Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization

2025-07-21

AI Summary

To address the energy scheduling challenge in multi-microgrid systems—where high renewable penetration and strong uncertainty impede simultaneous achievement of low-carbon operation, robustness, and real-time responsiveness—this paper proposes a carbon- and risk-aware diffusion-enhanced deep reinforcement learning (DRL) method. Methodologically, we innovatively integrate diffusion models into DRL action distribution modeling, leveraging denoising generation to enhance policy expressiveness and environmental adaptability. We further pioneer joint modeling of carbon-emission constraints and operational risk sensitivity, enabling multi-objective optimization and explicit uncertainty characterization. Experimental results demonstrate that, compared with state-of-the-art DRL baselines, the proposed method reduces operational cost by 2.3%–30.1%, cuts carbon emissions by 28.7% relative to its non-carbon-aware variant, and significantly mitigates performance volatility—while maintaining strong generalizability and deployment flexibility.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Multiagent Systems: Multiagent Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Optimizes real-time energy scheduling in multi-microgrid systems
• Reduces carbon emissions and operational risk under uncertainty
• Enhances policy expressiveness using diffusion-modeled reinforcement learning

Innovation

• Diffusion-modeled RL for microgrid optimization
• Carbon- and risk-aware adaptive scheduling
• Denoising process enhances policy expressiveness