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Development of integrated demand and supply side management strategy of multi-energy system for residential building application

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  • Luo, X.J.
  • Fong, K.F.

Abstract

The multi-energy system that contains the highly coupled energy supply equipment units can be adopted to simultaneously satisfy the cooling, heating and electrical energy demands. Owing to the complex nature of multiple supplies and demands, an integrated demand and supply side management strategy was proposed for aligning the system operation and building energy demands. The proposed strategy included three core algorithms: demand side rolling optimization, supply side rolling optimization and feedback correction. Demand and supply side rolling optimizations were simultaneously implemented to determine the schedulable appliances and operating capacities of energy supply equipment units over the next 24-h planning horizon. Feedback correction was involved for continual modification on any discrepancy between various actual and predicted energy demands. In this study, a trigeneration system primed with solid oxide fuel cell-gas turbine was designed as the multi-energy system to serve a high-rise apartment building with electric cars. It was found that the primary energy consumption, the design capacity of the prime mover and the capacity of electricity storage would be decreased by 8.75%, 10% and 43% respectively through the integrated demand and supply side management strategy. It was also noted that electric cars played important roles in effective demand side management.

Suggested Citation

  • Luo, X.J. & Fong, K.F., 2019. "Development of integrated demand and supply side management strategy of multi-energy system for residential building application," Applied Energy, Elsevier, vol. 242(C), pages 570-587.
  • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:570-587
    DOI: 10.1016/j.apenergy.2019.03.149
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    5. Mingshan Mo & Xinrui Xiong & Yunlong Wu & Zuyao Yu, 2023. "Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties," Energies, MDPI, vol. 16(22), pages 1-18, November.
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    7. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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