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Carbon-aware day-ahead optimal dispatch for integrated power grid thermal systems with aggregated distributed resources

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  • Gou, Tong
  • Xu, Yinliang
  • Sun, Hongbin

Abstract

The accommodation of large-scale renewable energy and distributed resources with uncertainty and variability imposes higher flexibility requirements in integrated energy systems. This article proposes a low-carbon day-ahead optimal scheduling model for the integrated power grid thermal systems. First, the network topology and safety operation constraints of the integrated power grid thermal system are considered to ensure the economical and stable operation of the system. Second, a polyhedral based thermally controllable residential load aggregation/ disaggregation method is proposed to obtain the approximate feasible region and equivalent cost parameters of the aggregator, and the uncertainty of the parameters is modeled through distributed robust chance constraints. Third, on the basis of the theory of carbon emission flow, the carbon potential of the prescheduled power grid thermal system is analyzed to guide the development of resource scheduling strategies. Method studies with different scales of integrated power grid thermal systems were conducted, and the results showed that the proposed model can reduce carbon emissions by 8.67 % and 10.71 %, respectively, while ensuring economic benefits and safety.

Suggested Citation

  • Gou, Tong & Xu, Yinliang & Sun, Hongbin, 2025. "Carbon-aware day-ahead optimal dispatch for integrated power grid thermal systems with aggregated distributed resources," Applied Energy, Elsevier, vol. 389(C).
    • Handle: RePEc:eee:appene:v:389:y:2025:i:c:s0306261925004453
    DOI: 10.1016/j.apenergy.2025.125715
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    References listed on IDEAS

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    1. Zhai, Junyi & Wang, Sheng & Guo, Lei & Jiang, Yuning & Kang, Zhongjian & Jones, Colin N., 2022. "Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid," Applied Energy, Elsevier, vol. 326(C).
    2. Li, Zhanhe & Li, Xiaoqian & Lu, Chao & Ma, Kechun & Bao, Weihan, 2024. "Carbon emission responsibility accounting in renewable energy-integrated DC traction power systems," Applied Energy, Elsevier, vol. 355(C).
    3. Yong, Qingqing & Jin, Kaiyuan & Li, Xiaobo & Yang, Ronggui, 2023. "Thermo-economic analysis for a novel grid-scale pumped thermal electricity storage system coupled with a coal-fired power plant," Energy, Elsevier, vol. 280(C).
    4. Chen, Haoyu & Huang, Hai & Zheng, Yong & Yang, Bing, 2024. "A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model," Applied Energy, Elsevier, vol. 375(C).
    5. Ding, Zhetong & Li, Yaping & Zhang, Kaifeng & Peng, Jimmy Chih-Hsien, 2024. "Two-stage dynamic aggregation involving flexible resource composition and coordination based on submodular optimization," Applied Energy, Elsevier, vol. 360(C).
    6. Sun, Xiaocong & Bao, Minglei & Ding, Yi & Hui, Hengyu & Song, Yonghua & Zheng, Chenghang & Gao, Xiang, 2024. "Modeling and evaluation of probabilistic carbon emission flow for power systems considering load and renewable energy uncertainties," Energy, Elsevier, vol. 296(C).
    7. Shi, Zhengkun & Yang, Yongbiao & Xu, Qingshan & Wu, Chenyu & Hua, Kui, 2023. "A low-carbon economic dispatch for integrated energy systems with CCUS considering multi-time-scale allocation of carbon allowance," Applied Energy, Elsevier, vol. 351(C).
    8. Yang, Weijia & Huang, Yuping & Zhang, Tianren & Zhao, Daiqing, 2023. "Mechanism and analytical methods for carbon emission-exergy flow distribution in heat-electricity integrated energy system," Applied Energy, Elsevier, vol. 352(C).
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