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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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    2. 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).
    3. Yang, Weijia & Huang, Yuping & Zhao, Daiqing, 2023. "A coupled hydraulic–thermal dynamic model for the steam network in a heat–electricity integrated energy system," Energy, Elsevier, vol. 263(PC).
    4. Jakubek, Dariusz & Ocłoń, Paweł & Nowak-Ocłoń, Marzena & Sułowicz, Maciej & Varbanov, Petar Sabev & Klemeš, Jiří Jaromír, 2023. "Mathematical modelling and model validation of the heat losses in district heating networks," Energy, Elsevier, vol. 267(C).
    5. Hiris, Daniel P. & Pop, Octavian G. & Dobrovicescu, Alexandru & Dudescu, Mircea C. & Balan, Mugur C., 2023. "Modelling of solar assisted district heating system with seasonal storage tank by two mathematical methods and with two climatic data as input," Energy, Elsevier, vol. 284(C).
    6. Huang, Shangjiu & Lu, Hao & Chen, Maozhi & Zhao, Wenjun, 2023. "Integrated energy system scheduling considering the correlation of uncertainties," Energy, Elsevier, vol. 283(C).
    7. 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).
    8. Jiawei Wang & Aidong Zeng & Yaheng Wan, 2023. "Multi-Time-Scale Optimal Scheduling of Integrated Energy System Considering Transmission Delay and Heat Storage of Heating Network," Sustainability, MDPI, vol. 15(19), pages 1-26, September.

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