Journal of Terahertz Science and Electronic Information Technology , Volume. 21, Issue 1, 112(2023)

Reinforcement Learning-based Optimizing Dynamic Pricing algorithm in smart grid

CAO Jun*, SUN Yingying, and ZHAO Hang
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    References(8)

    [2] [2] MOHASSEL R R, FUNG A, MOHAMMADI F, et al. A survey on advanced metering infrastructure[J]. International Journal of Electrical Power & Energy Systems, 2014(63):473-484.

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    [8] [8] JIN M, FENG W, MARNAY C, et al. Microgrid to enable optimal distributed energy retail and end-user demand response[J]. Applied Energy, 2018(210):1312-1335.

    [9] [9] LU Renzhi, HONG Seung Ho, ZHANG Xiongfeng. A dynamic pricing demand response algorithm for smart grid: reinforcement learning approach[J]. Applied Energy, 2018(220):220-230.

    [10] [10] REZA B,NASSER M,BABAK B. Optimizing dynamic pricing demand response algorithm using reinforcement learning in smart grid[C]// 2020 25th International Computer Conference. Tehran,Iran:IEEE, 2020:1-5.

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    CAO Jun, SUN Yingying, ZHAO Hang. Reinforcement Learning-based Optimizing Dynamic Pricing algorithm in smart grid[J]. Journal of Terahertz Science and Electronic Information Technology , 2023, 21(1): 112

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    Paper Information

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    Received: Apr. 28, 2020

    Accepted: --

    Published Online: Mar. 14, 2023

    The Author Email: Jun CAO (huxyu_82@sohu.com)

    DOI:10.11805/tkyda2020178

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