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An Integrative DR Study for Optimal Home Energy Management Based on Approximate Dynamic Programming

Author

Listed:
  • Hepeng Li

    (Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    The author contributed equally to this work.)

  • Peng Zeng

    (Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    The author contributed equally to this work.)

  • Chuanzhi Zang

    (Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    The author contributed equally to this work.)

  • Haibin Yu

    (Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    The author contributed equally to this work.)

  • Shuhui Li

    (Department of Electrical & Computer Engineering, the University of Alabama, Tuscaloosa, AL 35487, USA
    The author contributed equally to this work.)

Abstract

This paper presents an integrative demand response (DR) mechanism for energy management of appliances, an energy storage system and an electric vehicle (EV) within a home. The paper considers vehicle-to-home (V2H) and vehicle-to-grid (V2G) functions for energy management of EVs and the degradation cost of the EV battery caused by the V2H/V2G operation in developing the proposed DR mechanism. An efficient optimization algorithm is developed based on approximate dynamic programming, which overcomes the challenges of solving high dimensional optimization problems for the integrative home energy system. To investigate how the participation of different home appliances affects the DR efficiency, several DR scenarios are designed. Then, a detailed simulation study is conducted to investigate and compare home energy management efficiency under different scenarios.

Suggested Citation

  • Hepeng Li & Peng Zeng & Chuanzhi Zang & Haibin Yu & Shuhui Li, 2017. "An Integrative DR Study for Optimal Home Energy Management Based on Approximate Dynamic Programming," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1248-:d:104962
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    References listed on IDEAS

    as
    1. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    2. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
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    Cited by:

    1. Siiri Söyrinki & Eva Heiskanen & Kaisa Matschoss, 2018. "Piloting Demand Response in Retailing: Lessons Learned in Real-Life Context," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    2. Mohammed Qais & K. H. Loo & Hany M. Hasanien & Saad Alghuwainem, 2023. "Optimal Comfortable Load Schedule for Home Energy Management Including Photovoltaic and Battery Systems," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    3. Omaji Samuel & Sakeena Javaid & Nadeem Javaid & Syed Hassan Ahmed & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(11), pages 1-27, November.
    4. Doğukan Aycı & Ferhat Öğüt & Ulaş Özen & Bora Batuhan İşgör & Sinan Küfeoğlu, 2021. "Energy Optimisation Models for Self-Sufficiency of a Typical Turkish Residential Electricity Customer of the Future," Energies, MDPI, vol. 14(19), pages 1-24, September.

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