引用本文:廖芷燕,李银红.基于R藤Copula-DBN时空相关性建模的风光荷功率概率预测[J].电力自动化设备,2022,42(3):
LIAO Zhiyan,LI Yinhong.Probabilistic forecasting of wind-photovoltaic-load power based on temporal-spatial correlation modelling of Regular Vine Copula-DBN[J].Electric Power Automation Equipment,2022,42(3):
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基于R藤Copula-DBN时空相关性建模的风光荷功率概率预测
廖芷燕, 李银红
华中科技大学 电气与电子工程学院,湖北 武汉 430074
摘要:
计及时空相关性的多维风光荷功率概率预测可全面描述风光荷不确定性,为电力系统安全稳定和经济运行提供保障。提出一种基于R藤Copula-动态贝叶斯网络(DBN)时空相关性建模的风光荷功率概率预测方法。基于R藤Copula模型和传递熵刻画多维变量的空间相关性,建立初始状态的贝叶斯网络;将初始贝叶斯网络在时间点序列上进行延拓,构建可刻画风光荷功率时间自相关性和空间相关性的DBN;将所建模型应用于计及时空相关性的高维风光荷功率概率预测。算例仿真验证了所提方法的有效性。
关键词:  时空相关性  动态贝叶斯网络  R藤Copula  概率预测
DOI:10.16081/j.epae.202112021
分类号:TM73
基金项目:
Probabilistic forecasting of wind-photovoltaic-load power based on temporal-spatial correlation modelling of Regular Vine Copula-DBN
LIAO Zhiyan, LI Yinhong
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract:
The probabilistic forecasting of multi-dimensional wind-photovoltaic-load power considering temporal-spatial correlation can fully describe the uncertainty of wind-photovoltaic-load, which provides guarantee for the safety, stability and economic operation of power system. A probabilistic forecasting method of wind-photo-voltaic-load power based on temporal-spatial correlation modelling of Regular Vine Copula-DBN(Dynamic Bayesian Network) is proposed. The spatial correlation of multi-dimensional variables is described based on Regular Vine Copula model and transfer entropy, and the Bayesian network in the initial state is established. The initial Bayesian network is extended on the time series to construct a DBN that can describe the temporal autocorrelation and spatial correlation of wind-photovoltaic-load. The model is applied for probabilistic forecasting of high-dimensional wind-photovoltaic-load power considering temporal-spatial correlation. Case simulation verifies the effectiveness of the proposed method.
Key words:  temporal-spatial correlation  dynamic Bayesian network  Regular Vine Copula  probabilistic forecasting

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