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Wind power time series simulation model based on typical daily output processes and Markov algorithm

2022-03-16

论文推荐】大唐新能源丛智慧等：基于典型日出力场景和马尔科夫算法的风电功率时间序列模拟模型

文章导读

风电功率时间序列模拟是可再生能源功率分配规划、运行模式计算和安全评估的关键环节。传统的单点建模方法在每一时刻离散地生成模拟结果，而忽略了风电日出力特性，无法兼顾建模精度和效率。针对这一问题，提出了一种基于典型日出力场景和马尔科夫算法的风力发电时间序列模拟模型。首先，提出了一种基于时间序列相似度和改进K-means聚类算法的典型日出力场景划分方法。其次，以典型日出力场景为状态变量，建立了基于马尔可夫算法的风电功率时间序列模拟模型。最后，以中国某风电场实测数据为例进行了分析。通过与传统方法的比较，验证了该模型的有效性和适用性。对比结果表明，该模型生成的风电功率时间序列的统计特性、概率分布特性和自相关性均优于传统方法，且能够有效提升建模效率。

Wind power time series simulation model based on typical daily output processes and Markov algorithm

基于典型日出力场景和马尔科夫算法的风电功率时间序列模拟模型

Zhihui Cong¹, Yuecong Yu², Linyan Li², Jie Yan²

(1.Datang (Chifeng) New Energy Co., Ltd, Chifeng 024000, P. R. China 2.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, P. R. China)

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Wind power time series simulation model based on typical da

Abstract

The simulation of wind power time series is a key process in renewable power allocation planning, operation mode calculation, and safety assessment. Traditional single-point modeling methods discretely generate wind power at each moment; however, they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency. To resolve this problem, a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed. First, a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented. Second, considering the typical daily output processes as status variables, a wind power time series simulation model based on Markov algorithm is constructed. Finally, a case is analyzed based on the measured data of a wind farm in China. The proposed model is then compared with traditional methods to verify its effectiveness and applicability. The comparison results indicate that the statistical characteristics, probability distributions, and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods. Moreover, modeling efficiency considerably improves.

Keywords

Wind power, Time series, Typical daily output processes, Markov algorithm, Modified K-means clustering algorithm.

Fig.1 Modeling process

Fig.2 Optimal number of clusters

Fig.3 Results of original K-means clustering algorithm based on eigenvalues

Fig.4 Results of K-means clustering algorithm based on Euclidean distance

Fig.5 Results of modified K-means clustering algorithm with time series similarity

Fig.6 Partial simulation results of wind power time series

Fig.7 Probability distribution of simulation results of different models and historical wind power series

Fig.8 ACC of simulation results of different models and historical wind power series

本文引文信息

Zhihui Cong, Yuecong Yu, Linyan Li, Jie Yan (2022) Wind power time series simulation model based on typical daily output processes and Markov algorithm. Global Energy Interconnection, 5(1):44-54

丛智慧，于越聪，李林晏，阎洁（2022）基于典型日出力场景和马尔科夫算法的风电功率时间序列模拟模型. 全球能源互联网（英文），5(1):44-54

Biographies

Zhihui Cong

received his master’s degree from North China Electric Power University (NCEPU), Baoding, China, in 2018. He is now the director of safety and environmental protection supervision department of Datang (Chifeng) New Energy Co., Ltd. His major research interests include new energy power generation technology and management.

Yuecong Yu

received her bachelor degree from North China Electric Power University (NCEPU), Beijing, China, in 2019 and is now working toward a master’s degree at NCEPU, Beijing, China. Her major research interests include wind energy resource assessment and wind power forecasting.

Linyan Li

received his bachelor degree from North China Electric Power University (NCEPU), Beijing, China, in 2015 and is now working toward a master’s degree at NCEPU, Beijing, China. His major research interests include Wind-solar output characteristics analysis and wind-solar complementary system optimization scheduling.

Jie Yan

received her joint educated Ph.D. degree in renewable & clean energy from North China Electric Power University (NCEPU), Beijing, China and University of Bath, Bath, U.K. in 2016. She is currently an associate professor with the school of renewable energy in NCEPU. Her major research interest includes wind/solar power

forecasting, wind farm control and multi-energy operation.

编辑：王彦博

审核：王伟

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Wind power time series simulation model based on typical daily output processes and Markov algorithm

论文推荐】大唐新能源 丛智慧等：基于典型日出力场景和马尔科夫算法的风电功率时间序列模拟模型

郑重声明

论文推荐】大唐新能源丛智慧等：基于典型日出力场景和马尔科夫算法的风电功率时间序列模拟模型