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Carbon and electricity trading for the green hydrogen-based integrated energy system: A deep reinforcement learning-based scheduling optimization

Author

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  • Lan, Penghang
  • Chen, She
  • Wang, Feng

Abstract

Achieving low-carbon and economically efficient operation is the primary goal of the green hydrogen-based integrated energy system (GHIES). While carbon trading effectively reduces emissions and electricity trading improves economic performance, their integration within the GHIES context has rarely been investigated. A key challenge is managing uncertainties when optimizing GHIES. To address this, deep reinforcement learning (DRL) is adopted to optimize GHIES without explicitly characterizing uncertainties. Furthermore, four scenarios are compared: (1) neither electricity trading nor carbon trading is considered, (2) carbon trading is considered, but electricity trading is not, (3) electricity trading is considered, but carbon trading is not, and (4) both electricity trading and carbon trading are considered. The results show that Scenario 4 outperforms others in both training and testing, with daily costs reduced by 30.44 % and CO2 emissions lowered by 20.32 %. This research contributes to a low-carbon and economically efficient roadmap for GHIES and sheds light on the application of artificial intelligence in renewable energy systems.

Suggested Citation

  • Lan, Penghang & Chen, She & Wang, Feng, 2026. "Carbon and electricity trading for the green hydrogen-based integrated energy system: A deep reinforcement learning-based scheduling optimization," Renewable Energy, Elsevier, vol. 256(PC).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pc:s0960148125018403
    DOI: 10.1016/j.renene.2025.124176
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