计及风电场发电功率不确定性的电力系统模糊潮流

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摘要风速随机性与间歇性的存在, 给大型风电场并网后电力系统的稳定运行提出了新的问题和挑战。本文应用概率统计及模糊集的相关理论对计及风电场发电功率不确定性的电力系统模糊潮流计算进行了研究。首先取给定参数值的威布尔曲线作为风速概率统计分布, 并基于随机模糊相容性原理, 将其转换成风速的可能性分布, 再结合笼型异步风力发电机的风速-功率关系特性曲线建立了风力发电机组出力的非线性模糊模型；并在对该模糊模型线性化的基础上, 采用基于潮流分析灵敏度的方法对系统模糊交流潮流的可能性分布进行了求解。最后给出了计及风电场的IEEE14算例系统下的仿真结果。

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	洪芦诚
	石立宝
	姚良忠
	MasoudBazargan
	倪以信

关键词 ：风速间歇性, 笼型异步风力发电机, 概率密度, 模糊集理论, 交流潮流

Abstract：The intermittent and fluctuant nature of wind generation is posing great new issues and challenges to the planning and operation of interconnected power systems with high penetration of grid-connected wind farms. In this paper, the probability theory and fuzzy sets theory are employed to carry out systematic and thorough studies on power system fuzzy load flow calculation incorporating uncertainties of wind farm generation. The Weibull distribution curve with specified parameters is simulated as the wind speed probability density distribution, and with the possibility/probability consistency principle(PPCP), the wind speed probability density distribution is first transformed to the wind speed possibility distribution. In accordance with simplified power-speed curve of squirrel cage induction generator (SCIG), the fuzzy model of SCIG power output is then built. The proposed fuzzy model is linearized further for simplicity. The sensitivity analysis based method is also employed to carry out the possibility distribution of fuzzy load flow solutions. Numerical results based on a modified IEEE 14-bus system demonstrate the validity and effectiveness of the proposed models and methods.

Key words： Wind intermittence SCIG probability density fuzzy set theory AC load flow

收稿日期: 2009-04-21 出版日期: 2014-03-04

PACS:

TM744

基金资助:国家重点基础研究专项经费(2004CB217900)和高等学校博士学科点专项科研基金(200800031068)资助项目

作者简介: 洪芦诚男, 1985年生, 硕士, 主要研究方向为风力发电及电力系统稳态分析。石立宝男, 1971年生, 博士, 副教授, 研究方向为大规模电力系统安全经济运行, 计及FACTS的电力系统动态安全评估, 计算智能/网格技术及其在电力系统中的应用, EMS/DTS系统集成。

引用本文:

洪芦诚, 石立宝, 姚良忠, MasoudBazargan, 倪以信. 计及风电场发电功率不确定性的电力系统模糊潮流[J]. 电工技术学报, 2010, 25(8): 116-122. Hong Lucheng, Shi Libao, Yao Liangzhong, Masoud Bazargan, Ni Yixin. Fuzzy Modelling and Solution of Load Flow Incorporating Uncertainties of Wind Farm Generation. Transactions of China Electrotechnical Society, 2010, 25(8): 116-122.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2010/V25/I8/116

[1] Borkowska B. Probabilistic load flow[J]. IEEE Trans. Power App. Syst., 1974, PAS(93): 752-759.
[2] 胡泽春, 王锡凡. 考虑负荷概率分布的随机最优潮流方法[J]. 电力系统自动化, 2007, 31(16): 14-18.
[3] Hatziagryriou N D, Karakatsanis T S, Papadopoulos M. Probabilistic load flow in distribution systems containing dispersed wind power generation[J]. IEEE Trans. on Power Systems, 1993, 8(l): 159-165.
[4] 吴义纯, 丁明, 李生虎. 风电场对发输电系统可靠性影响的评估[J]. 电工技术学报, 2004, 19(11): 72- 76.
[5] Miranda V, Saraiva J T. Fuzzy modeling of power system optimal load flow[J]. IEEE Trans. on PWRS, 1992, 7(2): 843-849.
[6] 张焰, 陈章潮. 电网规划中的模糊潮流计算[J]. 电力系统自动化, 1998, 22(3): 20-22.
[7] 陈海焱, 陈金富, 段献忠. 含风电场电力系统经济调度的模糊建模及优化算法[J]. 电力系统自动化, 2006, 30(2): 22-26.
[8] Bowden G J, Barker P R, Shestopal V O, et al. The Weibull distribution function and wind power statistics[J]. Wind Engineering, 1983, 7:85-98.
[9] Jangamshetti S H, Rau V. Optimum siting of wind turbine generators[J]. IEEE Trans. on Energy Conver- sion, 2001, 16(1):8-13.
[10] 丁明, 吴义纯, 张立军. 风电场风速概率分布参数计算方法的研究[J]. 中国电机工程学报, 2005, 25 (10): 107-110.
[11] Dubois D, Prade H. On several representations of an uncertain body of evidence[C]. In: M.M. Gupta and E. Sanchez, Editors, Fuzzy Information and Decision Processes, North Holland, Amsterdam, 1982: 167- 181.
[12] Dubois D, Foulloy L, Mauris G, et al. Probability- possibility transformations, triangular fuzzy sets, and probabilistic inequalities[J]. Reliable Computing, 2004, 10(4): 273-297.
[13] 吴俊玲, 周双喜, 孙建锋, 等. 并网风力发电场的最大注入功率分析[J]. 电网技术, 2004, 28(20): 28-32.
[14] 王海超, 周双喜, 鲁宗相, 等. 含风电场的电力系统潮流计算的联合迭代方法及应用[J]. 电网技术, 2005, 29(18): 59-62.
[15] 张伯明, 陈寿孙. 高等电力网络分析[M]. 北京: 清华大学出版社, 1996.