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Retracted: Distributed buildings energy storage charging load forecasting method considering parallel deep learning model
Author(s) -
Yang Shengying,
Wu Jianfeng,
Qin Huibin,
Xie Qiangqiang,
Xu Zhiwang,
Hua Yongzhu
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5580
Subject(s) - correctness , energy storage , renewable energy , computer science , mode (computer interface) , supercapacitor , grid , electricity , automotive engineering , control (management) , energy consumption , consumption (sociology) , distributed generation , power (physics) , electrical engineering , engineering , capacitance , mathematics , chemistry , physics , geometry , electrode , quantum mechanics , programming language , operating system , social science , artificial intelligence , sociology
Summary At present, with total building energy consumption accounts for about 21.33% of the energy consumption of social terminals, the building energy consumption has a tendency to continue to grow. According to the geographical location of users and the local weather conditions, we analyze the energy resources available to users and design a multienergy complementary coupling system. We have taken full account of the use of renewable energy and recovery of waste heat resources. Large‐scale increase of electrical equipment, a large number of charging load access to the grid, the power system planning, operation and operation of the electricity market will have a profound impact. The current mode of the supercapacitor and the DC bus determines the mode of operation of the converter. Based on a detailed analysis of each working mode, we design a corresponding control scheme and achieve a smooth transition and switching between modes. Simulation and experiment verify the correctness and effectiveness of the converter and hybrid energy storage control strategy.