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Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties

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  • Liu, Yangyang
  • Shen, Zhongqi
  • Tang, Xiaowei
  • Lian, Hongbo
  • Li, Jiarui
  • Gong, Jinxia

Abstract

It is challenging for renewable power (such as wind power) to participate in electricity markets, because of various uncertainties in terms of prices and power generation fluctuations. Further, the exact probability distributions of random variables are difficult to specify, leading to problems and errors with respect to the bidding strategy and risk management conducted by power generation companies. To overcome these issues, a risk averse bidding strategy is proposed to allow a wind-hydro hybrid system to participate in an electricity market when only partial information is available about the underlying probability distributions of random variables. A mixture distribution structure is employed to model multiple distributional uncertainties for the hybrid system, and the worst-case conditional value-at-risk is used to measure the hybrid system’s risk considering the distributional uncertainties. This bidding strategy provides a solution that allows power generation companies to manage their distributional uncertainties in electricity markets, especially for renewable power with low accuracy forecasts. This method can estimate the benefits of forecast accuracy improvement and predictions’ probability information on generation companies. Compared with the stochastic bidding strategy, the proposed bidding strategy obtains robuster results for distributions to achieve better risk management, as illustrated by the study case.

Suggested Citation

  • Liu, Yangyang & Shen, Zhongqi & Tang, Xiaowei & Lian, Hongbo & Li, Jiarui & Gong, Jinxia, 2019. "Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316058
    DOI: 10.1016/j.apenergy.2019.113918
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    6. He, Shuaijia & Gao, Hongjun & Tang, Zao & Chen, Zhe & Jin, Xiaolong & Liu, Junyong, 2023. "Worst CVaR based energy management for generalized energy storage enabled building-integrated energy systems," Renewable Energy, Elsevier, vol. 203(C), pages 255-266.
    7. Street, Alexandre & Valladão, Davi & Lawson, André & Velloso, Alexandre, 2020. "Assessing the cost of the Hazard-Decision simplification in multistage stochastic hydrothermal scheduling," Applied Energy, Elsevier, vol. 280(C).
    8. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun, 2021. "Hybrid time-scale energy optimal scheduling strategy for integrated energy system with bilateral interaction with supply and demand," Applied Energy, Elsevier, vol. 285(C).
    9. Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
    10. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    11. Yongqi Zhao & Jiajia Chen, 2021. "A Quantitative Risk-Averse Model for Optimal Management of Multi-Source Standalone Microgrid with Demand Response and Pumped Hydro Storage," Energies, MDPI, vol. 14(9), pages 1-17, May.
    12. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun, 2021. "A novel bi-level robust game model to optimize a regionally integrated energy system with large-scale centralized renewable-energy sources in Western China," Energy, Elsevier, vol. 228(C).
    13. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    14. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    15. Li, Qirui & Yang, Zhifang & Yu, Juan & Li, Wenyuan, 2023. "Impacts of previous revenues on bidding strategies in electricity market: A quantitative analysis," Applied Energy, Elsevier, vol. 345(C).
    16. Khaloie, Hooman & Abdollahi, Amir & Shafie-khah, Miadreza & Anvari-Moghaddam, Amjad & Nojavan, Sayyad & Siano, Pierluigi & Catalão, João P.S., 2020. "Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model," Applied Energy, Elsevier, vol. 259(C).

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