• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2021, Vol. 42 ›› Issue (12): 1-8.doi: 10.12204/j.issn.1000-7229.2021.12.001

• Integrated Multiple Energy and Information Technologies in Enabling Planning and Operation of Energy Internet ·Hosted by Associate Professor LIU Yang and Dr. HAN Fujia· • Previous Articles     Next Articles

Load Scenario Generation of Integrated Energy System Using Generative Adversarial Networks

ZHU Qing1(), ZHENG Hongjuan1(), TANG Ziyi2(), WEI Siya1(), ZOU Zixiao3(), WU Xi3()   

  1. 1. NARI Technology Co., Ltd., Nanjing 211106, China
    2. Hangzhou Yuhang District Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311100, China
    3. School of Electrical Engineering, Southeast University, Nanjing 210096, China
  • Received:2021-06-08 Online:2021-12-01 Published:2021-11-26
  • Contact: WU Xi E-mail:dreamathstat@163.com;451173096@qq.com;25804126@qq.com;weisiya_bess@163.com;961498170@qq.com;wuxi@seu.edu.cn
  • Supported by:
    State Grid Corporation of China Research Program(5600-201955167A-0-0-00)

Abstract:

Load scenario generation is the basis of studying energy measurement, operation scheduling and other fields, which is of great significance. Due to difficulty of data collection and multi-energy coupling of integrated energy system, it is still a big challenge to generate load data with diversity. A novel multi-load scenario generation method based on generative adversarial network (GAN) is proposed in this paper. Firstly, the Wasserstein generative adversarial network model with gradient penalty optimization is established to overcome the misconvergence and mode collapse caused by high randomness of load. Secondly, on the basis of the recurrent neural network with deep long-term and short-term memory, the generator and discriminator in the GAN are constructed to be more suitable for load data generation of complex integrated energy system. The result shows that the scenarios generated by proposed model achieves better results in probability distribution, curve signature features and correlation in cooling, heating and power load than original GAN and Monte Carlo method. The model can generate realistic load scenarios with diversity in different modes.

Key words: integrated energy system, scenario generation, deep learning, generative adversarial network (GAN), long short-term memory(LSTM)

CLC Number: