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A projection neural network for optimal demand response in smart grid environment

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Abstract

This paper presents a smart grid model that fully considers the power consumption types of users and the features of electricity price. A satisfaction function is added into the bill function to balance the user experience of electricity usage and the consumption of load. For the purpose of minimizing the electricity bill of all users, a single-layer projection neural network (PNN) is used, which is proven to be global convergence. And the simulation results reveal that the effectiveness and peculiarities of the proposed PNN.

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Acknowledgments

This work is supported by Fundamental Research Funds for the Central Universities (Grant no. XDJK2016B017, XDJK2016E032), Natural Science Foundation of China (Grant nos: 61403313, 61374078), and also supported by Natural Science Foundation Project of Chongqing CSTC (Grant no. cstc2014jcyjA40014). This publication was made possible by NPRP Grant No. NPRP 7-1482-1-278 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Yao Yao & Xing He

  2. Department of Mathematics, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  3. School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC, 3000, Australia

    Chaojie Li

  4. School of Information Engineering, Guizhou Minzu University, Guiyang, 550025, China

    Dawen Xia

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  1. Yao Yao
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  2. Xing He
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  4. Chaojie Li
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  5. Dawen Xia
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Correspondence to Xing He.

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Yao, Y., He, X., Huang, T. et al. A projection neural network for optimal demand response in smart grid environment. Neural Comput & Applic 29, 259–267 (2018). https://doi.org/10.1007/s00521-016-2532-0

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