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Grid-connected energy hubs in the coordinated multi-energy management based on day-ahead market framework

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  • Dini, Anoosh
  • Pirouzi, Sasan
  • Norouzi, Mohammadali
  • Lehtonen, Matti

Abstract

This paper presents the coordinated energy hubs energy management in different networks based on the cooperation of hubs in day-ahead (DA) markets. The proposed model connects the electrical, natural gas and district heating networks by considering the electrical, natural gas as the hub's input, and electrical and district heating energies for the output of hubs. In this study, combined heat and power (CHP) and electrical storage systems, boiler, electric vehicles (EVs) parking lot and renewable energy sources (RESs) are considered in the context of energy hubs. In the process, the linear coordinated energy management problem includes a single linear objective function that is aimed at maximizing hubs profit in the DA market while considering linear networks and hubs constraints. Moreover, this model is taken into account uncertainty in different loads, DA market energy price, RES output power, and EVs parameters. Hence, the proposed problem is formulated as a scenario-based stochastic model using Monte Carlo simulation (MCS) for scenario generating and the fast backward/forward scenario reduction method for scenario reducing. Finally, this model is applied to a test system in the GAMS software environment, and the capacity and validity of proposed hubs energy management are investigated. Finally, simulation results confirm the efficiency of the hub coordinated energy management in the DA market.

Suggested Citation

  • Dini, Anoosh & Pirouzi, Sasan & Norouzi, Mohammadali & Lehtonen, Matti, 2019. "Grid-connected energy hubs in the coordinated multi-energy management based on day-ahead market framework," Energy, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:energy:v:188:y:2019:i:c:s0360544219317505
    DOI: 10.1016/j.energy.2019.116055
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    5. AkbaiZadeh, MohammadReza & Niknam, Taher & Kavousi-Fard, Abdollah, 2021. "Adaptive robust optimization for the energy management of the grid-connected energy hubs based on hybrid meta-heuristic algorithm," Energy, Elsevier, vol. 235(C).
    6. Dini, Anoosh & Hassankashi, Alireza & Pirouzi, Sasan & Lehtonen, Matti & Arandian, Behdad & Baziar, Ali Asghar, 2022. "A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response," Energy, Elsevier, vol. 239(PA).
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    11. Hosseini, Seyed Hamid Reza & Allahham, Adib & Walker, Sara Louise & Taylor, Phil, 2021. "Uncertainty analysis of the impact of increasing levels of gas and electricity network integration and storage on Techno-Economic-Environmental performance," Energy, Elsevier, vol. 222(C).
    12. Zhang, XiaoWei & Yu, Xiaoping & Ye, Xinping & Pirouzi, Sasan, 2023. "Economic energy managementof networked flexi-renewable energy hubs according to uncertainty modeling by the unscented transformation method," Energy, Elsevier, vol. 278(PB).
    13. 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).
    14. Sina Parhoudeh & Pablo Eguía López & Abdollah Kavousi Fard, 2023. "Stochastic Coordinated Management of Electrical–Gas–Thermal Networks in Flexible Energy Hubs Considering Day-Ahead Energy and Ancillary Markets," Sustainability, MDPI, vol. 15(13), pages 1-26, July.
    15. 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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