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Operation management of residential energy-supplying networks based on optimization approaches

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  • Wakui, Tetsuya
  • Kawayoshi, Hiroki
  • Yokoyama, Ryohei
  • Aki, Hirohisa

Abstract

An operation management system for residential energy-supplying networks using multiple cogeneration units was developed by hierarchically integrating energy demand prediction, operational planning, and operational control, using optimization approaches. The energy demand for multiple dwellings was predicted by support vector regression with information on occupant behavior as well as forecasted weather and energy demand history. Mixed-integer linear programming was employed for the operational planning of the cogeneration units to the predicted energy demand. The energy demand prediction and operational planning were updated using a variable frequency receding horizon approach. This was done to limit the unnecessary shutdown and start-up of the cogeneration units and to reduce the influences of prediction errors for energy demand. Regarding the operational control, the actual on–off schedule of the cogeneration units conformed to the operational planning result. Additionally, the power and heat outputs of the cogeneration units and the heat supply from the storage tanks were modulated in response to the actual energy demand, based on predefined rules. The developed operation management system was applied to annual operation simulation of a residential energy-supplying network consisting of four cogeneration units using fuel cells in a housing complex. For comparative analysis, history-based approaches for energy demand prediction and separate operation of each cogeneration unit were also considered. The results revealed the effectiveness of the developed operation management system as well as the high energy-saving performance of the residential energy-supplying network.

Suggested Citation

  • Wakui, Tetsuya & Kawayoshi, Hiroki & Yokoyama, Ryohei & Aki, Hirohisa, 2016. "Operation management of residential energy-supplying networks based on optimization approaches," Applied Energy, Elsevier, vol. 183(C), pages 340-357.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:340-357
    DOI: 10.1016/j.apenergy.2016.08.171
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    References listed on IDEAS

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    2. Giarola, Sara & Forte, Ornella & Lanzini, Andrea & Gandiglio, Marta & Santarelli, Massimo & Hawkes, Adam, 2018. "Techno-economic assessment of biogas-fed solid oxide fuel cell combined heat and power system at industrial scale," Applied Energy, Elsevier, vol. 211(C), pages 689-704.
    3. Ramadhani, F. & Hussain, M.A. & Mokhlis, H. & Hajimolana, S., 2017. "Optimization strategies for Solid Oxide Fuel Cell (SOFC) application: A literature survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 460-484.
    4. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.
    5. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    6. Aki, Hirohisa & Wakui, Tetsuya & Yokoyama, Ryohei & Sawada, Kento, 2018. "Optimal management of multiple heat sources in a residential area by an energy management system," Energy, Elsevier, vol. 153(C), pages 1048-1060.
    7. Sasaki, Kento & Aki, Hirohisa & Ikegami, Takashi, 2022. "Application of model predictive control to grid flexibility provision by distributed energy resources in residential dwellings under uncertainty," Energy, Elsevier, vol. 239(PB).
    8. Gianluca Valenti & Aldo Bischi & Stefano Campanari & Paolo Silva & Antonino Ravidà & Ennio Macchi, 2021. "Experimental and Numerical Study of a Microcogeneration Stirling Unit under On–Off Cycling Operation," Energies, MDPI, vol. 14(4), pages 1-14, February.
    9. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    10. Zhang, Guoqing & Wang, Jiangjiang & Ren, Fukang & Liu, Yi & Dong, Fuxiang, 2021. "Collaborative optimization for multiple energy stations in distributed energy network based on electricity and heat interchanges," Energy, Elsevier, vol. 222(C).

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