确定风电厂最优落点的概率特征值灵敏度方法

doi:10.12204/j.issn.1000-7229.2021.10.013

电力建设 ›› 2021, Vol. 42 ›› Issue (10): 119-128.doi: 10.12204/j.issn.1000-7229.2021.10.013

确定风电厂最优落点的概率特征值灵敏度方法

和萍¹(), 刘东哲¹, 文福拴², 方祺元¹, 郑明明¹, 李钊¹

1.郑州轻工业大学电气信息工程学院,郑州市 450002
2.浙江大学电气工程学院,杭州市 310027

收稿日期:2021-01-07 出版日期:2021-10-01 发布日期:2021-10-09
作者简介:和萍(1980),女,博士,副教授,主要研究方向为电力系统稳定性分析控制、新能源并网,E-mail: hplkz@126.com。
刘东哲(1991),男,硕士研究生,主要研究方向为电力系统稳定性分析与控制、变电站智能化。
文福拴(1965),男,教授,博士生导师,主要研究方向为电力系统故障诊断与系统恢复、电力经济与电力市场、智能电网与电动汽车等。
方祺元(1997),男,硕士研究生,主要研究方向为电力系统稳定性分析与控制、新能源并网。
郑明明(1995),男,硕士研究生,主要研究方向为风电并网后电力系统稳定性分析与控制。
李钊(1996),男,硕士研究生,主要研究方向为风电并网后电力系统稳定性分析与控制、储能建模。
基金资助:
国家自然科学基金资助项目(51607158);河南省科技攻关研究项目(202102210305);郑州轻工业大学重点培育计划项目(2020ZDPY0204)

Probability Eigenvalue Sensitivity Indices for Determining Optimal Access Points of Wind Farms

HE Ping¹(), LIU Dongzhe¹, WEN Fushuan², FANG Qiyuan¹, ZHENG Mingming¹, LI Zhao¹

1. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
2. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Received:2021-01-07 Online:2021-10-01 Published:2021-10-09
Supported by:
National Natural Science Foundation of China(51607158);Henan Science and Technology Research Project(202102210305);Key Project of Zhengzhou University of Light Industry(2020ZDPY0204)

摘要/Abstract

摘要：

随着电力系统中风电渗透率的不断提高,电力系统潮流分布的复杂性和不确定性随之增加。为解析含风电互联电力系统的稳定运行特性,提升接纳间歇性可再生能源发电的能力,文章提出利用概率灵敏度指标优化配置风电落点。首先,针对互联系统多种运行方式,研究系统状态矩阵与留数之间关系,构建系统概率灵敏度指标,辨别引起系统低频振荡的相关因素和强相关电机组。之后,采用特征根和动态时域仿真,对比分析“增加”和“替换”风电两种配置方案对系统振荡特性的影响,并确定风电入网的最优配置落点。最后,采用算例对所提方法的可行性与有效性进行仿真验证。

关键词: 互联电力系统, 概率灵敏度, 特征根分析, 小干扰稳定, 风电场, 落点

Abstract:

With the ever-increasing penetration of wind power in an interconnected power system, the power flow distribution is becoming more and more complicated and uncertain. To analyze the stable operating characteristics of an interconnected power system with wind power integrated and enhance the accommodation capability for intermittent renewable generation, this paper applies probabilistic eigenvalue sensitivity indices to determine optimal access points of wind farms. Under multiple operation conditions of an interconnected power system, the relationship between the system state matrix and the residual index is studied, the probability sensitivity indices are developed, and the correlation factors and strongly related units that cause the low-frequency oscillation of the system are identified. The eigenvalue analysis method and dynamic time-domain simulation are used to analyze and compare the influences of “adding” and “replacing” wind power configuration schemes on the system oscillation characteristics, and to determine the optimal access points of wind farms. Finally, two numerical examples are employed to demonstrate the feasibility and efficiency of the proposed approach.

Key words: interconnected power system, probabilistic sensitivity, eigenvalue analysis, small-signal stability, wind farm, access point

中图分类号:

TM74

和萍, 刘东哲, 文福拴, 方祺元, 郑明明, 李钊. 确定风电厂最优落点的概率特征值灵敏度方法[J]. 电力建设, 2021, 42(10): 119-128.

HE Ping, LIU Dongzhe, WEN Fushuan, FANG Qiyuan, ZHENG Mingming, LI Zhao. Probability Eigenvalue Sensitivity Indices for Determining Optimal Access Points of Wind Farms[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(10): 119-128.

图/表 19

图1 特征值实部概率分布图

Fig.1 Probability distribution of real part of the eigenvalues

图2 期望特征值的分布区域

Fig.2 Desired eigenvalue distribution region

图3 WSCC 3机9节点系统

Fig.3 The WSCC 3-machine 9-bus system

表1 对应于不同输入信号的概率灵敏度指标

Table 1 PSIs corresponding to different input signals

图4 特征值的概率分布

Fig.4 Probability distribution of eigenvalues

表2 不同输入信号下 α ' k和 ξ ' k的概率灵敏度指标PSIs

Table 2 PSIs of α ' k and ξ ' k corresponding to different input signals

图5 双馈风电机组结构

Fig.5 Structure of a DFIG

表3 电力系统稳定器参数

Table 3 Parameters of PSSs

图6 风电场的“接入”和“替换”模型

Fig.6 Power system with “addition” or “replacement” of a wind farm

图7 DFIG输出增加时两种振荡模式系统阻尼比变化

Fig.7 Damping ratio changes for two oscillation modes with DFIG output increased

表4 不同工况下的机电振荡模式

Table 4 Electro-mechanical oscillatory modes under different operating conditions

表5 不同DFIG输出时特征值分析(DFIG接入母线3)

Table 5 Partial results of eigenvalues analysis under various DFIG outputs (DFIG at Bus 3)

表6 不同DFIG输出时的机电振荡模式分析(DFIG接入母线2)

Table 6 Electro-mechanical oscillatory modes under various DFIG outputs (DFIG at Bus 2)

图8 不同方案下的响应曲线

Fig.8 Response curves under different schemes

表7 不同方案下特征值分析 (DFIG接入母线3)

Table 7 Partial results of eigenvalues analysis under different scenarios (DFIG at Bus 3)

图9 不同方案下G1的输出动态响应曲线

Fig.9 Active power response curves of G1 under different scenarios

表8 不同运行情况下阻尼比对应功率信号的PSIs指标

Table 8 PSIs of the damping ratios corresponding to power signal under different operating conditions

表9 不同模式对应的强相关发电机组及最大 H m a x P S I

Table 9 The SCG corresponding to different modes with its H m a x P S I

表10 DFIG接入不同母线时的振荡模式分析及 H m a x P S I

Table 10 Electro-mechanical modes for the system with DFIG connected to different PCC buses

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确定风电厂最优落点的概率特征值灵敏度方法

Probability Eigenvalue Sensitivity Indices for Determining Optimal Access Points of Wind Farms

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