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Automated Energy Scheduling Algorithms for Residential Demand Response Systems

Author

Listed:
  • Laihyuk Park

    (School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, Korea)

  • Yongwoon Jang

    (School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, Korea)

  • Hyoungchel Bae

    (School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, Korea)

  • Juho Lee

    (School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, Korea)

  • Chang Yun Park

    (School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, Korea)

  • Sungrae Cho

    (School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, Korea)

Abstract

Demand response technology is a key technology for distributing electricity tasks in response to electricity prices in a smart grid system. In the current demand response research, there has been much demand for an automated energy scheduling scheme that uses smart devices for residential customers in the smart grid. In this paper, two automated energy scheduling schemes are proposed for residential smart grid demand response systems: semi-automated scheduling and fully-automated scheduling. If it is possible to set the appliance preference, semi-automated scheduling will be conducted, and if it is impossible, fully-automated scheduling will be operated. The formulated optimization problems consider the electricity bill along with the user convenience. For the fully-automated scheduling, the appliance preference can automatically be found according to appliance type from the electricity consumption statistics. A performance evaluation validates that the proposed scheme shifts operation to avoid peak load, that the electricity bill is significantly reduced, and that user convenience is satisfied.

Suggested Citation

  • Laihyuk Park & Yongwoon Jang & Hyoungchel Bae & Juho Lee & Chang Yun Park & Sungrae Cho, 2017. "Automated Energy Scheduling Algorithms for Residential Demand Response Systems," Energies, MDPI, vol. 10(9), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1326-:d:110707
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    References listed on IDEAS

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    1. Rubino, Luigi & Capasso, Clemente & Veneri, Ottorino, 2017. "Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility," Applied Energy, Elsevier, vol. 207(C), pages 438-464.
    2. Zhou, Bowen & Yao, Feng & Littler, Tim & Zhang, Huaguang, 2016. "An electric vehicle dispatch module for demand-side energy participation," Applied Energy, Elsevier, vol. 177(C), pages 464-474.
    3. Bishnu P. Bhattarai & Kurt S. Myers & Birgitte Bak-Jensen & Sumit Paudyal, 2017. "Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks," Energies, MDPI, vol. 10(1), pages 1-18, January.
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    Cited by:

    1. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    2. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
    3. Tahir, Muhammad Faizan & Chen, Haoyong & Khan, Asad & Javed, Muhammad Sufyan & Cheema, Khalid Mehmood & Laraik, Noman Ali, 2020. "Significance of demand response in light of current pilot projects in China and devising a problem solution for future advancements," Technology in Society, Elsevier, vol. 63(C).
    4. Seong-Kyu Kim & Jun-Ho Huh, 2018. "A Study on the Improvement of Smart Grid Security Performance and Blockchain Smart Grid Perspective," Energies, MDPI, vol. 11(8), pages 1-22, July.
    5. Lorenzo Bartolucci & Stefano Cordiner & Vincenzo Mulone & Marina Santarelli, 2019. "Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems," Energies, MDPI, vol. 12(12), pages 1-18, June.

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