IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v261y2020ics030626191932080x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191932080X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.114393?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jadidbonab, Mohammad & Babaei, Ebrahim & Mohammadi-ivatloo, Behnam, 2019. "CVaR-constrained scheduling strategy for smart multi carrier energy hub considering demand response and compressed air energy storage," Energy, Elsevier, vol. 174(C), pages 1238-1250.
    2. Kanwar, Neeraj & Gupta, Nikhil & Niazi, K.R. & Swarnkar, Anil & Bansal, R.C., 2017. "Simultaneous allocation of distributed energy resource using improved particle swarm optimization," Applied Energy, Elsevier, vol. 185(P2), pages 1684-1693.
    3. Hemmati, S. & Ghaderi, S.F. & Ghazizadeh, M.S., 2018. "Sustainable energy hub design under uncertainty using Benders decomposition method," Energy, Elsevier, vol. 143(C), pages 1029-1047.
    4. Lee, Wen-Shing & Chen, Yi -Ting & Wu, Ting-Hau, 2009. "Optimization for ice-storage air-conditioning system using particle swarm algorithm," Applied Energy, Elsevier, vol. 86(9), pages 1589-1595, September.
    5. Rakipour, Davood & Barati, Hassan, 2019. "Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response," Energy, Elsevier, vol. 173(C), pages 384-399.
    6. Davatgaran, Vahid & Saniei, Mohsen & Mortazavi, Seyed Saeidollah, 2018. "Optimal bidding strategy for an energy hub in energy market," Energy, Elsevier, vol. 148(C), pages 482-493.
    7. Davatgaran, Vahid & Saniei, Mohsen & Mortazavi, Seyed Saeidollah, 2019. "Smart distribution system management considering electrical and thermal demand response of energy hubs," Energy, Elsevier, vol. 169(C), pages 38-49.
    8. Ayele, Getnet Tadesse & Haurant, Pierrick & Laumert, Björn & Lacarrière, Bruno, 2018. "An extended energy hub approach for load flow analysis of highly coupled district energy networks: Illustration with electricity and heating," Applied Energy, Elsevier, vol. 212(C), pages 850-867.
    9. Turner, W.J.N. & Walker, I.S. & Roux, J., 2015. "Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass," Energy, Elsevier, vol. 82(C), pages 1057-1067.
    10. Adefarati, T. & Bansal, R.C., 2017. "Reliability and economic assessment of a microgrid power system with the integration of renewable energy resources," Applied Energy, Elsevier, vol. 206(C), pages 911-933.
    11. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad & Hajizadeh, Amin & Mohammadi-ivatloo, Behnam, 2022. "A comprehensive review on optimization challenges of smart energy hubs under uncertainty factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Nikmehr, Nima, 2020. "Distributed robust operational optimization of networked microgrids embedded interconnected energy hubs," Energy, Elsevier, vol. 199(C).
    3. Mostafavi Sani, Mostafa & Mostafavi Sani, Hossein & Fowler, Michael & Elkamel, Ali & Noorpoor, Alireza & Ghasemi, Amir, 2022. "Optimal energy hub development to supply heating, cooling, electricity and freshwater for a coastal urban area taking into account economic and environmental factors," Energy, Elsevier, vol. 238(PB).
    4. Davatgaran, Vahid & Saniei, Mohsen & Mortazavi, Seyed Saeidollah, 2019. "Smart distribution system management considering electrical and thermal demand response of energy hubs," Energy, Elsevier, vol. 169(C), pages 38-49.
    5. Rakipour, Davood & Barati, Hassan, 2019. "Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response," Energy, Elsevier, vol. 173(C), pages 384-399.
    6. Morteza Vahid-Ghavidel & Mohammad Sadegh Javadi & Matthew Gough & Sérgio F. Santos & Miadreza Shafie-khah & João P.S. Catalão, 2020. "Demand Response Programs in Multi-Energy Systems: A Review," Energies, MDPI, vol. 13(17), pages 1-17, August.
    7. Jiao, P.H. & Chen, J.J. & Cai, X. & Wang, L.L. & Zhao, Y.L. & Zhang, X.H. & Chen, W.G., 2021. "Joint active and reactive for allocation of renewable energy and energy storage under uncertain coupling," Applied Energy, Elsevier, vol. 302(C).
    8. Ghayour, Sepideh Saravani & Barforoushi, Taghi, 2022. "Optimal scheduling of electrical and thermal resources and appliances in a smart home under uncertainty," Energy, Elsevier, vol. 261(PA).
    9. Naghikhani, Ali & Hosseini, Seyed Mohammad Hassan, 2022. "Optimal thermal and power planning considering economic and environmental issues in peak load management," Energy, Elsevier, vol. 239(PA).
    10. Aslani, Mehrdad & Mashayekhi, Mehdi & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes," Applied Energy, Elsevier, vol. 321(C).
    11. Mohamed, Mohamed A. & Tajik, Elham & Awwad, Emad Mahrous & El-Sherbeeny, Ahmed M. & Elmeligy, Mohammed A. & Ali, Ziad M., 2020. "A two-stage stochastic framework for effective management of multiple energy carriers," Energy, Elsevier, vol. 197(C).
    12. 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).
    13. Salehi, Javad & Namvar, Amin & Gazijahani, Farhad Samadi & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Effect of power-to-gas technology in energy hub optimal operation and gas network congestion reduction," Energy, Elsevier, vol. 240(C).
    14. Innocent Kamwa & Leila Bagherzadeh & Atieh Delavari, 2023. "Integrated Demand Response Programs in Energy Hubs: A Review of Applications, Classifications, Models and Future Directions," Energies, MDPI, vol. 16(11), pages 1-21, May.
    15. Gronier, Timothé & Fitó, Jaume & Franquet, Erwin & Gibout, Stéphane & Ramousse, Julien, 2022. "Iterative sizing of solar-assisted mixed district heating network and local electrical grid integrating demand-side management," Energy, Elsevier, vol. 238(PA).
    16. Arsalan Najafi & Mousa Marzband & Behnam Mohamadi-Ivatloo & Javier Contreras & Mahdi Pourakbari-Kasmaei & Matti Lehtonen & Radu Godina, 2019. "Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response," Energies, MDPI, vol. 12(8), pages 1-20, April.
    17. Fu, Xueqian & Zhang, Xiurong & Qiao, Zheng & Li, Gengyin, 2019. "Estimating the failure probability in an integrated energy system considering correlations among failure patterns," Energy, Elsevier, vol. 178(C), pages 656-666.
    18. Ma, Tengfei & Pei, Wei & Xiao, Hao & Kong, Li & Mu, Yunfei & Pu, Tianjiao, 2020. "The energy management strategies based on dynamic energy pricing for community integrated energy system considering the interactions between suppliers and users," Energy, Elsevier, vol. 211(C).
    19. Yuwei Wang & Yuanjuan Yang & Liu Tang & Wei Sun & Huiru Zhao, 2019. "A Stochastic-CVaR Optimization Model for CCHP Micro-Grid Operation with Consideration of Electricity Market, Wind Power Accommodation and Multiple Demand Response Programs," Energies, MDPI, vol. 12(20), pages 1-33, October.
    20. Chen, Honglin & Liu, Mingbo & Liu, Yingqi & Lin, Shunjiang & Yang, Zhibin, 2020. "Partial surrogate cuts method for network-constrained optimal scheduling of multi-carrier energy systems with demand response," Energy, Elsevier, vol. 196(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:261:y:2020:i:c:s030626191932080x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.