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Commercial building integrated energy system: sizing and energy-economic assessment

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  • Sadegh Nikbakht Naserabad
  • Moslem Akbari Vakilabadi
  • Mohammad Hossein Ahmadi

Abstract

Integrated energy systems are one of the potential options for buildings that can reduce emission. In this research study, the energetic and economic performance of a micro-gas turbine combined heating and cooling plant coupled with a solar PV is analyzed for an office building in Iran. For each analysis, two different scenarios have been performed. System sizing parameters defined in a way that renewable to fossil fuel share is correlated to plant performance and economy. To model the studied system, a time-dependent method is used, which is the inherent characteristic of renewable energies. The renewable energies used here are solar heaters and solar panels. Contours of Net Present Value (NPV) are evaluated as a function of solar heating share and different economic parameters. In addition, optimal system sizing for a typical building is obtained and the results are provided. Effect of various major parameters shows that under the current condition and despite the supportive incentive for renewable energies, strategies and plans even without solar energy are not economically viable due to the high discount rates. In addition, results provide that, in reasonable and normal discount rate, fuel and grid electricity prices, governmental subsidization for conventional combined heat, and power (CHP) andcombined cooling, heat, and power(CCHP) is not necessary, and only in this condition solar electricity selling price (i.e. governmental support program) is effective to increase renewable penetration. The results show that if the interest rate is less than 5%, the NPV becomes positive. Also, when the electricity price reaches $0.07/kWh or higher, the NPV becomes positive.

Suggested Citation

  • Sadegh Nikbakht Naserabad & Moslem Akbari Vakilabadi & Mohammad Hossein Ahmadi, 2023. "Commercial building integrated energy system: sizing and energy-economic assessment," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 714-726.
    • Handle: RePEc:oup:ijlctc:v:18:y:2023:i::p:714-726.
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    Cited by:

    1. Cheng, Ziwei & Yao, Zhen, 2024. "A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building," Energy, Elsevier, vol. 312(C).

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