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A Mixed Artificial Bee Colony Algorithm for the Time-of-Use Pricing Optimization

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10385))

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Abstract

Demand side management (DSM) is proposed to solve the contradiction between supply and demand of electricity market. To avoid the peak load, time-of-use (TOU) pricing strategy plays an important role in DSM to affect the behavior of using electricity by the users. In this paper, we proposed a mixed artificial bee colony (mABC) algorithm to TOU pricing optimization. Different from traditional research which optimizes the time division and electricity price separately, we consider these two factors together and optimize them simultaneously through the proposed mABC. The experimental results on a real-world scenario show the superiority of the mABC over traditional state-of-the-art methods.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (71601028, 71671024, 71421001, 71431002). The source codes are available at http://faculty.dlut.edu.cn/li/en/article/960204/list/.

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Authors and Affiliations

  1. Faculty of Management and Economics, Dalian University of Technology, Dalian, China

    Huiyan Yang, Xianneng Li & Guangfei Yang

Authors
  1. Huiyan Yang
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  2. Xianneng Li
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  3. Guangfei Yang
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Corresponding author

Correspondence to Xianneng Li .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Yang, H., Li, X., Yang, G. (2017). A Mixed Artificial Bee Colony Algorithm for the Time-of-Use Pricing Optimization. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_36

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  • DOI: https://doi.org/10.1007/978-3-319-61824-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61823-4

  • Online ISBN: 978-3-319-61824-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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