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Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

Author

Listed:
  • Yasuaki Miyazato

    (Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)

  • Hayato Tahara

    (Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan
    These authors contributed equally to this work.)

  • Kosuke Uchida

    (Department of Electrical Engineering, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan
    These authors contributed equally to this work.)

  • Cirio Celestino Muarapaz

    (Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan
    These authors contributed equally to this work.)

  • Abdul Motin Howlader

    (Hawaii Natural Energy Institute, University of Hawaii, 1680 East-West Rd, Honolulu, HI 96822, USA
    These authors contributed equally to this work.)

  • Tomonobu Senjyu

    (Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan
    These authors contributed equally to this work.)

Abstract

A smart house generally has a Photovoltaic panel (PV), a Heat Pump (HP), a Solar Collector (SC) and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions.

Suggested Citation

  • Yasuaki Miyazato & Hayato Tahara & Kosuke Uchida & Cirio Celestino Muarapaz & Abdul Motin Howlader & Tomonobu Senjyu, 2016. "Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems," Sustainability, MDPI, vol. 8(12), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1273-:d:84633
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    References listed on IDEAS

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    Cited by:

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    2. Pascal A. Schirmer & Iosif Mporas, 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation," Sustainability, MDPI, vol. 11(11), pages 1-14, June.

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