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A Comparison of Optimal Operation of a Residential Fuel Cell Co-Generation System Using Clustered Demand Patterns Based on Kullback-Leibler Divergence

Author

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  • Akira Yoshida

    (Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, 162-0044, Tokyo, Japan)

  • Yoshiharu Amano

    (Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, 162-0044, Tokyo, Japan)

  • Noboru Murata

    (School of Science and Engineering, Waseda University, 1-4-3 Okubo, Shinjuku-ku, 169-8555, Tokyo, Japan)

  • Koichi Ito

    (Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, 162-0044, Tokyo, Japan)

  • Takumi Hasizume

    (Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, 162-0044, Tokyo, Japan)

Abstract

When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, the authors aim to extract basic time-series demand patterns from two kinds of measured demand (electricity and domestic hot water), and also aim to reveal effective demand patterns for primary energy saving. Time-series demand data are categorized with a hierarchical clustering method using a statistical pseudo-distance, which is represented by the generalized Kullback-Leibler divergence of two Gaussian mixture distributions. The classified demand patterns are built using hierarchical clustering and then a comparison is made between the optimal operation of a polymer electrolyte membrane fuel cell co-generation system and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the appropriately built demand profiles. Our results show that basic demand patterns are extracted by the proposed method, and the heat-to-power ratio of demand, the amount of daily demand, and demand patterns affect the primary energy saving of the co-generation system.

Suggested Citation

  • Akira Yoshida & Yoshiharu Amano & Noboru Murata & Koichi Ito & Takumi Hasizume, 2013. "A Comparison of Optimal Operation of a Residential Fuel Cell Co-Generation System Using Clustered Demand Patterns Based on Kullback-Leibler Divergence," Energies, MDPI, vol. 6(1), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:1:p:374-399:d:22879
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    References listed on IDEAS

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    1. Wakui, Tetsuya & Yokoyama, Ryohei & Shimizu, Ken-ichi, 2010. "Suitable operational strategy for power interchange operation using multiple residential SOFC (solid oxide fuel cell) cogeneration systems," Energy, Elsevier, vol. 35(2), pages 740-750.
    2. Katarina Košmelj & Vladimir Batagelj, 1990. "Cross-sectional approach for clustering time varying data," Journal of Classification, Springer;The Classification Society, vol. 7(1), pages 99-109, March.
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    Cited by:

    1. Wakui, Tetsuya & Yokoyama, Ryohei, 2015. "Impact analysis of sampling time interval and battery installation on optimal operational planning of residential cogeneration systems without electric power export," Energy, Elsevier, vol. 81(C), pages 120-136.
    2. Shunyong Yin & Jianjun Xia & Yi Jiang, 2020. "Characteristics Analysis of the Heat-to-Power Ratio from the Supply and Demand Sides of Cities in Northern China," Energies, MDPI, vol. 13(1), pages 1-14, January.
    3. Long-Yi Chang & Hung-Cheng Chen, 2014. "Linearization and Input-Output Decoupling for Nonlinear Control of Proton Exchange Membrane Fuel Cells," Energies, MDPI, vol. 7(2), pages 1-16, January.

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