IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v265y2023ics0360544222032479.html
   My bibliography  Save this article

Virtual smart energy Hub: A powerful tool for integrated multi energy systems operation

Author

Listed:
  • Bashiri Khouzestani, Leyla
  • Sheikh-El-Eslami, Mohammad Kazem
  • Salemi, Amir Hosein
  • Gerami Moghaddam, Iman

Abstract

The present work introduces a novel unit, named virtual smart energy hub (VSEH). VSEH consists of a combination of smart energy grids, smart energy hubs (SEHs), and virtual power plants (VPPs) and simultaneously benefits from the advantages of SEHs and VPPs on a smart-energy-grid platform. A comprehensive two-stage model was proposed to solve the VSEH optimal operation problem in order to trading in day-ahead and intraday heat and power market. The proposed stochastic programming model is risk based and the conditional value at risk (CVaR) index was employed to represent the risk in the scheduling model. On the first stage of the proposed model, VSEH schedules transactions amounts by considering energy prices and SEHs constraints in order to maximize profits from participation in day-ahead heat and power markets. In the second stage, VSEH, through the more accurate prediction of uncertainties before operation time and with the aim of maximizing profits and correcting deviations relative to the settled transactions in the first stage, re-schedules the SEHs in intraday heat and power markets. Non-arbitrage constraints were established between day-ahead and intraday energy markets in the proposed model. Moreover, the presented structure was modeled and comprehensively defined in mathematical terms as a mixed-integer linear programming (MILP) model with a minimum number of real variables. The performance of this model was evaluated by implementing it on a coalition with various SEHs, as a result of which the profitability of the coalition and the increase in the risk tolerance level of the SEHs group was confirmed. The results indicate that formation of the VSEH can act as means of risk management in addition to reducing risk-associated costs regardless of the circumstances on the day of operation and the acceptable level risk tolerance of the SEHs. In other words, the integration of SEHs will result in a lower risk aversion cost.

Suggested Citation

  • Bashiri Khouzestani, Leyla & Sheikh-El-Eslami, Mohammad Kazem & Salemi, Amir Hosein & Gerami Moghaddam, Iman, 2023. "Virtual smart energy Hub: A powerful tool for integrated multi energy systems operation," Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222032479
    DOI: 10.1016/j.energy.2022.126361
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222032479
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126361?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Majidi, Majid & Nojavan, Sayyad & Zare, Kazem, 2017. "A cost-emission framework for hub energy system under demand response program," Energy, Elsevier, vol. 134(C), pages 157-166.
    2. Ma, Tengfei & Wu, Junyong & Hao, Liangliang & Lee, Wei-Jen & Yan, Huaguang & Li, Dezhi, 2018. "The optimal structure planning and energy management strategies of smart multi energy systems," Energy, Elsevier, vol. 160(C), pages 122-141.
    3. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, December.
    4. Chicco, Gianfranco & Mancarella, Pierluigi, 2009. "Distributed multi-generation: A comprehensive view," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(3), pages 535-551, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Minghao & Sun, Yi & Xie, Zhiyuan & Lin, Nvgui & Wu, Peng, 2023. "An efficient and privacy-preserving algorithm for multiple energy hubs scheduling with federated and matching deep reinforcement learning," Energy, Elsevier, vol. 284(C).
    2. Wang, Jian & Ilea, Valentin & Bovo, Cristian & Xie, Ning & Wang, Yong, 2023. "Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework," Energy, Elsevier, vol. 278(PB).
    3. Liu, Tianhao & Tian, Jun & Zhu, Hongyu & Goh, Hui Hwang & Liu, Hui & Wu, Thomas & Zhang, Dongdong, 2023. "Key technologies and developments of multi-energy system: Three-layer framework, modelling and optimisation," Energy, Elsevier, vol. 277(C).
    4. Zhao, Kaifang & Qiu, Kai & Yan, Jian & Shaker, Mir Pasha, 2023. "Technical and economic operation of VPPs based on competitive bi–level negotiations," Energy, Elsevier, vol. 282(C).
    5. Liao, Zitian & Liao, Xiaoqun & Khakichi, Aroos, 2024. "Optimum planning of energy hub with participation in electricity market and heat markets and application of integrated load response program with improved particle swarm algorithm," Energy, Elsevier, vol. 286(C).
    6. Liu, Xin & Li, Yang & Wang, Li & Tang, Junbo & Qiu, Haifeng & Berizzi, Alberto & Valentin, Ilea & Gao, Ciwei, 2024. "Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability," Energy, Elsevier, vol. 297(C).

    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. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. 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).
    3. Ramsebner, J. & Haas, R. & Auer, H. & Ajanovic, A. & Gawlik, W. & Maier, C. & Nemec-Begluk, S. & Nacht, T. & Puchegger, M., 2021. "From single to multi-energy and hybrid grids: Historic growth and future vision," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    4. Hoseini, Naghi & Sheikholeslami, Abdolreza & Barforoushi, Taghi & Latify, Mohammad Amin, 2020. "Preventive maintenance mid-term scheduling of resources in multi-carrier energy systems," Energy, Elsevier, vol. 197(C).
    5. 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).
    6. Qiu, Dawei & Dong, Zihang & Zhang, Xi & Wang, Yi & Strbac, Goran, 2022. "Safe reinforcement learning for real-time automatic control in a smart energy-hub," Applied Energy, Elsevier, vol. 309(C).
    7. Charani Shandiz, Saeid & Rismanchi, Behzad & Foliente, Greg, 2021. "Energy master planning for net-zero emission communities: State of the art and research challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    8. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    9. Gabriele Loreti & Andrea Luigi Facci & Stefano Ubertini, 2021. "High-Efficiency Combined Heat and Power through a High-Temperature Polymer Electrolyte Membrane Fuel Cell and Gas Turbine Hybrid System," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    10. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    11. Funcke, Simon & Bauknecht, Dierk, 2016. "Typology of centralised and decentralised visions for electricity infrastructure," Utilities Policy, Elsevier, vol. 40(C), pages 67-74.
    12. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    13. Grover-Silva, Etta & Heleno, Miguel & Mashayekh, Salman & Cardoso, Gonçalo & Girard, Robin & Kariniotakis, George, 2018. "A stochastic optimal power flow for scheduling flexible resources in microgrids operation," Applied Energy, Elsevier, vol. 229(C), pages 201-208.
    14. Aman, M.M. & Jasmon, G.B. & Bakar, A.H.A. & Mokhlis, H., 2014. "A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm," Energy, Elsevier, vol. 66(C), pages 202-215.
    15. Gottschamer, L. & Zhang, Q., 2016. "Interactions of factors impacting implementation and sustainability of renewable energy sourced electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 164-174.
    16. Hernán Gómez-Villarreal & Miguel Carrión & Ruth Domínguez, 2019. "Optimal Management of Combined-Cycle Gas Units with Gas Storage under Uncertainty," Energies, MDPI, vol. 13(1), pages 1-29, December.
    17. Alfredo Alcayde & Raul Baños & Francisco M. Arrabal-Campos & Francisco G. Montoya, 2019. "Optimization of the Contracted Electric Power by Means of Genetic Algorithms," Energies, MDPI, vol. 12(7), pages 1-13, April.
    18. Hanif, Sarmad & Alam, M.J.E. & Roshan, Kini & Bhatti, Bilal A. & Bedoya, Juan C., 2022. "Multi-service battery energy storage system optimization and control," Applied Energy, Elsevier, vol. 311(C).
    19. Jiang-Jiang, Wang & Chun-Fa, Zhang & You-Yin, Jing, 2010. "Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China," Applied Energy, Elsevier, vol. 87(4), pages 1247-1259, April.
    20. Llaria, Alvaro & Curea, Octavian & Jiménez, Jaime & Camblong, Haritza, 2011. "Survey on microgrids: Unplanned islanding and related inverter control techniques," Renewable Energy, Elsevier, vol. 36(8), pages 2052-2061.

    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:energy:v:265:y:2023:i:c:s0360544222032479. 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.journals.elsevier.com/energy .

    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.