深度强化学习驱动的电-碳-氢协同决策范式

张富春; 陈文君; 曾天泽; 刘念; 郭红珍; 刘敦楠; 王鹏; 许传博

doi:10.11930/j.issn.1004-9649.202602036

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深度强化学习驱动的电-碳-氢协同决策范式

智能-绿色-韧性：面向2035的新型电力系统技术与市场融合 | 更新时间：2026-07-01

- 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
- 中国电力 2026年59卷第6期页码：24-36
- 作者机构：
  
  1. 华北电力大学经济与管理学院,北京,102206
  2. 澳大利亚新南威尔士大学计算机科学与工程学院，澳大利亚悉尼 NSW,2052
  3. 新能源电力系统全国重点实验室（华北电力大学）,北京,102206
  4. 华北电力大学国家能源发展战略研究院,北京,102206
- 作者简介：
- 基金信息：
  
  国家自然科学基金联合基金重点项目（U23B20124）;国家自然科学基金资助项目（72303063）。
- DOI：10.11930/j.issn.1004-9649.202602036
  中图分类号：
- 网络首发：2026-06-22，
  
  纸质出版：2026
- 稿件说明：
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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.

ZHANG Fuchun, CHEN Wenjun, ZENG Tianze, et al. Deep reinforcement learning-driven decision-making paradigm for electricity-carbon-hydrogen collaboration[J]. 2026, 59(6): 24-36.
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. DOI： 10.11930/j.issn.1004-9649.202602036.

ZHANG Fuchun, CHEN Wenjun, ZENG Tianze, et al. Deep reinforcement learning-driven decision-making paradigm for electricity-carbon-hydrogen collaboration[J]. 2026, 59(6): 24-36. DOI： 10.11930/j.issn.1004-9649.202602036.

摘要

为实现低碳能源系统协同优化，电-碳-氢协同成为关键路径之一，但其高维、非线性、强不确定等特征制约传统优化方法应用。深度强化学习具备从数据中自主学习、适应动态环境与多目标决策的能力，为破解该协同优化难题提供了新途径。系统梳理了电-碳-氢协同机理，阐述了深度强化学习应用于该领域的必要性，综述了其在电力市场、碳市场、电-碳协同、电-氢协同及三者集成中的研究进展。结果表明，深度强化学习在提升新能源消纳、优化碳交易与多能流协同等方面潜力显著，但仍面临模型复杂性、策略可解释性与安全性、多目标权衡等挑战。未来应聚焦深度强化学习与大语言模型融合、鲁棒与安全机制、可解释性提升及跨尺度协同等方向，以推动其实际应用。

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

Keywords

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