
    Wu, P. Z.
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2017, 263 (02) : 719 - 732
  • [43] Probabilistic Energy Flow Calculation for Integrated Energy Systems Based on Radial Basis Function-stochastic Response Surface Method
    Chen Q.
    Zhang S.
    Cheng H.
    Wang S.
    Yuan K.
    Song Y.
    Han F.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2022, 42 (22): : 8075 - 8088
  • [44] Optimal maximum power point tracking of wind turbine doubly fed induction generator based on driving training algorithm
    Mostafa, Mohamed Abdelateef
    El-Hay, Enas A. A.
    ELkholy, Mahmoud M. M.
    WIND ENGINEERING, 2023, 47 (03) : 671 - 687
  • [45] Model Predictive Control Method for Multi-energy Flow of Smart Community Combined with Stochastic Response Surface Method
    Ma R.
    Wang J.
    Ma, Rui (marui818@126.com), 2018, Automation of Electric Power Systems Press (42): : 121 - 127
  • [46] A Low-carbon Probabilistic Optimal Energy Flow Analysis Method for Integrated Electricity and Natural Gas Systems Based on Stochastic Response Surface Method Improved by Decoupling Cross-terms
    Jiang Y.
    Ren Z.
    Chen Z.
    Wang P.
    Hu X.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2023, 43 (16): : 6205 - 6217
  • [47] A Discrete Point Estimate Method for Probabilistic Load Flow Based on the Measured Data of Wind Power
    Ai, Xiaomeng
    Wen, Jinyu
    Wu, Tong
    Lee, Wei-Jen
    2012 IEEE INDUSTRY APPLICATIONS SOCIETY ANNUAL MEETING (IAS), 2012,
  • [48] Two-point estimate method for probabilistic optimal power flow computation including wind farms with correlated parameters
    Li, Xue
    Cao, Jia
    Du, Dajun
    Communications in Computer and Information Science, 2013, 355 : 417 - 423
  • [49] Two-Point Estimate Method for Probabilistic Optimal Power Flow Computation Including Wind Farms with Correlated Parameters
    Li, Xue
    Cao, Jia
    Du, Dajun
    INTELLIGENT COMPUTING FOR SUSTAINABLE ENERGY AND ENVIRONMENT, 2013, 355 : 417 - 423
  • [50] Finite difference probabilistic slope stability analysis based on collocation-based stochastic response surface method (CSRSM)
    Ghedjati, Samir
    Lamara, Mohammed
    Houmadi, Youcef
    INNOVATIVE INFRASTRUCTURE SOLUTIONS, 2020, 5 (03)