广东工业大学学报 ›› 2018, Vol. 35 ›› Issue (02): 63-68,94.doi: 10.12052/gdutxb.170104

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含风电场最优潮流及其关键技术研究综述

邱明晋, 陈璟华, 唐俊杰   

  1. 广东工业大学 自动化学院, 广东 广州 510006
  • 收稿日期:2017-05-23 出版日期:2018-03-09 发布日期:2018-03-13
  • 作者简介:邱明晋(1992-),男,硕士研究生,主要研究方向为电力系统运行与控制.
  • 基金资助:
    中央财政支持地方高校发展专项资金项目(粤财教[2016]202号)

An Overview of Optimal Power Flow with Wind Farm and Relevant Key Technology

Qiu Ming-jin, Chen Jing-hua, Tang Jun-jie   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2017-05-23 Online:2018-03-09 Published:2018-03-13
  • Supported by:
     

摘要: 大型风电场并网发电给电网带来清洁能源的同时也使电力系统安全可靠运行面临诸多挑战,研究含风电场最优潮流对于提高电力系统稳定运行与能源利用率具有重要意义. 含风电场最优潮流是一个新型的高维、非线性的复杂优化问题,且由于风电的随机性、间歇性和不可控性使其复杂程度和计算难度进一步加大. 针对这一问题,本文通过数学模型和求解方法这一主线,阐述了含风电场最优潮流的发展现状以及因风电的随机性对电力系统造成的影响;总结了对风电场处理的现有研究技术,分析了求解含风电场最优潮流的各种算法的优点与不足;在此基础上对未来含风电场最优潮流的研究方向进行展望.

关键词: 电力系统, 风电场, 最优潮流, 人工智能算法, 经典解算法

Abstract: Large-scale wind farm brings clean energy to the grid, and at the same time also many challenges to the power system's safe and reliable operation. A research is conducted on the optimal power flow with wind farm for improving the stability and energy efficiency of power system operation is great significance. The optimal power flow with wind farms is a new complex problem of high dimension and nonlinearity, and the complexity and computational complexity of optimal power flow are further increased due to the randomness, intermittency and uncontrollability of wind power. With this problem in view, the development of the optimal power flow with wind farms and the influence of the randomness of wind power on the power system through the main line of mathematical model and solving method are expounded, and the existing research on wind farm treatment summarized, and the advantages and disadvantages of various algorithms for solving the optimal power flow with wind farm analyzed. On this basis, the future research direction of optimal power flow with wind farm is prospected.

Key words: power system, wind farm, optimal power flow, artificial intelligence algorithm, classical solution algorithm

中图分类号: 

  • TM744
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