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Multi-objective planning of regional integrated energy system aiming at exergy efficiency and economy

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  • Wang, Yongli
  • Huang, Feifei
  • Tao, Siyi
  • Ma, Yang
  • Ma, Yuze
  • Liu, Lin
  • Dong, Fugui

Abstract

This paper introduces the exergy efficiency that takes into account both the quantity and quality of energy, and constructs a bi-level planning optimization model of the regional integrated energy system. Factors such as equipment exergy efficiency are considered by the upper-level planning model. Through matter-element information theory and Frequent Patterm-growth algorithm, the energy structure of the regional integrated energy system planning is determined by quantitative means. The lower-level planning model is intended for economy and exergy efficiency, and the tabu search algorithm is embedded in the solving algorithm of the multi-objective genetic algorithm for solving, determining the capacity of each equipment in the energy structure. In the case study, three comparison schemes are used to verify the proposed model and method through the regional integrated energy system in a resort town in northern China. Among them, Scheme 1 is devised for economy, Scheme 2 is proposed for economy and energy efficiency, and Scheme 3 is intended for economy and exergy efficiency. The results show that compared with the Scheme 1 and Scheme 2, the system exergy efficiency of Scheme 3 corresponding to this model has increased by 17.34% and 11.17%, respectively. Despite an increase of 10.61% in total annualized cost in Scheme 3 compared to Scheme 1, there is a decrease of 6.37% compared to Scheme 2. Despite the conflict in improving exergy efficiency and economy, there is still a balance to draw in the proposed model in this paper.

Suggested Citation

  • Wang, Yongli & Huang, Feifei & Tao, Siyi & Ma, Yang & Ma, Yuze & Liu, Lin & Dong, Fugui, 2022. "Multi-objective planning of regional integrated energy system aiming at exergy efficiency and economy," Applied Energy, Elsevier, vol. 306(PB).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pb:s0306261921013994
    DOI: 10.1016/j.apenergy.2021.118120
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    Cited by:

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    2. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    3. Li Peng & Hongjun Wu & Wenlong Cao & Qianjun Mao, 2023. "Exergy Analysis of a Shell and Tube Energy Storage Unit with Different Inclination Angles," Energies, MDPI, vol. 16(11), pages 1-17, May.
    4. Li, Jiaxi & Wang, Dan & Jia, Hongjie & Lei, Yang & Zhou, Tianshuo & Guo, Ying, 2022. "Mechanism analysis and unified calculation model of exergy flow distribution in regional integrated energy system," Applied Energy, Elsevier, vol. 324(C).
    5. Liu, Lintong & Zhai, Rongrong & Hu, Yangdi, 2023. "Multi-objective optimization with advanced exergy analysis of a wind-solar‑hydrogen multi-energy supply system," Applied Energy, Elsevier, vol. 348(C).
    6. Lijun Tang & Xiaolong Gou & Junyu Liang & Yang Yang & Xingyu Yuan & Jiaquan Yang & Yuting Yan & Dada Wang & Yongli Wang & Xin Chen & Bo Yuan & Siyi Tao, 2022. "A Two-Stage Planning Optimization Study of an Integrated Energy System Considering Uncertainty," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    7. Pengfei Duan & Mengdan Feng & Bingxu Zhao & Qingwen Xue & Kang Li & Jinglei Chen, 2024. "Operational Optimization of Regional Integrated Energy Systems with Heat Pumps and Hydrogen Renewable Energy under Integrated Demand Response," Sustainability, MDPI, vol. 16(3), pages 1-18, January.
    8. Guillermo Valencia Ochoa & York Castillo Santiago & Jorge Duarte Forero & Juan B. Restrepo & Alberto Ricardo Albis Arrieta, 2023. "A Comprehensive Comparative Analysis of Energetic and Exergetic Performance of Different Solar-Based Organic Rankine Cycles," Energies, MDPI, vol. 16(6), pages 1-26, March.
    9. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).
    10. Chen, Xianqing & Dong, Wei & Yang, Lingfang & Yang, Qiang, 2023. "Scenario-based robust capacity planning of regional integrated energy systems considering carbon emissions," Renewable Energy, Elsevier, vol. 207(C), pages 359-375.
    11. Liang, Weikun & Lin, Shunjiang & Liu, Mingbo & Sheng, Xuan & Pan, Yue & Liu, Yun, 2023. "Risk assessment for cascading failures in regional integrated energy system considering the pipeline dynamics," Energy, Elsevier, vol. 270(C).

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