Electric vehicle charging current scenario generation based on generative adversarial network combined with clustering algorithm. (2nd June 2021)

• Record Type: Journal Article
• Authors: Yang, Fan; Yin, Shoupeng; Zhou, Suyang; Li, Dongdong; Fang, Chen; Lin, Shunfu
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-02
• Subjects: electric vehicle -- generative adversarial networks (GANs) -- K‐Means -- scenario generation; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.12971
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: The generation of charging current scenario is an important step in the operation and planning of power systems with high electric vehicle (EV) penetrations. With the development of the modeling method, a number of methods based on probabilistic models are applied to generate scenarios. Model‐based methods are often difficult to scale or sample. Data‐driven technologies use a large number of data to mine the mapping relationships, instead of explicitly specifying a model. In this paper, we proposed a data‐driven approach to generate scenarios using generative adversarial networks (GANs), which can learn the distribution of the charging current of EVs and obtain more abundant scenarios. The proposed method is applied to time‐series data from the charging current dataset of EVs. Firstly, the K‐Means clustering algorithm is used to preprocess the data to divide the distribution of charging current into four areas. Then, aiming to improve the training speed, GANs with gradient penalty (GP) is used for the generation of EV scenarios, which can use the GP term to optimize the Lipschitz limit. Finally, statistical methods are applied to estimate the quality of the generated data. Results show that the proposed method can effectively extend the historical data for the operation and planning of EVs in the future compared with the traditional GANs. Abstract : We proposed a data‐driven approach to generate scenarios using GANs, which can learn the distribution of charging current of electric vehicle (EV) and obtain more abundant scenarios. The proposed method can effectively extend the historical data for the operation and planning of EV in the future compared with the traditional GANs.