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An internal trading strategy for optimal energy management of combined cooling, heat and power in building microgrids

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  • Bui, Van-Hai
  • Hussain, Akhtar
  • Im, Yong-Hoon
  • Kim, Hak-Man

Abstract

This paper proposes a three-step internal trading strategy for optimal energy sharing among building microgrids (MGs) to minimize the operation cost of the entire system. In the first step, barter trading is performed to exchange energies among building MGs of the network. Building MGs having shortage of electricity and surplus of heat can pair with buildings having surplus of electricity and shortage of heat to exchange their energies. Then, the surplus/shortage energy is updated after carrying out the barter trading. In the second step, building MGs sell their remaining surplus energy to other building MGs that have shortage energy. In the third step, building MGs, which have lower generation cost than the external system and ability to increase their power generation, increase their power generation and trade with other building MGs. These three steps can increase the power sharing among building MGs and reduce the trading with the external systems, which results in reduced operation cost of the network. An additional internal market along with price signals is also presented to carry out the proposed internal trading strategy. The internal market provides a mechanism for trading energies among the building MGs of a combined cooling, heat and power (CCHP) system. A hierarchical energy management system (EMS), including building EMSs (BEMSs) and a community EMS (C-EMS) is developed to minimize the operation cost of the CCHP system. Moreover, a heat pipeline (HPL) system is applied for storing the surplus heat energy for fulfilling the shortage heat energy in the building MGs. By using the proposed strategy, total trading cost of CCHP system is reduced by 7.43% compared with the conventional operation strategy.

Suggested Citation

  • Bui, Van-Hai & Hussain, Akhtar & Im, Yong-Hoon & Kim, Hak-Man, 2019. "An internal trading strategy for optimal energy management of combined cooling, heat and power in building microgrids," Applied Energy, Elsevier, vol. 239(C), pages 536-548.
    • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:536-548
    DOI: 10.1016/j.apenergy.2019.01.160
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    References listed on IDEAS

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    5. Jing, Rui & Wang, Meng & Zhang, Zhihui & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2019. "Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    7. Maria Symeonidou & Agis M. Papadopoulos, 2022. "Selection and Dimensioning of Energy Storage Systems for Standalone Communities: A Review," Energies, MDPI, vol. 15(22), pages 1-28, November.
    8. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2022. "A review on the integration and optimization of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    9. Millar, Michael-Allan & Yu, Zhibin & Burnside, Neil & Jones, Greg & Elrick, Bruce, 2021. "Identification of key performance indicators and complimentary load profiles for 5th generation district energy networks," Applied Energy, Elsevier, vol. 291(C).
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    11. Angizeh, Farhad & Ghofrani, Ali & Zaidan, Esmat & Jafari, Mohsen A., 2022. "Adaptable scheduling of smart building communities with thermal mapping and demand flexibility," Applied Energy, Elsevier, vol. 310(C).

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