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

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  • 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
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    References listed on IDEAS

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    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. Chankook Park & Wan Gyu Heo & Myung Eun Lee, 2024. "Study on Consumers’ Perceived Benefits and Risks of Smart Energy System," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 288-300, May.
    4. 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).
    5. 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).
    6. 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).
    7. 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).

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