文章摘要
卢苑,林舜江,刘明波,等.考虑风电场出力波动区间的电力系统静态电压稳定裕度计算[J].电力系统自动化,2018,42(8):92-100. DOI: 10.7500/AEPS20171103002.
LU Yuan,LIN Shunjiang,LIU Mingbo, et al.Computation of Static Voltage Stability Margin for Power System Considering Fluctuation Interval of Wind Farm Output[J].Automation of Electric Power Systems,2018,42(8):92-100. DOI: 10.7500/AEPS20171103002.
考虑风电场出力波动区间的电力系统静态电压稳定裕度计算
Computation of Static Voltage Stability Margin for Power System Considering Fluctuation Interval of Wind Farm Output
DOI:10.7500/AEPS20171103002
关键词: 风电  静态电压稳定  稳定裕度区间  仿射区间算法  区间相关性  灵敏度
KeyWords: wind power  static voltage stability  stability margin interval  affine-interval algorithm  interval correlation  sensitivity
上网日期:2018-03-06
基金项目:国家重点基础研究发展计划(973计划)资助项目(2013CB228205);广东省自然科学基金资助项目(2015A030313233);中央高校基本科研业务费专项资金资助项目(2015ZM106)
作者单位E-mail
卢苑 华南理工大学电力学院, 广东省广州市 510640  
林舜江 华南理工大学电力学院, 广东省广州市 510640 linshj@scut.edu.cn 
刘明波 华南理工大学电力学院, 广东省广州市 510640  
杨智斌 华南理工大学电力学院, 广东省广州市 510640  
摘要:
      大规模风电接入电力系统使得静态电压稳定裕度(SVSM)计算需要考虑风电场出力随机波动的影响。采用区间数描述风电场出力的随机波动特性,基于连续潮流法和仿射区间算法,提出了含风电场的电力系统SVSM区间的计算方法。首先根据电压稳定极限点分岔类型的不同将风电场出力波动区间进行分段,然后采用连续潮流法分别求出每段区间中心值对应的SVSM,并根据相应分岔点类型计算出SVSM对风电场出力的灵敏度,最后结合线性规划模型优化噪声元范围以获得区间收缩结果,从而得到SVSM区间。采用相关角表示不同风电场出力区间的相关性,根据分岔点类型相同与否,采用不同的处理方法计算得到SVSM区间。以IEEE 39节点系统和某964节点实际省级电网为例,并与蒙特卡洛模拟方法比较,结果表明所提出方法计算得到的SVSM区间具有较高的精度,且计算量明显减小。
Abstract:
      Large scale wind power integrated into power system makes the static voltage stability margin(SVSM)computation need to consider the impact of wind farm output fluctuations. The interval number is used to describe the random fluctuation characteristics of wind farm output, the method of calculating the interval value of SVSM of power system with wind farm integration is proposed based on continual power flow method and affine-interval algorithm, Firstly, the wind power output fluctuation interval is segmented according to the different bifurcation types of voltage stability limit points. Then the continual power flow method is used to calculate SVSM corresponding to each interval central value, and then the sensitivity of SVSM to wind farm output is calculated according to the corresponding bifurcation type. Finally, the results of interval shrinkage are obtained by the optimization of noise element range based on linear programming models, and the interval of SVSM is obtained. Correlation angle is used to express the correlation of different wind farm output intervals. According to the types of bifurcation are same or not, different methods are used to calculate the interval of SVSM. IEEE 39-bus system and a provincial 964 node power grid are taken as examples, and compared with Monte Carlo simulation method, the results show that the interval of SVSM obtained by the proposed method has higher accuracy and the computation burden is reduced obviously.
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