Photovoltaic–wind joint power probability model based on multiple temporal and spatial scale. Issue 20 (30th July 2018)

• Record Type: Journal Article
• Authors: Wu, Feng; Jiang, Zenan; Qian, Junxia; Shi, Linjun; Lin, Keman
• Is Part Of: IET generation, transmission & distribution. Volume 12:Issue 20(2018)
• Page Start: 4546
• Page End: 4550
• Publication Date: 2018-07-30
• Subjects: photovoltaic power systems -- wind power -- time series -- Gaussian processes -- mixture models -- probability -- turbogenerators; photovoltaic‐wind joint power probability model -- multiple temporal scales -- multiple spatial scales -- renewable energy -- power system -- intermittent wind power -- random wind power -- photovoltaic power -- probabilistic correlation -- third‐order Gaussian mixture model -- statistical probability characteristics -- measured power output data -- wind generator modelling -- photovoltaic generator modelling -- time series -- coastal joint power probability model -- inland joint power probability model; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.5491
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : 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.