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

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...

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Bibliographic Details
Published inSustainability Vol. 15; no. 21; p. 15248
Main Authors Sun, Chenhao, Jiang, Xiwei, Jia, Zhiwei, Yu, Kun, Xiang, Sheng, Su, Jianhong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2023
Subjects
Adaptation
Analysis
Case studies
Chromosomes
Efficiency
Electricity distribution
Energy consumption
Energy industry
Energy resources
Gas turbines
Linear programming
Mathematical programming
Natural gas
Neural networks
Noise control
Optimization
Radiation
Supply & demand
Support vector machines
Sustainability
Wind power
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su152115248