Open AccessPhotovoltaic–wind joint power probability model based on multiple temporal and spatial scale
Author(s) -
Wu Feng,
Jiang Zenan,
Qian Junxia,
Shi Linjun,
Lin Keman
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.5491
Subject(s) - wind power , photovoltaic system , power (physics) , renewable energy , probabilistic logic , wind speed , computer science , environmental science , meteorology , engineering , electrical engineering , artificial intelligence , geography , physics , quantum mechanics
With an increasing number of renewable energy integrated into the power system, the impact of intermittent and random wind power and photovoltaic power on the power system is becoming larger and larger. To study the probabilistic correlation between wind power and photovoltaic power on different temporal and spatial scale, a joint probability model needs to be established. On the basis of the third‐order Gaussian mixture model, a photovoltaic–wind joint power probability model is proposed in this study. By analysing the statistical probability characteristics of the measured power output data, this method avoids the error accumulation caused by the individual modelling of the wind and photovoltaic generators and considers the joint correlation between wind power and photovoltaic power. On the basis of this model, the corresponding time series of wind power and photovoltaic power is calculated. The results indicated that the coastal joint power probability model and the inland joint power probability model are quite different on the same time scale, the parameters of joint probability model on different time scales are close to each other. The effectiveness of the proposed method is verified by the measured data.