用扩散模型提升微电网调度的碳与风险感知能力，降低成本30%以上
Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
- 将扩散模型融入强化学习，通过去噪过程生成更优调度动作分布
- 相比传统算法成本降低2.3%-30.1%，碳排放减少28.7%，性能更稳定
- 适合关注低碳、高鲁棒性的智能微电网系统设计与部署
本文提出DiffCarl，一种基于扩散模型的碳与风险感知强化学习算法，用于多微电网系统的智能运行优化。随着可再生能源大规模接入和系统复杂性上升，微电网社区面临实时能源调度与不确定性下的优化挑战。DiffCarl将扩散模型引入深度强化学习框架，通过去噪生成过程学习动作分布，增强策略表达能力，实现对碳排放与运营风险的显式建模。实验表明，其相比经典算法与先进DRL方法，运营成本降低2.3%-30.1%，碳排放比无碳感知版本降低28.7%，性能波动显著减少。该方法设计灵活，可高效适配不同系统配置与目标，具备面向实际能源系统部署的潜力。
原文摘要 · Abstract (English)
This paper introduces DiffCarl, a diffusion-modeled carbon- and risk-aware reinforcement learning algorithm for intelligent operation of multi-microgrid systems. With the growing integration of renewables and increasing system complexity, microgrid communities face significant challenges in real-time energy scheduling and optimization under uncertainty. DiffCarl integrates a diffusion model into a deep reinforcement learning (DRL) framework to enable adaptive energy scheduling under uncertainty and explicitly account for carbon emissions and operational risk. By learning action distributions through a denoising generation process, DiffCarl enhances DRL policy expressiveness and enables carbon- and risk-aware scheduling in dynamic and uncertain microgrid environments. Extensive experimental studies demonstrate that it outperforms classic algorithms and state-of-the-art DRL solutions, with 2.3-30.1% lower operational cost. It also achieves 28.7% lower carbon emissions than those of its carbon-unaware variant and reduces performance variability. These results highlight DiffCarl as a practical and forward-looking solution. Its flexible design allows efficient adaptation to different system configurations and objectives to support real-world deployment in evolving energy systems.
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