引用本文:张晓英,贾磊,王琨,张蜡宝,陈伟.基于切片反射采样的风电并网系统可用输电能力概率风险评估[J].电力自动化设备,2017,37(7):
ZHANG Xiaoying,JIA Lei,WANG Kun,ZHANG Labao,CHEN Wei.Probabilistic risk assessment of ATC based on reflective slice sampling for power system with wind farm[J].Electric Power Automation Equipment,2017,37(7):
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基于切片反射采样的风电并网系统可用输电能力概率风险评估
张晓英1, 贾磊1, 王琨2, 张蜡宝3, 陈伟1
1.兰州理工大学 电气工程与信息工程学院,甘肃 兰州 730050;2.国网甘肃省电力公司电力科学研究院,甘肃 兰州 730030;3.南京大学 电子科学与工程学院,江苏 南京 210093
摘要:
如何全面地考虑风电功率的波动性和随机性,提高风电场出力概率分布采样的速度及精度,进而有效评估可用输电能力(ATC)成为亟待解决的问题。为此,提出基于切片反射采样(RSS)的改进方法对风电并网系统的ATC进行概率风险评估。对风电场出力进行优化建模,利用RSS对模型进行采样并构建样本空间,将样本值代入潮流方程进行最优潮流计算,并对ATC评估指标进行统计分析。含有风电场模型的IEEE 30节点系统仿真结果表明,与常规Gibbs采样算法相比,所提方法在确保计算效率的同时显著提高了采样方法的准确度。
关键词:  风电并网  风电场  可用输电能力  切片反射采样  Gibbs采样  加权高斯混合分布  风险评估
DOI:10.16081/j.issn.1006-6047.2017.07.005
分类号:TM614
基金项目:国家自然科学基金资助项目(51267012)
Probabilistic risk assessment of ATC based on reflective slice sampling for power system with wind farm
ZHANG Xiaoying1, JIA Lei1, WANG Kun2, ZHANG Labao3, CHEN Wei1
1.School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;2.State Grid Gansu Electric Power Company Electric Power Research Institute, Lanzhou 730030, China;3.School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
Abstract:
In order to enhance the speed and precision of the probabilistic distribution sampling of wind-farm power-output and further to effectively assess the ATC(Available Transfer Capability),it is essential to comprehensively consider the fluctuation and randomness of wind power, for which, an improved method based on RSS(Reflective Slice Sampling) is proposed to assess the probabilistic risk of the ATC of power system with wind farm. An optimal model of wind-farm power-output is established and then sampled based on RSS to construct a sample space. Samples are then applied to the power flow equations for calculating the optimal power flow and the ATC assessment indexes are statistically analyzed. The simulative results of IEEE 30-bus system with a wind farm show that, compared with traditional Gibbs sampling, the proposed method improves the precision of sampling method while ensures the calculation efficiency.
Key words:  wind farm integration  wind farms  available transfer capability  reflective slice sampling  Gibbs sampling  weighted Gaussian mixture distribution  risk assessment

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