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Deep integration planning of sustainable energies in district energy system and distributed energy station

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  • Li, Haoran
  • Zhang, Chenghui
  • Sun, Bo

Abstract

Distributed energy station receives multiple energy inputs to satisfy multiple energy demands of customers in district energy system. Their planning and design faces with challenges of high dimension, multivariate, and nonlinearity in comparing and selecting multiple components, connecting and coupling multiple energy flows, and evaluating whole life performance. This work analysis the characteristics of whole life operation of district energy system, and reveals the space-time helix and component turntable from its multivariate, high dimension, and nonlinearity. Deep integrated method is proposed to optimize all variables in interactive models to searching the optimum from all potential possibilities on system scale, component type, and operation mode. Free connection model, serial structure model, and modular operation model interact each other. The selfish optimum of gradual methods is eliminated and the nested algorithms of iterative methods is avoided. Through case study on a district energy system with three commercial buildings, compared with other integrated method, this method improves about 8.2%, 7.9%, and 9.5% benefits on economic, energy, and environmental sectors. The structuration and operation of distributed energy station designed by this method are more flexible and reliable. Compatibility and expansibility of models are presented. Also, the computation time of this deep integrated method is acceptable for engineering applications.

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  • Li, Haoran & Zhang, Chenghui & Sun, Bo, 2022. "Deep integration planning of sustainable energies in district energy system and distributed energy station," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    • Handle: RePEc:eee:rensus:v:154:y:2022:i:c:s136403212101159x
    DOI: 10.1016/j.rser.2021.111892
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    2. Zhou, Yuan & Wang, Jiangjiang & Yang, Mingxu & Xu, Hangwei, 2023. "Hybrid active and passive strategies for chance-constrained bilevel scheduling of community multi-energy system considering demand-side management and consumer psychology," Applied Energy, Elsevier, vol. 349(C).

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