基于概率分配法的含风电电力系统概率小扰动稳定分析

发布时间：2018-07-15 13:15

【摘要】：随着风电的不断开发利用和并网渗透率的不断提高,风电的随机性和波动性给电力系统带来了极大的不确定因素,使其安全稳定运行面临严峻挑战。因此,大规模风电并网后对电力系统的小干扰稳定性的影响值得关注。传统确定性小扰动分析方法由于不能考虑风电的不确定性而无法真实反映实际电力系统的安全运行水平。基于此,本文基于概率分配法(Probabilistic Collocation Methods,PCM)提出了一种计及风电不确定性的互联电力系统概率小扰动稳定分析方法,适用于研究风电接入对互联电力系统小扰动稳定性的影响。本文首先详细介绍了四种在电力系统不确定性评估中常用概率分析方法-蒙特卡洛法(Monte Carlo,MC)、点估计法(Point Estimation Methods,PEM)、半不变量法(Cumulant Methods,CM)和概率分配法,通过概率潮流计算比较上述方法的准确性和计算效率,总结PCM相对于其它方法在电力系统不确定性评估中的优势。其次,研究了PCM在传统电力系统概率小扰动分析中的应用。根据特征值分析法的机理和基本概念,建立输入变量的PCM模型,并详细介绍了基于PCM的传统电力系统概率小扰动特性分析流程。通过仿真表明:通过PCM法计算得到主导振荡模式的振荡频率以及阻尼比的概率分布特性,其结果与MC法保持一致,具有良好的准确性,且计算效率远高于MC法,可以准确高效地得到系统小扰动特性。最后,提出了一种基于PCM分析的含风电电力系统的概率小扰动稳定性分析方法。考虑到实际中风电出力的非连续性,建立了风电出力的PCM概率模型,提出了适用于含风电互联电力系统概率小扰动稳定性评估指标,并给出了基于PCM的风电互联电力系统的概率小扰动稳定分析的详细步骤。对含风电的新英格兰10机39节点系统进行仿真分析表明:本文所建立的风电出力PCM模型能够准确的反映风电的运行特性,随着风电接入容量的增加,整个风电系统的小扰动稳定性特性呈现降低趋势。
[Abstract]:With the continuous development and utilization of wind power and the continuous improvement of permeability, the randomness and volatility of wind power bring great uncertainties to the power system, which makes its safe and stable operation facing severe challenges. Therefore, the influence of large-scale wind power on the small disturbance stability of power system is worthy of attention. Because the traditional deterministic small disturbance analysis method can not take the uncertainty of wind power into account, it can not truly reflect the safe operation level of the actual power system. Based on the probabilistic collocation method (PCM), this paper presents a probabilistic small disturbance stability analysis method for interconnected power systems taking into account the uncertainty of wind power, which is suitable for studying the influence of wind power access on the stability of interconnected power systems. In this paper, four common probabilistic analysis methods in power system uncertainty assessment, Monte Carlo method, Point estimation method PEM, semi-invariant method and probabilistic distribution method, are introduced in detail. The accuracy and efficiency of the above methods are compared by the probabilistic power flow calculation, and the advantages of PCM compared with other methods in power system uncertainty assessment are summarized. Secondly, the application of PCM in traditional power system probabilistic small disturbance analysis is studied. According to the principle and basic concept of eigenvalue analysis, the PCM model of input variables is established, and the analysis flow of probabilistic small disturbance characteristic of traditional power system based on PCM is introduced in detail. The simulation results show that the oscillation frequency and the probability distribution of damping ratio of dominant oscillation mode are obtained by PCM method. The results are consistent with MC method and have good accuracy, and the calculation efficiency is much higher than that of MC method. The small disturbance characteristics of the system can be obtained accurately and efficiently. Finally, a method of probabilistic small disturbance stability analysis for wind power system based on PCM analysis is proposed. Considering the discontinuity of wind power output in practice, the PCM probability model of wind power output is established, and the evaluation index of probabilistic small disturbance stability for interconnected power system with wind power is proposed. The detailed steps of probabilistic small disturbance stability analysis for wind power interconnected power system based on PCM are given. The simulation analysis of a new England 10-machine 39-bus system with wind power shows that the wind power output PCM model can accurately reflect the operating characteristics of wind power, and with the increase of wind power access capacity, the PCM model can accurately reflect the operating characteristics of wind power. The small disturbance stability of the whole wind power system is decreasing.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM614;TM712

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