引入风电的交直流混合电力系统可靠性评估新方法

C. Wang, Haipeng Xie, Shiyu Liu, Z. Bie
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引用次数: 5

摘要

随着高压直流工程和可再生能源的快速发展,风电交直流混合电力系统的可靠性问题越来越受到人们的关注。为了描述风电的不确定性,本文提出风电BP神经网络模型来拟合实际风速的概率分布。数值结果表明,与Weibull分布模型等传统风电模型相比,BP神经网络模型更接近实际的风速概率分布。利用蒙特卡罗方法,得到了交直流混合系统的状态。在此基础上,考虑了交直流系统的相互作用,提出了一种新的混合系统最小减载模型。IEEE-RTS 96系统以中国北方实际风电数据为例进行了验证,说明该系统能够更准确地进行风电建模,并对集成风电的交直流混合电力系统进行了全面的可靠性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power
With the rapid development of HVDC projects and renewable energy, reliability of AC/DC hybrid power system with wind power draws more and more attention. To depict the uncertainty of wind power, this paper proposes the wind power BP neural network model to fit the probability distribution of actual wind speed. Compared with traditional wind power models such as Weibull distribution model, the BP neural network model is closer to the actual probability distribution of wind speed according to numerical results. By using Monte Carlo method, the AC/DC hybrid system states are obtained. Then considering the interaction between AC and DC system, a novel minimum load shedding model of hybrid system with HVDC is proposed. IEEE-RTS 96 system is testified with actual Northern China wind data, which illustrates a more accurate wind power modeling as well as a comprehensive reliability evaluation on AC/DC hybrid power system integrated with wind power.
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