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Optimal stochastic scheduling of cryogenic energy storage with wind power in the presence of a demand response program

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  • Kalavani, Farshad
  • Mohammadi-Ivatloo, Behnam
  • Zare, Kazem

Abstract

This paper provides a stochastic method to conduct the optimal scheduling of the combination of wind power and new-type large-scale energy storage with considering the demand response program in the electricity market. The integration of ASU and CES make the opportunity to store energy in the form of liquid in the off-peak periods and recovering the electricity in the peak periods. The uncertainty of electricity price, load demand and wind speed considered as the stochastic model uncertain parameters. The optimal operation of wind turbine, CES, and conventional generation units, considering the stochastic models for price, demand, and wind speed, was formulated as a mixed-integer non-linear programming (MINLP) problem. The constraints of CES operation, liquid and gas product demands, and ASU production were considered in ASU-CES modeling. The startup cost, minimum on/off time constraints, ramp rate, and capacity limits were considered in the formulation of conventional power generation. The demand response (DR) program was adopted to increase the total expected profit and decrease the total operational cost. The results revealed that the application of CES to attest system containing ASU increases the total profit of power generation units and decreases the total cost of generating power to serve load demands.

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  • Kalavani, Farshad & Mohammadi-Ivatloo, Behnam & Zare, Kazem, 2019. "Optimal stochastic scheduling of cryogenic energy storage with wind power in the presence of a demand response program," Renewable Energy, Elsevier, vol. 130(C), pages 268-280.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:268-280
    DOI: 10.1016/j.renene.2018.06.070
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    3. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2019. "A copula-based method to consider uncertainties for multi-objective energy management of microgrid in presence of demand response," Energy, Elsevier, vol. 175(C), pages 879-890.
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    5. Zhang, Mengling & Jiao, Zihao & Ran, Lun & Zhang, Yuli, 2023. "Optimal energy and reserve scheduling in a renewable-dominant power system," Omega, Elsevier, vol. 118(C).
    6. Xu, Qilong & Wang, Shuai & Luo, Kun & Mu, Yanfei & Pan, Lu & Fan, Jianren, 2023. "Process modelling and optimization of a 250 MW IGCC system: ASU optimization and thermodynamic analysis," Energy, Elsevier, vol. 282(C).
    7. Mirzaei, Mohammad Amin & Sadeghi-Yazdankhah, Ahmad & Mohammadi-Ivatloo, Behnam & Marzband, Mousa & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Integration of emerging resources in IGDT-based robust scheduling of combined power and natural gas systems considering flexible ramping products," Energy, Elsevier, vol. 189(C).
    8. O'Callaghan, O. & Donnellan, P., 2021. "Liquid air energy storage systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    9. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
    10. Borri, Emiliano & Tafone, Alessio & Romagnoli, Alessandro & Comodi, Gabriele, 2021. "A review on liquid air energy storage: History, state of the art and recent developments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    11. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    12. Vecchi, Andrea & Naughton, James & Li, Yongliang & Mancarella, Pierluigi & Sciacovelli, Adriano, 2020. "Multi-mode operation of a Liquid Air Energy Storage (LAES) plant providing energy arbitrage and reserve services – Analysis of optimal scheduling and sizing through MILP modelling with integrated ther," Energy, Elsevier, vol. 200(C).
    13. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    14. Hosseinnia, Hamed & Modarresi, Javad & Nazarpour, Daryoush, 2020. "Optimal eco-emission scheduling of distribution network operator and distributed generator owner under employing demand response program," Energy, Elsevier, vol. 191(C).
    15. Wu, Y.J. & Liang, X.Y. & Huang, T. & Lin, Z.W. & Li, Z.X. & Hossain, Mohammad Farhad, 2021. "A hierarchical framework for renewable energy sources consumption promotion among microgrids through two-layer electricity prices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    16. Nayak, Dhyaan Sandeep & Misra, Shamik, 2024. "An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand uncertainty," Energy, Elsevier, vol. 290(C).

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