Optimal trajectory-guided stochastic co-optimization for e-fuel system design and real-time operation

2026-03-03

AI Summary

This study addresses the challenge of jointly optimizing the design and operation of e-fuel systems under renewable energy uncertainty, a problem rendered intractable for conventional mathematical programming due to its vast combinatorial space. The authors propose MasCOR, a novel framework that uniquely integrates trajectory-guided stochastic co-optimization with reinforcement learning. By encoding both system configurations and renewable generation trends, MasCOR trains a single agent capable of generalizing across diverse designs and scenarios, enabling efficient co-optimization. Validated across four European e-methanol production sites, MasCOR achieves near-optimal performance with substantially higher computational efficiency than traditional methods. The results reveal that most sites achieve economically viable production costs of 1.0–1.2 USD/kg with capacities below 50 MW, whereas Dunkirk requires over 200 MW of capacity coupled with expanded energy storage to reach similar cost levels.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

E-fuels are promising long-term energy carriers supporting the net-zero transition. However, the large combinatorial design-operation spaces under renewable uncertainty make the use of mathematical programming impractical for co-optimizing e-fuel production systems. Here, we present MasCOR, a machine-learning-assisted co-optimization framework that learns from global operational trajectories. By encoding system design and renewable trends, a single MasCOR agent generalizes dynamic operation across diverse configurations and scenarios, substantially simplifying design-operation co-optimization under uncertainty. Benchmark comparisons against state-of-the-art reinforcement learning baselines demonstrate near-optimal performance, while computational costs are substantially lower than those of mathematical programming, enabling rapid parallel evaluation of designs within the co-optimization loop. This framework enables rapid screening of feasible design spaces together with corresponding operational policies. When applied to four potential European sites targeting e-methanol production, MasCOR shows that most locations benefit from reducing system load below 50 MW to achieve carbon-neutral methanol production, with production costs of 1.0-1.2 USD per kg. In contrast, Dunkirk (France), with limited renewable availability and high grid prices, favors system loads above 200 MW and expanded storage to exploit dynamic grid exchange and hydrogen sales to the market. These results underscore the value of the MasCOR framework for site-specific guidance from system design to real-time operation.

Problem

• e-fuel
• co-optimization
• renewable uncertainty
• system design
• real-time operation

Innovation

• co-optimization
• machine learning
• e-fuel
• stochastic optimization
• reinforcement learning