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Optimal bidding and scheduling of AA-CAES based energy hub considering cascaded consumption of heat

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  • Wu, Danman
  • Bai, Jiayu
  • Wei, Wei
  • Chen, Laijun
  • Mei, Shengwei

Abstract

The increasing level of uncertainty caused by high penetration of renewable energy and the widening gap of peak-valley demands call for the deployment of energy storage in power systems. Advanced-adiabatic compressed air energy storage (AA-CAES) is a large-scale physical energy storage technology with the merits of long lifetime, low environmental impact, and no emission. Moreover, AA-CAES works with electricity and heat, making it an excellent choice to realize the framework of energy hub. This paper proposes a cogeneration and storage architecture of an AA-CAES based energy hub in an industrial park. Particularly, the cascaded use of thermal energy to supply heat demands with different temperatures is modeled. A bi-level optimization model is established to study the optimal bidding and scheduling of AA-CAES based energy hub in the day-ahead market. The upper level is the power purchase and self-scheduling of the energy hub, aiming at minimizing the daily operation cost; the lower level represents the market clearing problem which determines the electricity price based on alternating-current optimal power flow. A radial basis function based surrogate optimization method is developed to solve the bi-level model with a nonlinear lower level problem. The problem is decomposed into second-order cone programs and a nonlinear surrogate model which can be solved without much computational effort. Numerical examples verify the effectiveness of the proposed method.

Suggested Citation

  • Wu, Danman & Bai, Jiayu & Wei, Wei & Chen, Laijun & Mei, Shengwei, 2021. "Optimal bidding and scheduling of AA-CAES based energy hub considering cascaded consumption of heat," Energy, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:energy:v:233:y:2021:i:c:s0360544221013815
    DOI: 10.1016/j.energy.2021.121133
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    5. He, Xin & Wang, Huanran & Li, Ruixiong & Sun, Hao & Chen, Hao & Li, ChengChen & Ge, Gangqiang & Tao, Feiyue, 2022. "Thermo-conversion of a physical energy storage system with high-energy density: Combination of thermal energy storage and gas-steam combined cycle," Energy, Elsevier, vol. 239(PE).
    6. Zhu, Dafeng & Yang, Bo & Ma, Chengbin & Wang, Zhaojian & Zhu, Shanying & Ma, Kai & Guan, Xinping, 2022. "Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints," Applied Energy, Elsevier, vol. 317(C).
    7. Chen, Wei & Bai, Jianshu & Wang, Guohua & Xie, Ningning & Ma, Linrui & Wang, Yazhou & Zhang, Tong & Xue, Xiaodai, 2023. "First and second law analysis and operational mode optimization of the compression process for an advanced adiabatic compressed air energy storage based on the established comprehensive dynamic model," Energy, Elsevier, vol. 263(PC).
    8. Kruk-Gotzman, Sylwia & Ziółkowski, Paweł & Iliev, Iliya & Negreanu, Gabriel-Paul & Badur, Janusz, 2023. "Techno-economic evaluation of combined cycle gas turbine and a diabatic compressed air energy storage integration concept," Energy, Elsevier, vol. 266(C).
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    10. Ziółkowski, Paweł & Stasiak, Kamil & Amiri, Milad & Mikielewicz, Dariusz, 2023. "Negative carbon dioxide gas power plant integrated with gasification of sewage sludge," Energy, Elsevier, vol. 262(PB).

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