Economic, Low-Carbon Dispatch of Seasonal Park Integrated Energy System Based on Adjustable Cooling–Heating–Power Ratio

With the application and continuous development of green energy within the park integrated energy systems (PIESs), environmental pollution and resource depletion caused by traditional energy sources have been effectively mitigated. However, the existing research primarily focuses on fixed operating conditions, leading to significant wastage of renewable energy. To enhance the integration of renewable energy and improve overall energy efficiency, in this paper, a seasonal park integrated energy system (SPIES) based on an adjustable cooling–heating–power ratio (SPIESchpr) strategy is proposed to maximize the energy utilization efficiency and system operational economy. In SPIESchpr, to achieve additional carbon emission reductions, a novel seasonal laddered carbon trading mechanism (SLCTM) is proposed. Compared to traditional carbon trading methods, the SLCTM significantly improves the low-carbon performance of PIES. Finally, the effectiveness of the proposed SPIESchpr is validated through three scenario analyses and a detailed case study of typical daily operations. The experimental results demonstrate that, compared to fixed heat-to-cool ratios and conventional carbon trading mechanisms, the proposed SPIESchpr significantly reduces both total operational costs and carbon emissions during both heating and cooling seasons. Consequently, the proposed SPIESchpr not only enhances the energy efficiency, economic benefits, and carbon reduction potential of PIES but also provides a valuable reference for year-round operational dispatching strategies.

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Publication Details

Journal
Energies
Published
2025-09-24
DOI
https://doi.org/10.3390/en18195071
Citations
1
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
0.73
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article

Economic, Low-Carbon Dispatch of Seasonal Park Integrated Energy System Based on Adjustable Cooling–Heating–Power Ratio

Baihao Qiao, Wei Tao, Jing Ye, Yan Li et al.
1 citations
Energies
Integrated Energy Systems Optimization
0.73
article

Economic, Low-Carbon Dispatch of Seasonal Park Integrated Energy System Based on Adjustable Cooling–Heating–Power Ratio

Baihao Qiao, Wei Tao, Jing Ye, Yan Li, Hui Xu, Hejuan Hu, Yitong Liu
article en
1 citations

Abstract

With the application and continuous development of green energy within the park integrated energy systems (PIESs), environmental pollution and resource depletion caused by traditional energy sources have been effectively mitigated. However, the existing research primarily focuses on fixed operating conditions, leading to significant wastage of renewable energy. To enhance the integration of renewable energy and improve overall energy efficiency, in this paper, a seasonal park integrated energy system (SPIES) based on an adjustable cooling–heating–power ratio (SPIESchpr) strategy is proposed to maximize the energy utilization efficiency and system operational economy. In SPIESchpr, to achieve additional carbon emission reductions, a novel seasonal laddered carbon trading mechanism (SLCTM) is proposed. Compared to traditional carbon trading methods, the SLCTM significantly improves the low-carbon performance of PIES. Finally, the effectiveness of the proposed SPIESchpr is validated through three scenario analyses and a detailed case study of typical daily operations. The experimental results demonstrate that, compared to fixed heat-to-cool ratios and conventional carbon trading mechanisms, the proposed SPIESchpr significantly reduces both total operational costs and carbon emissions during both heating and cooling seasons. Consequently, the proposed SPIESchpr not only enhances the energy efficiency, economic benefits, and carbon reduction potential of PIES but also provides a valuable reference for year-round operational dispatching strategies.

EnergiesVol. 18(19)
Zhongyuan University of Technology (CN), Henan University of Engineering (CN)
Openalex Percentile: Top 23%
Integrated Energy Systems Optimization
0.73
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