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Information gap decision theory (IGDT)-based robust scheduling of combined cooling, heat and power energy hubs

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  • Jordehi, A. Rezaee
  • Javadi, Mohammad Sadegh
  • Shafie-khah, Miadreza
  • Catalão, João P.S.

Abstract

Energy hubs (EHs) are units wherein multiple energy carriers can be converted, stored and conditioned to simultaneously supply different energy demands. In this paper, a new model is proposed for unit commitment in renewable EHs with electric, thermal and cooling demands, different storage systems, combined heat and power (CHP) unit, boiler, electric chiller, absorption chiller, PV module, wind turbine and battery charging station (BCS). Using information gap decision theory (IGDT), day-ahead EH scheduling is done from risk-neutral, risk-averse and risk-seeking perspectives, considering the uncertainties of electric demands, BCS demands, heat demands, cooling demands, PV and wind power and electricity prices. Comprehensive models are used for storage systems considering their degradation, charging loss, discharging loss and storage loss; the ramp-up and ramp-down rate limits, start-up and shut-down costs of CHP, boiler and cooling components are considered. The effect of risk as well as effect of critical cost deviation factor and target cost deviation factor on EH operation cost and schedule of EH components is investigated. The findings indicate that the sensitivity of EH operation cost may be very different with respect to different sets of uncertain input data. The findings also show the significant effect of risk-awareness on schedule of EH components and its operation cost.

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  • 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).
    • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s036054422101166x
    DOI: 10.1016/j.energy.2021.120918
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    References listed on IDEAS

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    Cited by:

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    2. Roy, Nibir Baran & Das, Debapriya, 2024. "Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
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    5. Wang, Y.X. & Chen, J.J. & Zhao, Y.L. & Xu, B.Y., 2024. "Incorporate robust optimization and demand defense for optimal planning of shared rental energy storage in multi-user industrial park," Energy, Elsevier, vol. 301(C).
    6. Qiu, Dawei & Xue, Juxing & Zhang, Tingqi & Wang, Jianhong & Sun, Mingyang, 2023. "Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading," Applied Energy, Elsevier, vol. 333(C).
    7. Tostado-Véliz, Marcos & Jordehi, Ahmad Rezaee & Mansouri, Seyed Amir & Jurado, Francisco, 2023. "A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots," Energy, Elsevier, vol. 263(PD).
    8. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal market-based operation of microgrid with the integration of wind turbines, energy storage system and demand response resources," Energy, Elsevier, vol. 239(PB).

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