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An Optimization Ensemble for Integrated Energy System Configuration Strategy Incorporating Demand–Supply Coordination

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  • Chenhao Sun

    (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Xiwei Jiang

    (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Zhiwei Jia

    (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Kun Yu

    (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Sheng Xiang

    (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Jianhong Su

    (International College of Engineering, Changsha University of Science and Technology, Changsha 410114, China)

Abstract

As one representative smart energy infrastructure in smart cities, an integrated energy system (IES) consists of several types of energy sources, thus making more complicated coupling connections between the supply and demand sides than a power grid. This will impact when allocating different energy sources to ensure the appropriate energy utilization in the IES. With this motivation, an IES energy configuration optimization strategy based on a multi-model ensemble is proposed in this paper. Firstly, one coupling model is constructed to assess the underlying collaborative relationships between two sides for a renewable-energy-connected IES. Next, the independent component analysis (ICA) method is implemented for noise reduction in massive heterogeneous input databases, which can effectively improve the computing efficiency under such high-dimensional data conditions. Also, the self-adaptive quantum genetic model (SAQGM) is built for subsequent configuration optimization. Specifically, the quantum bit representation is incorporated to reduce computation complexity in multi-states scenarios, the double-chain formation of chromosomes is deployed to diminish the uncertainty when encoding, and the dynamic adaptation quantum gate is established to successively amend parameters. Finally, an empirical case study is conducted which can demonstrate the benefits of this strategy in terms of feasibility, efficiency, and economy.

Suggested Citation

  • Chenhao Sun & Xiwei Jiang & Zhiwei Jia & Kun Yu & Sheng Xiang & Jianhong Su, 2023. "An Optimization Ensemble for Integrated Energy System Configuration Strategy Incorporating Demand–Supply Coordination," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15248-:d:1266783
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    References listed on IDEAS

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