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Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies

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  • Heidari, A.
  • Mortazavi, S.S.
  • Bansal, R.C.

Abstract

The energy hub as a new concept has attracted much attention in modern power systems. One of the aspects of an energy hub deals with its optimal operation. Energy hub scheduling for a day-ahead time horizon including demand response program, different kinds of energy storage, and renewable energy resources, are focused on this current study. In particular, the effects of ice storage, as a novel and developing storage device and yet researchable subject, on the performance and efficiency of the energy hub operation cost are investigated. The stochastic behavior of ice storage is also considered to be compared with deterministic conditions. The studied energy hub is composed of energy converters, including combined cooling, heating, and power (CCHP), to deliver energy to its electrical, heating, and cooling loads. It uses clean, green and, renewable energies as wind turbines and solar panels. The method applied is that the studied energy hub minimizes its operation costs while satisfying demand response constraints in an uncertain environment. The proposed methodology has been evaluated in comparative case studies, and the obtained results show the requirement of including uncertain mode of ice storage in the energy hub.

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  • Heidari, A. & Mortazavi, S.S. & Bansal, R.C., 2020. "Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies," Applied Energy, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s030626191932080x
    DOI: 10.1016/j.apenergy.2019.114393
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    15. Jordehi, A. Rezaee & Javadi, Mohammad Sadegh & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Information gap decision theory (IGDT)-based robust scheduling of combined cooling, heat and power energy hubs," Energy, Elsevier, vol. 231(C).
    16. Zhang, Lizhi & Kuang, Jiyuan & Sun, Bo & Li, Fan & Zhang, Chenghui, 2020. "A two-stage operation optimization method of integrated energy systems with demand response and energy storage," Energy, Elsevier, vol. 208(C).
    17. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    18. Fan, Guangyao & Liu, Zhijian & Liu, Xuan & Shi, Yaxin & Wu, Di & Guo, Jiacheng & Zhang, Shicong & Yang, Xinyan & Zhang, Yulong, 2022. "Two-layer collaborative optimization for a renewable energy system combining electricity storage, hydrogen storage, and heat storage," Energy, Elsevier, vol. 259(C).
    19. Guo, Jiacheng & Liu, Zhijian & Wu, Xuan & Wu, Di & Zhang, Shicong & Yang, Xinyan & Ge, Hua & Zhang, Peiwen, 2022. "Two-layer co-optimization method for a distributed energy system combining multiple energy storages," Applied Energy, Elsevier, vol. 322(C).
    20. Khashayar Hamedi & Shahrbanoo Sadeghi & Saeed Esfandi & Mahdi Azimian & Hessam Golmohamadi, 2021. "Eco-Emission Analysis of Multi-Carrier Microgrid Integrated with Compressed Air and Power-to-Gas Energy Storage Technologies," Sustainability, MDPI, vol. 13(9), pages 1-18, April.
    21. Xiong, Kang & Hu, Weihao & Cao, Di & Li, Sichen & Zhang, Guozhou & Liu, Wen & Huang, Qi & Chen, Zhe, 2023. "Coordinated energy management strategy for multi-energy hub with thermo-electrochemical effect based power-to-ammonia: A multi-agent deep reinforcement learning enabled approach," Renewable Energy, Elsevier, vol. 214(C), pages 216-232.
    22. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
    23. Lu, Xinhui & Li, Haobin & Zhou, Kaile & Yang, Shanlin, 2023. "Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response," Energy, Elsevier, vol. 262(PB).

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