深度强化学习驱动的电-碳-氢协同决策范式OA

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.