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Data-driven stochastic robust optimization of sustainable utility system

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  • Wang, Qipeng
  • Zhao, Liang

Abstract

Sustainable utility systems reduce reliance on fossil fuels by using renewable energy sources. Multi-scale uncertainties associated with renewable energy and utility systems pose challenges to the modeling and optimization of sustainable utility systems. This study proposes a sustainable retrofit framework for utility systems based on a data-driven stochastic robust optimization approach. Kernel density estimation and fuzzy clustering were employed to capture the uncertainty features in a holistic framework. A life cycle assessment approach was used to calculate the global warming potential (GWP) of the sustainable utility system, and a multi-objective environmental and economic optimization model was developed. A nested decomposition-based algorithm was proposed to solve a large-scale mixed-integer nonlinear programming problem. Finally, a case study of an industrial utility system was conducted to demonstrate the effectiveness of the proposed method. The optimization results show that the proposed method reduces GWP by 9 % after introducing renewable energy and achieves a balance between economic and environmental performance.

Suggested Citation

  • Wang, Qipeng & Zhao, Liang, 2023. "Data-driven stochastic robust optimization of sustainable utility system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:rensus:v:188:y:2023:i:c:s1364032123006986
    DOI: 10.1016/j.rser.2023.113841
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