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

TL;DR

This paper introduces MasCOR, a machine-learning framework that efficiently co-optimizes e-fuel system design and operation under renewable uncertainty, outperforming traditional methods in speed and adaptability.

Contribution

The paper presents MasCOR, a novel ML-assisted co-optimization framework that generalizes across configurations, enabling rapid, near-optimal design and operational decisions for e-fuel systems.

Findings

MasCOR achieves near-optimal performance with lower computational costs.

Most European sites benefit from reduced system loads for cost-effective, carbon-neutral e-methanol production.

Site-specific strategies vary, with some sites favoring larger systems and storage for market opportunities.

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.