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Stochastic programming-based optimal bidding of compressed air energy storage with wind and thermal generation units in energy and reserve markets

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  • Akbari, Ebrahim
  • Hooshmand, Rahmat-Allah
  • Gholipour, Mehdi
  • Parastegari, Moein

Abstract

One effective way to compensate for uncertainties is the use and management of energy storage. Therefore, a new method based on stochastic programming (SP) is proposed here, for optimal bidding of a generating company (GenCo) owning a compressed air energy storage (CAES) along with wind and thermal units to maximize profits. This scheduling has been presented for the GenCo's participation in day-ahead energy and spinning reserve (SR) markets and CVaR is also considered as a risk-controlling index. Firstly, the obtained results are validated by comparing with those of two previous studies. Then, the complete results of the proposed method are presented on a real power system, which indicate the capability of SP in scheduling CAES units. Furthermore, it is observed that CAES units can gain greater profits in joint energy and reserve markets due to their high ramp rates. In addition, the value of stochastic solution (VSS) is used to quantify the advantage of the stochastic method over a deterministic one, which illustrates the advantage of SP-based optimal bidding method especially for CAES and wind units and also for risk-averse GenCos. Overall, it is concluded that the stochastic method is efficient for optimal-bidding of GenCos owning CAES and wind units.

Suggested Citation

  • Akbari, Ebrahim & Hooshmand, Rahmat-Allah & Gholipour, Mehdi & Parastegari, Moein, 2019. "Stochastic programming-based optimal bidding of compressed air energy storage with wind and thermal generation units in energy and reserve markets," Energy, Elsevier, vol. 171(C), pages 535-546.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:535-546
    DOI: 10.1016/j.energy.2019.01.014
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    12. Ochoa, Tomás & Gil, Esteban & Angulo, Alejandro & Valle, Carlos, 2022. "Multi-agent deep reinforcement learning for efficient multi-timescale bidding of a hybrid power plant in day-ahead and real-time markets," Applied Energy, Elsevier, vol. 317(C).
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    14. Peng, Feixiang & Hu, Shubo & Fan, Xuanxuan & Sun, Hui & Zhou, Wei & Guo, Furan & Song, Wenzhuo, 2021. "Sequential coalition formation for wind-thermal combined bidding," Energy, Elsevier, vol. 236(C).
    15. Li, Ke & Yang, Fan & Wang, Lupan & Yan, Yi & Wang, Haiyang & Zhang, Chenghui, 2022. "A scenario-based two-stage stochastic optimization approach for multi-energy microgrids," Applied Energy, Elsevier, vol. 322(C).
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    17. Mostafa Nasouri Gilvaei & Mahmood Hosseini Imani & Mojtaba Jabbari Ghadi & Li Li & Anahita Golrang, 2021. "Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units," Energies, MDPI, vol. 14(3), pages 1-20, January.
    18. Khashayar Hamedi & Shahrbanoo Sadeghi & Saeed Esfandi & Mahdi Azimian & Hessam Golmohamadi, 2021. "Eco-Emission Analysis of Multi-Carrier Microgrid Integrated with Compressed Air and Power-to-Gas Energy Storage Technologies," Sustainability, MDPI, vol. 13(9), pages 1-18, April.
    19. Yuan, Jiahang & Luo, Xinggang & Li, Zhendong & Li, Lingfei & Ji, Pengli & Zhou, Qing & Zhang, Zhongliang, 2021. "Sustainable development evaluation on wind power compressed air energy storage projects based on multi-source heterogeneous data," Renewable Energy, Elsevier, vol. 169(C), pages 1175-1189.
    20. Liang Tian & Yunlei Xie & Bo Hu & Xinping Liu & Tuoyu Deng & Huanhuan Luo & Fengqiang Li, 2019. "A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage," Energies, MDPI, vol. 12(17), pages 1-27, August.
    21. Yang, Jie & Ma, Tieding & Ma, Kai & Yang, Bo & Guerrero, Josep M. & Liu, Zhixin, 2021. "Trading mechanism and pricing strategy of integrated energy systems based on credit rating and Bayesian game," Energy, Elsevier, vol. 232(C).

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