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Optimised Two-Layer Configuration of SESS-CCHP System Considering Wind and Light Output Correlation and Load Sensitivity

Author

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  • Mingxi Cai

    (School of Electrical Engineering, Nanhua University, Hengyang 421001, China)

  • Tiejun Zeng

    (School of Electrical Engineering, Nanhua University, Hengyang 421001, China)

  • Linjun Zeng

    (School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410000, China)

  • Xinying Zhou

    (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410000, China)

  • Xin Huang

    (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410000, China)

Abstract

With the gradual depletion of fossil energy sources and the diversification of users’ energy demand, combined cooling, heating and power (CCHP) microgrids have become a hot technology to improve energy efficiency and promote efficient and synergistic energy operation. However, the uncertainty and correlation of wind power and photovoltaic (PV) outputs have posed a great challenge to the reliability of CCHP system operation, so CCHP systems are often equipped with energy storage devices to improve system flexibility to ensure the reliability of energy supply. However, system-owned reserves still have shortcomings such as high investment O&M costs and large space requirements. As an emerging model, “shared energy storage” can reduce the investment pressure of users and open up new ways for the economic and stable operation of CCHP systems. Therefore, based on the scenario of wind and solar power correlation and considering different types of load flexibility, this paper proposes to construct a shared energy storage station (SESS)-CCHP double-layer synergistic optimal allocation model. The model incorporates the consideration of the actual operation strategy of the CCHP system in the planning stage of energy storage. An example analysis shows that SESS reduces the total operating cost of the CCHP system by 25.96% and improves the new energy consumption rate by 10.46% compared with no energy storage. Compared with the system independently configured with energy storage, the cost saving is 2.14%, thus validating the effectiveness of the proposed model.

Suggested Citation

  • Mingxi Cai & Tiejun Zeng & Linjun Zeng & Xinying Zhou & Xin Huang, 2024. "Optimised Two-Layer Configuration of SESS-CCHP System Considering Wind and Light Output Correlation and Load Sensitivity," Energies, MDPI, vol. 17(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4638-:d:1479625
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    References listed on IDEAS

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    1. Hakimi, Seyed Mehdi & Hasankhani, Arezoo & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market," Applied Energy, Elsevier, vol. 298(C).
    2. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    4. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
    5. Ye, Jianan & Xie, Min & Zhang, Shiping & Huang, Ying & Liu, Mingbo & Wang, Qiong, 2023. "Stochastic optimal scheduling of electricity–hydrogen enriched compressed natural gas urban integrated energy system," Renewable Energy, Elsevier, vol. 211(C), pages 1024-1044.
    6. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    7. Liu, Jingkun & Zhang, Ning & Kang, Chongqing & Kirschen, Daniel & Xia, Qing, 2017. "Cloud energy storage for residential and small commercial consumers: A business case study," Applied Energy, Elsevier, vol. 188(C), pages 226-236.
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