A C-DCGAN-based method for generating extreme risk scenarios of high percentage new energy systems

Author:

Li Yanchun1,Li Peng1,Yang Tianmeng1,Chen Zelong1,Song Xuan2,Zhao Yumin1,Liu Jicheng1,Feng Wei1

Affiliation:

1. Northeast Branch of State Grid Corporation of China,Shenyang,China

2. Harbin Turbine Company Limited,Harbin,China

Funder

State Grid Corporation of China

Publisher

IEEE

Reference13 articles.

1. Research on distribution network planning in the context of integrated energy [J];Lidi;Electricity supply and use,2018

2. A classical scenario set generation algorithm for wind power/photovoltaic based on Wasserstein distance and improved K-medoids clustering [J];Qun;Chinese Journal of Electrical Engineering,2015

3. Research on source-load scene generation method based on AM- GAN[J];Yuhan;Automation Instrumentation,2022

4. Deep embedded clustering based water-optical load uncertainty source scene generation method [J];Jingxian;Chinese Journal of Electrical Engineering,2020

5. Building air-conditioning load scenario generation method based on conditional time series generation adversarial network[J];Shuang;Grid Technology,2022

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