Advances in reinforcement learning for enhancing scheduling of hydrogen-integrated energy systems

Hydrogen-Integrated Energy Systems have emerged as a critical architecture for deep decarbonization, yet their operational complexity — characterized by nonlinear electrochemical dynamics, profound stochasticity, and rigid physical safety constraints — renders traditional model-based optimization increasingly insufficient. This paper presents a comprehensive review of Reinforcement Learning applications in H-IES scheduling, underpinned by a novel lifecycle-oriented framework. This framework systematically aligns the hydrogen value chain from production and storage to multi-energy coordination with RL research. As the study’s primary contribution, it serves as a unifying architecture that maps fragmented literature to specific operational stages and engineering mandates, establishing a coherent roadmap for researchers and practitioners. We systematically evaluate the performance of various RL algorithms, distinguishing between model-free approaches for economic optimization and hybrid frameworks designed to enforce physical safety constraints. Notably, while deep reinforcement learning algorithms evolve rapidly, the Markov Decision Process formulation and the lifecycle-stage requirements they encode are more durable than any specific algorithmic choice, especially given the slower implementation cycle of real H-IES assets. Furthermore, this review critically identifies the key engineering barriers hindering real-world deployment, including data scarcity, the simulation-to-reality gap, and the lack of interpretability. Finally, this review articulates prospective research directions, highlighting the potential of emerging technologies such as Large Language Models, Meta-Reinforcement Learning, and Digital Twins to evolve RL from a theoretical tool into a robust engine for the intelligent management of the next-generation hydrogen economy.