Stochastic Scenarios Generation for Wind Power and Photovoltaic System Based on Generative Moment Matching Network

被引：0
|
作者：
Zhu R. [1 ]
Liao W. [2 ]
Wang Y. [3 ]
Wang Y. [3 ]
Chen J. [5 ]
机构：
[1] School of Electrical Engineering, Tibet Agriculture and Animal Husbandry University, Linzhi
[2] Department of Energy Technology, Aalborg University, Aalborg
[3] State Grid Tianjin Chengxi Electric Power Supply Branch, Tianjin
[4] School of Electrical Engineering and Computer Science, KTH, Stockholm
[5] School of Software and Microelectronics, Peking University, Beijing
来源：
Gaodianya Jishu/High Voltage Engineering | 2022年 / 48卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Auto-encoder; Data driven; Deep learning; Generative moment matching network; Renewable energy; Scenarios generation;
D O I：
10.13336/j.1003-6520.hve.20201370
中图分类号：
学科分类号：
摘要：
The penetrations of wind power and photovoltaic system in distribution network are increasing year by year. The randomness and fluctuation of their output powers bring great challenges to the planning and operation of distribution networks. Aimed at the uncertainty of output power of renewable energy, a stochastic scenarios generation method for photovoltaic and wind power based on generative moment matching network (GMMN) is proposed. In this method, the maximum mean discrepancy is adopted as the loss function of the generator, and the auto-encoder is adopted to reduce the dimension of the generated stochastic scenarios, so as to solve the low dimensional manifold problem of the high-dimensional power curves. According to the characteristics of the power curves, the network structure suitable for the stochastic scenarios generation of renewable energy is designed. The effectiveness and adaptability of the proposed method are verified by real data. The simulation results show that the proposed GMMN can not only simulate the shape characteristics, probability distribution characteristics, fluctuation, and spatial-temporal correlation of the photovoltaic and wind power curves, but also has universality. It can be applied to the random field generation tasks for different generation units only by adjusting the structure and parameters of the network. © 2022, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：374 / 384
页数：10
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