Deep reinforcement learning-driven decision-making paradigm for electricity-carbon-hydrogen collaboration

doi:10.11930/j.issn.1004-9649.202602036

Abstract

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.

Key words: deep reinforcement learning, electric-carbon-hydrogen synergy, energy system optimization, electricity market, carbon market

ZHANG Fuchun, CHEN Wenjun, ZENG Tianze, LIU Nian, GUO Hongzhen, LIU Dunnan, WANG Peng, XU Chuanbo. Deep reinforcement learning-driven decision-making paradigm for electricity-carbon-hydrogen collaboration[J]. Electric Power, 2026, 59(6): 24-36.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202602036

https://www.electricpower.com.cn/EN/Y2026/V59/I6/24

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References 61

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复杂性	具体表现	对传统模型的挑战	对决策范式的影响
多能源耦合	电能流、氢能流与碳排放流在统一系统内相互耦合	难以在单一优化模型中完整刻画	决策需同时考虑跨能源影响，更适合策略层面统一协调
多时间尺度嵌套	电力调度（分钟-小时）、氢能储运（日-季节）、碳履约（年度）并存	静态或单周期优化难以反映跨期约束与长期影响	决策从单周期最优转向跨期策略演化
强不确定性	新能源出力、负荷、电价、碳价及政策预期高度不确定且非平稳	依赖概率分布或场景假设的模型鲁棒性不足	需要具备在线学习与自适应能力的决策方法
多主体博弈	发电主体、制氢主体、用能主体及监管机构目标不一致	集中式优化难以刻画主体间策略互动	决策对象由系统最优解转向主体行为规则
多目标内在冲突	经济性、低碳性与安全性难以同时最优，权重随环境变化	固定权重目标函数难以适配动态偏好	决策更关注长期综合回报而非瞬时最优
规则与环境演化	电力与碳市场规则、补贴与政策边界持续调整	模型频繁失配，需要反复重构与求解	决策范式由模型驱动转向交互驱动
高维状态空间	系统状态包含能量流、价格、配额等多维信息	状态空间维度灾难，求解效率受限	需要具备高维表征能力的决策方法

复杂性	具体表现	对传统模型的挑战	对决策范式的影响
多能源耦合	电能流、氢能流与碳排放流在统一系统内相互耦合	难以在单一优化模型中完整刻画	决策需同时考虑跨能源影响，更适合策略层面统一协调
多时间尺度嵌套	电力调度（分钟-小时）、氢能储运（日-季节）、碳履约（年度）并存	静态或单周期优化难以反映跨期约束与长期影响	决策从单周期最优转向跨期策略演化
强不确定性	新能源出力、负荷、电价、碳价及政策预期高度不确定且非平稳	依赖概率分布或场景假设的模型鲁棒性不足	需要具备在线学习与自适应能力的决策方法
多主体博弈	发电主体、制氢主体、用能主体及监管机构目标不一致	集中式优化难以刻画主体间策略互动	决策对象由系统最优解转向主体行为规则
多目标内在冲突	经济性、低碳性与安全性难以同时最优，权重随环境变化	固定权重目标函数难以适配动态偏好	决策更关注长期综合回报而非瞬时最优
规则与环境演化	电力与碳市场规则、补贴与政策边界持续调整	模型频繁失配，需要反复重构与求解	决策范式由模型驱动转向交互驱动
高维状态空间	系统状态包含能量流、价格、配额等多维信息	状态空间维度灾难，求解效率受限	需要具备高维表征能力的决策方法

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