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Stochastic energy management in multi-carrier residential energy systems

Author

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  • Kazemdehdashti, A.
  • Mohammadi, M.
  • Seifi, A.R.
  • Rastegar, M.

Abstract

This paper proposes a stochastic framework for residential energy management (REM) in a multi-carrier building. The proposed REM program minimizes the purchase cost of electricity and natural gas of a building, considering the operational constraints of energy dispatch and various building components such as micro-combined heat and power (CHP), heat storage system, heater, gas boiler, plug-in electric vehicles (PEVs), and renewable energy resources (RERs). A part of the electrical and thermal load of the building is assumed to be flexible in time and the amount of energy consumption. Uncertainties of renewable generation, the traveling time of PEVs, energy prices, and electricity/thermal loads are addressed in the program. Since the conventional stochastic methods are time-consuming or rely on asymptotic approximation, the proposed stochastic method in this paper has a low computational burden and a high precision level, which make it a suitable solution for large-scale stochastic programming problems. This method is examined on a test building for the stochastic day-ahead REM problem, and the results are compared with the other conventional methods to demonstrate the advantages of the proposed one.

Suggested Citation

  • Kazemdehdashti, A. & Mohammadi, M. & Seifi, A.R. & Rastegar, M., 2020. "Stochastic energy management in multi-carrier residential energy systems," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220308975
    DOI: 10.1016/j.energy.2020.117790
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    References listed on IDEAS

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    1. Fabrizio, Enrico & Corrado, Vincenzo & Filippi, Marco, 2010. "A model to design and optimize multi-energy systems in buildings at the design concept stage," Renewable Energy, Elsevier, vol. 35(3), pages 644-655.
    2. Hawkes, A.D. & Leach, M.A., 2007. "Cost-effective operating strategy for residential micro-combined heat and power," Energy, Elsevier, vol. 32(5), pages 711-723.
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    Cited by:

    1. Lyu, Xiangmei & Liu, Tianqi & Liu, Xuan & He, Chuan & Nan, Lu & Zeng, Hong, 2023. "Low-carbon robust economic dispatch of park-level integrated energy system considering price-based demand response and vehicle-to-grid," Energy, Elsevier, vol. 263(PB).
    2. Zhang, Heng & Zhang, Shenxi & Hu, Xiao & Cheng, Haozhong & Gu, Qingfa & Du, Mengke, 2022. "Parametric optimization-based peer-to-peer energy trading among commercial buildings considering multiple energy conversion," Applied Energy, Elsevier, vol. 306(PB).
    3. Li, Peng & Wang, Zixuan & Wang, Jiahao & Yang, Weihong & Guo, Tianyu & Yin, Yunxing, 2021. "Two-stage optimal operation of integrated energy system considering multiple uncertainties and integrated demand response," Energy, Elsevier, vol. 225(C).
    4. Mehrjerdi, Hasan & Mahdavi, Sajad & Hemmati, Reza, 2021. "Resilience maximization through mobile battery storage and diesel DG in integrated electrical and heating networks," Energy, Elsevier, vol. 237(C).

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