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Development of an integrated energy system for smart communities

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  • Abu-Rayash, Azzam
  • Dincer, Ibrahim

Abstract

This paper introduces a newly developed integrated multigeneration energy system designed for a smart community. The system constitutes of various renewable energy sources, including solar and wind farms, and a quintuple geothermal system with reinjection. The system meets the demand and provides the main commodities of a small city with 5000 houses for their electricity, space heating, domestic hot water, and fresh water. The system is analyzed both energetically and exergetically using thermodynamic principles. The overall energy and exergy efficiencies of the proposed system are found to be 81.3% and 84.6% respectively. In addition, the energy and exergy efficiencies of the PVT system are 57.9% and 49.2%, respectively. The total electricity production is evaluated at 430 MW, while the capacity for domestic hot water is 20 MW. District heating is incorporated into this system at a capacity of 50 MW. The energy and exergy efficiencies of the geothermal system are found to be 27.6% and 35.7% respectively. Within the organic Rankine cycle, the exergy destruction at the boiler and the condenser add up to 15.8 MW, which makes up 94% of the total exergy destruction of this cycle. Moreover, a number of parametric studies are conducted to evaluate the level of influence that key parameters have on each system and consequently the overall system performance.

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  • Abu-Rayash, Azzam & Dincer, Ibrahim, 2020. "Development of an integrated energy system for smart communities," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220307908
    DOI: 10.1016/j.energy.2020.117683
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    References listed on IDEAS

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    Cited by:

    1. Zhao Luo & Jinghui Wang & Ni Xiao & Linyan Yang & Weijie Zhao & Jialu Geng & Tao Lu & Mengshun Luo & Chenming Dong, 2022. "Low Carbon Economic Dispatch Optimization of Regional Integrated Energy Systems Considering Heating Network and P2G," Energies, MDPI, vol. 15(15), pages 1-14, July.
    2. Xiao, Tianqi & You, Fengqi, 2024. "Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities," Applied Energy, Elsevier, vol. 353(PB).
    3. Lopez-Ruiz, G. & Alava, I. & Blanco, J.M., 2021. "Study on the feasibility of the micromix combustion principle in low NOx H2 burners for domestic and industrial boilers: A numerical approach," Energy, Elsevier, vol. 236(C).
    4. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).

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