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Leveraging heat accumulation of district heating network to improve performances of integrated energy system under source-load uncertainties

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  • Wang, Jiangjiang
  • Huo, Shuojie
  • Yan, Rujing
  • Cui, Zhiheng

Abstract

The multiple uncertainties in renewable energy and loads and the thermoelectric coupling characteristic of the integrated energy system (IES) restrict the accommodation of renewable energy. The IES contains massive pipelines in its district heating network, which signifies the heat storage potential. This paper incorporates the dynamic performance of the district heating network into the multi-scenario optimization model to improve IES's operational performance. Herein, the graph theory and Kirchhoff law are employed to construct the dynamic model of district heating network from the single pipeline and network viewpoints, which characterizes the thermal accumulation performance. The stochastic scenarios are generated by combining Latin hypercube sampling for the initial scenarios and scenario curtailment 0–1 algorithm based on Wasserstein probability distance for the curtailment scenarios to capture the uncertainties. Then, a stochastic multi-scenario optimization method is proposed, which is implemented into a case study to analyze the influences of critical parameters and the performance improvement resulted from the network thermal accumulation. The results show that the scenario curtailment 0–1 algorithm can obtain stable and repeatable scenarios. Considering the heat accumulation characteristics of the district heating network can improve the economic performance by 2.41% and wind energy accommodation by 5.51%.

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  • Wang, Jiangjiang & Huo, Shuojie & Yan, Rujing & Cui, Zhiheng, 2022. "Leveraging heat accumulation of district heating network to improve performances of integrated energy system under source-load uncertainties," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222009057
    DOI: 10.1016/j.energy.2022.124002
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    7. Bogdanovics, Raimonds & Zemitis, Jurgis & Zajacs, Aleksandrs & Borodinecs, Anatolijs, 2024. "Small-scale district heating system as heat storage for decentralized solar thermal collectors during non-heating period," Energy, Elsevier, vol. 298(C).
    8. Aidong Zeng & Jiawei Wang & Yaheng Wan, 2023. "Coordinated Optimal Dispatch of Electricity and Heat Integrated Energy Systems Based on Fictitious Node Method," Energies, MDPI, vol. 16(18), pages 1-24, September.
    9. Morales Sandoval, Daniel A. & Saikia, Pranaynil & De la Cruz-Loredo, Ivan & Zhou, Yue & Ugalde-Loo, Carlos E. & Bastida, Héctor & Abeysekera, Muditha, 2023. "A framework for the assessment of optimal and cost-effective energy decarbonisation pathways of a UK-based healthcare facility11The short version of the paper was presented at ICAE2022, Bochum, German," Applied Energy, Elsevier, vol. 352(C).
    10. Li, Yuxuan & Zhang, Junli & Wu, Xiao & Shen, Jiong & Maréchal, François, 2023. "Stochastic-robust planning optimization method based on tracking-economy extreme scenario tradeoff for CCHP multi-energy system," Energy, Elsevier, vol. 283(C).
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