中国电力2026，Vol.59Issue(6)：24-36,13.DOI:10.11930/j.issn.1004-9649.202602036
深度强化学习驱动的电-碳-氢协同决策范式
Deep reinforcement learning-driven decision-making paradigm for electricity-carbon-hydrogen collaboration
摘要
Abstract
To achieve synergistic optimization of low-carbon energy systems,electricity-carbon-hydrogen synergy has become one of the critical pathways.However,its high dimensionality,nonlinearity,and strong uncertainties limit traditional optimization methods.Deep reinforcement learning(DRL),with its ability to learn from data,adapt to dynamic environments,and support multi-objective decision-making,offers a promising solution.This paper reviews the mechanisms of electricity-carbon-hydrogen synergy and the necessity of applying DRL,summarizing recent progress in electricity markets,carbon markets,electricity-carbon synergy,electricity-hydrogen synergy,and their integration.The results show that DRL holds significant potential for enhancing renewable energy integration,optimizing carbon trading,and coordinating multi-energy flows,though challenges remain in model complexity,interpretability,safety,and multi-objective trade-offs.Future research should focus on integrating DRL with large language models,improving robustness,safety,and interpretability,and enabling cross-scale coordination to facilitate practical deployment.关键词
深度强化学习/电-碳-氢协同/能源系统优化/电力市场/碳市场Key words
deep reinforcement learning/electric-carbon-hydrogen synergy/energy system optimization/electricity market/carbon market引用本文复制引用
张富春,陈文君,曾天泽,刘念,郭红珍,刘敦楠,王鹏,许传博..深度强化学习驱动的电-碳-氢协同决策范式[J].中国电力,2026,59(6):24-36,13.基金项目
This work is supported by Joint Funds of National Natural Science Foundation of China(No.U23B20124)and National Natural Science Foundation of China(No.72303063).国家自然科学基金联合基金重点项目(U23B20124) （No.U23B20124）
国家自然科学基金资助项目(72303063). （72303063）
