A Guided Safe Reinforcement Learning Framework for Adaptive Energy Management in Green Hydrogen Production Systems

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🔬研究者:Presents a novel safe RL architecture with self-reducing shield intervention, advancing the intersection of reinforcement learning and energy system optimization.

🏢実務担当者:Offers a practical framework for adaptive control of electrolyzers coupled with renewables, potentially improving operational efficiency and reducing costs in green hydrogen plants.

The rapid expansion of variable renewable energy (VRE) presents both opportunities and challenges for sustainable energy systems. Green hydrogen is emerging as a key solution for large-scale storage and decarbonization of hard-to-electrify sectors. However, direct coupling of electrolyzers to VRE introduces operational volatility, accelerated degradation, and complex dispatch challenges that conventional rule-based and optimization-based methods cannot fully address. This paper proposes a Guided Safe Reinforcement Learning framework for adaptive energy management in green hydrogen systems. The method integrates Soft Actor-Critic (SAC) with a deterministic two-branch safety shield that ensures feasibility at execution time by construction, regardless of the action proposed by the policy, guaranteeing that battery state-of-charge limits and electrolyzer operating mode constraints are satisfied at every timestep. The framework further incorporates an intervention-aware corrective reward signal which converts each shield event into a structured learning signal and a Self-Guided Memory (SGM) mechanism that replays shield-corrected feasible transitions to bias the policy toward constraint-satisfying regions, causing shield intervention frequency to self-reduce as the policy improves. The framework is validated on a HOMER-sized green hydrogen system using year-long renewable profiles of Purros area in Namibia. Under identical safety enforcement and across four independent random seeds, the proposed guided agent achieves a 3.5% increase in hydrogen production (p = 0.003) and a 29.2% reduction in electrolyzer cycling cost (p = 0.007) relative to a shielded SAC baseline, with additional reductions in renewable curtailment (12.9%) and battery degradation cost (12.1%). Structural analysis further shows reduced ramp volatility, with mean electrolyzer ramp magnitude decreasing from 1.87 MW to 1.62 MW (14% reduction) and lower ramp dispersion. The guided agent also reduces the safety-shield intervention rate from 17.7% to 10.9%, indicating improved safety autonomy.

• semanticscholar https://doi.org/10.1109/access.2026.3693117first seen 2026-05-24 04:46:47 · last seen 2026-05-27 05:01:32