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
  • [21] Vectorization Implementation of Optimal Power Flow in Rectangular Form Based on Interior Point Method
    Qin, Zhijun
    Yang, Yude
    2008 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, VOLS 1-11, 2008, : 1147 - 1154
  • [22] An Extended Optimal Power Flow Measure for Unsolvable Cases Based on Interior Point Method
    Liu, Lin
    Wang, Xifan
    Ding, Xiaoying
    Li, Furong
    Fu, Min
    2009 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, VOLS 1-8, 2009, : 2357 - +
  • [23] Temperature-Dependent Optimal Power Flow Based on Simplified Interior Point Method
    Gao, Qin
    Wei, Zhinong
    Sun, Guoqiang
    Sun, Yonghui
    Zang, Haixiang
    2015 5TH INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES (DRPT 2015), 2015, : 765 - 769
  • [24] Optimal Power Flow of a Power System Incorporating Stochastic Wind Power Based on Modified Moth Swarm Algorithm
    Elattar, Ehab E.
    IEEE ACCESS, 2019, 7 : 89581 - 89593
  • [25] An Optimal Reactive Power Dispatch Strategy for Interior-point Method Based Wind Farms
    Li, Lixia
    Yao, Xingjia
    Wang, Xiaodong
    Liu, Yingming
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2017, 38 (05): : 1397 - 1404
  • [26] Power market oriented optimal power flow via an interior point method
    Xie, K
    Song, YH
    IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 2001, 148 (06) : 549 - 556
  • [27] An Optimal Power Flow Approach for Stochastic Wind and Solar Energy Integrated Power Systems
    Shafiq, Sundas
    Javaid, Nadeem
    Asif, Sikandar
    Ali, Farwa
    Chughtai, Nasir Hussain
    Khurshid, Nouman
    COMPLEX, INTELLIGENT, AND SOFTWARE INTENSIVE SYSTEMS, 2019, 772 : 292 - 304
  • [28] Optimal Power Flow Analysis of Power System for Petrochemical Enterprises Based on the Interior Point Method and PSASP
    Li Wenchen
    Huang Yanhao
    He Lei
    Gao Bo
    2018 CHINA INTERNATIONAL CONFERENCE ON ELECTRICITY DISTRIBUTION (CICED), 2018, : 2482 - 2487
  • [29] Stochastic Dynamic Power Flow Analysis Based on Stochastic Response Surfarce Method and ARMA-GARCH Model
    Zhung Nguyen-Hong
    Nakanishi, Yosuke
    2017 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2017,
  • [30] Stochastic Response Surface Method Addressing Correlated Wind Power for Probabilistic Evaluation of Voltage Stability
    Bao, Haibo
    Wei, Hua
    Guo, Xiaoxuan
    2016 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2016, : 1660 - 1664