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Risk-averse stochastic scheduling of hydrogen-based flexible loads under 100% renewable energy scenario

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  • Chen, Mengxiao
  • Cao, Xiaoyu
  • Zhang, Zitong
  • Yang, Lun
  • Ma, Donglai
  • Li, Miaomiao

Abstract

The development of 100% renewable energy (RE) systems provides a viable solution for achieving the global target of carbon neutrality. To support the reliable and economical operation of RE-based local energy networks, this paper presents a joint scheduling model for grid-scale RE generation and hydrogen-based flexible loads. The direct load control (DLC) through hydrogen-electrical microgrids is analytically modeled for leveraging the intrinsic flexibility of demand-side multi-energy synergy. To handle the uncertainty and volatility of RE generation, a risk-averse stochastic programming method with the receding-horizon mechanism is developed. Also, the power balancing cost in scheduling objectives is represented as a conditional value-at-risk (CVaR) measure to control the risks of fully RE supply. Case studies on an exemplary RE system confirm the effectiveness and economic benefits of the proposed method. The hydrogen-enabled DLC can largely mitigate the supply–demand mismatches, which shows a great potential to facilitate 100% RE scenarios.

Suggested Citation

  • Chen, Mengxiao & Cao, Xiaoyu & Zhang, Zitong & Yang, Lun & Ma, Donglai & Li, Miaomiao, 2024. "Risk-averse stochastic scheduling of hydrogen-based flexible loads under 100% renewable energy scenario," Applied Energy, Elsevier, vol. 370(C).
    • Handle: RePEc:eee:appene:v:370:y:2024:i:c:s0306261924009528
    DOI: 10.1016/j.apenergy.2024.123569
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    2. Zhang, Xiao-Yan & Wang, Cenfeng & Xiao, Jiang-Wen & Wang, Yan-Wu, 2025. "A transactive energy cooperation scheduling for hydrogen-based community microgrid with refueling preferences of hydrogen vehicles," Applied Energy, Elsevier, vol. 377(PC).
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    4. Wu, Haotian & Ke, Deping & Xu, Jian & Song, Lin & Liao, Siyang & Zhang, Pengcheng, 2025. "Low-carbon economic dispatch of iron and steel industry empowered by wind‑hydrogen energy: Modeling and stochastic programming," Applied Energy, Elsevier, vol. 387(C).

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