采用改进马尔科夫链蒙特卡洛法的风电功率序列建模

doi:10.19595/j.cnki.1000-6753.tces.190044

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摘要建立能更好复现历史数据特征的风电功率序列模型,对计及高渗透率风能接入影响的电网规划和运行具有重要意义。该文首先通过研究面向随机变量（风电功率）建模的滑动平均滤波参数寻优方法和构建状态数优化决策模型,提出风电功率序列的自适应状态划分策略,客观划分历史数据。然后针对现有方法难以计及状态转移概率随状态持续时间增长而变化的问题,提出三维状态转移概率矩阵及其解维修正方法,抽样生成人造风电功率状态序列,进而在分析历史数据波动量及噪声概率分布的基础上,完善现有的波动特征叠加方法,模拟人造风电功率序列。与现有马尔科夫链蒙特卡洛（MCMC）法进行对比分析表明,该方法能更好地复现历史数据特征（转移、波动特征等）,在提高建模精度的同时,并未增加状态转移概率矩阵生成算法的时间复杂度。

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	朱晨曦
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关键词 ：风电功率序列, 状态划分策略, 三维状态转移概率矩阵, 马尔科夫链蒙特卡洛法, 转移特征, 波动特征

Abstract：Establishing a wind power time series model, which can better reproduce history data's characteristics, is important to the planning and operation of power systems with high permeability of wind energy. First, a self-adaptive strategy was proposed to objectively divide history data into different states, after the parameter optimization method of the moving average filter for the wind power random variable modelling was studied and a state number optimization decision model was constructed. Then, a three dimensional state transition probability matrix, which is used to generate a synthetic wind power state time series, was constructed and then revised along its third dimension for considering the fact that the transition probability changes with the state duration. Last, the existing fluctuation characteristic addition method, by which a synthetic wind power time series is generated, was completed based on analyzing the distributions of the fluctuation quantity and noise. Simulation shows the methodology proposed in this paper can generate synthetic wind power time series better preserving historical characteristics such as the transition and fluctuation characteristics than other Markov Chain Monte Carlo (MCMC) methods, and meanwhile can improve the modelling accuracy without increasing the time complexity of the transition matrix generation algorithm.

Key words： Time series of wind power strategy of state division three dimensional state transition probability matrix Markov chain Monte Carlo (MCMC) transition characteristic fluctuation characteristic

收稿日期: 2019-01-16 出版日期: 2020-02-12

PACS:

TM614

基金资助:国家重点研发计划项目（2018YFB0904200）和国家电网公司科技项目（SGDK0000NYJS1807745）资助

通讯作者: 张焰女,1958年生,教授,博士生导师,研究方向为电力系统规划及电力系统可靠性。E-mail：zhang_yan@sjtu.edu.cn

作者简介: 朱晨曦男,1992年生,博士研究生,研究方向为新能源并网、主动配电网运行优化及配电网可靠性评估。E-mail：benny_zcx@sjtu.edu.cn

引用本文:

朱晨曦, 张焰, 严正, 祝锦舟, 赵腾. 采用改进马尔科夫链蒙特卡洛法的风电功率序列建模[J]. 电工技术学报, 2020, 35(3): 577-589. Zhu Chenxi, Zhang Yan, Yan Zheng, Zhu Jinzhou, Zhao Teng. A Wind Power Time Series Modeling Method Based on the Improved Markov Chain Monte Carlo Method. Transactions of China Electrotechnical Society, 2020, 35(3): 577-589.

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