考虑输出功率相关性的光伏电站典型场景生成方法研究
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TM76

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江苏省重点研发计划资助项目“基于物联网融合的用能互联网运行与交易应用科技示范”


A typical scene generation method considering output power correlation of photovoltaic power plants
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    摘要:

    含光伏的传统概率潮流计算常使用Beta、Weibull等分布模型,但这些模型难以精确体现不同场景下输出功率特性的变化。针对此问题,文中提出一种光伏输出功率多场景生成方法。该方法将多个光伏电站输出功率的相关性以及相关性跟随外界条件的变化纳入考量范围,可实现多个光伏电站输出功率的精确建模。首先,对光伏电站历史输出功率数据进行基于密度中心的聚类处理,产生的多个聚类中心作为输出功率场景;其次,对不同场景进行核密度估计,建立Copula函数以描述多个输出功率的概率分布;然后,对生成的多场景概率分布模型进行拉丁超立方采样,利用生成的样本进行概率潮流计算,评估含光伏的配电网运行状态;最后,分析算法的稳定性,讨论采样规模对计算结果的影响。结果表明,文中提出的多场景生成方法有助于提升光伏输出功率的建模精度和概率潮流计算精度。

    Abstract:

    Since quintessential probabilistic photovoltaic (PV) models like Weibull and Beta distribution are not able to reflect accurately the changes of PV station's output characteristics in different scenarios, a data-driven scenario generation model for distribution system is proposed. The model which is based on the algorithm of local density clustering (LDC), takes into account the correlation of multiple PV stations and the influence of external conditions, thus realizing the accurate modeling of multiple PV stations' output power. Firstly, LDC is used to classify historical data into several clusters. Secondly, kernel density function and copula function are utilized to build joint probabilistic density functions (PDF) in each cluster, and the data of joint PDF are processed by applying Latin Hypercube Sampling (LHS). Then, the samples are used for probabilistic power flow calculation to set up the model which estimates operational condition of distribution network. Finally, the stability of model is analyzed, and influence of sampling size on model is also discussed. It is shown that the proposed method in the paper improves the accuracy of modelling for PV station's output in distribution network, and it also reduces the error of probabilistic power flow calculation.

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陈颢元,蒋玮,韩俊.考虑输出功率相关性的光伏电站典型场景生成方法研究[J].电力工程技术,2021,40(2):11-17, 25

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历史
  • 收稿日期:2020-09-11
  • 最后修改日期:2020-10-28
  • 录用日期:2020-08-23
  • 在线发布日期: 2021-04-02
  • 出版日期: 2021-03-28