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Day-ahead scheduling of energy hubs with parking lots for electric vehicles considering uncertainties

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  • Jordehi, A. Rezaee
  • Javadi, Mohammad Sadegh
  • Catalão, João P.S.

Abstract

Energy hubs (EHs) are units in which multiple energy carriers are converted, conditioned and stored to simultaneously supply different forms of energy demands. In this research, the objective is to develop a new stochastic model for unit commitment in EHs including an intelligent electric vehicle (EV) parking lot, boiler, photovoltaic (PV) module, fuel cell, absorption chiller, electric heat pump, electric/thermal/cooling storage systems, with electricity and natural gas (NG) as inputs and electricity, heat, cooling and NG as demands. The uncertainties of demands, PV power and initial energy of EV batteries are modeled with Monte Carlo Simulation. The effect of demand response and demand participation factors as well as effect of EVs and storage systems on EH operation are investigated. The results indicate that thermal demand response is more effective than electric and cooling demand response; as it decreases EH operation cost by 12%, while electric demand response and cooling demand response decrease it respectively by 9.3% and 4.2%. The results show that at low electric/thermal/cooling demand participation factors, an increase in participation factor sharply decreases EH operation cost, while the same amount of increase at higher participation factors leads to a smaller decrease in operation cost. The results also indicate that thermal storage system and cooling storage system have significant effect on reduction of EH operation cost, while the effect of electric storage system is trivial.

Suggested Citation

  • Jordehi, A. Rezaee & Javadi, Mohammad Sadegh & Catalão, João P.S., 2021. "Day-ahead scheduling of energy hubs with parking lots for electric vehicles considering uncertainties," Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:energy:v:229:y:2021:i:c:s0360544221009579
    DOI: 10.1016/j.energy.2021.120709
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    References listed on IDEAS

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    1. Salehimaleh, Mohammad & Akbarimajd, Adel & Valipour, Khalil & Dejamkhooy, Abdolmajid, 2018. "Generalized modeling and optimal management of energy hub based electricity, heat and cooling demands," Energy, Elsevier, vol. 159(C), pages 669-685.
    2. Mansouri, Seyed Amir & Ahmarinejad, Amir & Javadi, Mohammad Sadegh & Catalão, João P.S., 2020. "Two-stage stochastic framework for energy hubs planning considering demand response programs," Energy, Elsevier, vol. 206(C).
    3. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
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    Cited by:

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    2. Javadi, Mohammad Sadegh & Esmaeel Nezhad, Ali & Jordehi, Ahmad Rezaee & Gough, Matthew & Santos, Sérgio F. & Catalão, João P.S., 2022. "Transactive energy framework in multi-carrier energy hubs: A fully decentralized model," Energy, Elsevier, vol. 238(PB).
    3. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Arévalo, Paul & Turky, Rania A. & Jurado, Francisco, 2022. "A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations," Energy, Elsevier, vol. 253(C).
    4. 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).
    5. Luo, Lizi & He, Pinquan & Gu, Wei & Sheng, Wanxing & Liu, Keyan & Bai, Muke, 2022. "Temporal-spatial scheduling of electric vehicles in AC/DC distribution networks," Energy, Elsevier, vol. 255(C).
    6. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    7. Anna Auza & Ehsan Asadi & Behrang Chenari & Manuel Gameiro da Silva, 2023. "A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles," Energies, MDPI, vol. 16(13), pages 1-25, June.
    8. Najafi, Arsalan & Pourakbari-Kasmaei, Mahdi & Jasinski, Michal & Lehtonen, Matti & Leonowicz, Zbigniew, 2021. "A hybrid decentralized stochastic-robust model for optimal coordination of electric vehicle aggregator and energy hub entities," Applied Energy, Elsevier, vol. 304(C).
    9. 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).
    10. Zhang, Bin & Hu, Weihao & Cao, Di & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach," Applied Energy, Elsevier, vol. 339(C).
    11. Saberi-Beglar, Kasra & Zare, Kazem & Seyedi, Heresh & Marzband, Mousa & Nojavan, Sayyad, 2023. "Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    12. Lankeshwara, Gayan & Sharma, Rahul & Yan, Ruifeng & Saha, Tapan K., 2022. "Control algorithms to mitigate the effect of uncertainties in residential demand management," Applied Energy, Elsevier, vol. 306(PA).
    13. 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).

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