Integrating life cycle assessment in multi-objective optimization of green hydrogen systems: a review of literature and methodological challenges
Diego Larrahondo Chavez, Catherine Azzaro‐Pantel, Florent Montignac, Alain Ruby
Abstract
As global demand for sustainable energy solutions intensifies, reducing the environmental impact of energy systems while maintaining cost-effectiveness has become imperative, particularly for emerging energy carriers like hydrogen. This article presents a comprehensive review of life cycle assessment (LCA) and multi-objective optimization (MOO) in the context of green hydrogen (H 2 ) systems, focusing on key contributions from 2019 to 2023. The review categorizes the literature into three areas: (i) Recent LCA studies on green H 2 production, (ii) MOO studies that include LCA of renewable energy systems, (iii) Research integrating environmental criteria into MOO for green H 2 systems. The review highlights significant gaps, particularly the limited depth of environmental detail in LCAs integrated into MOO, compared to standalone environmental LCAs. Additionally, the analysis reveals a lack of end-of-life considerations across all study categories. In terms of optimization, most research adopts a bi-objective framework, typically focusing on economic performance and a single environmental metric (often CO 2 emissions), thereby constraining the development of more comprehensive, sustainable systems. The article concludes by offering recommendations: future LCAs of hydrogen systems should incorporate advanced optimization algorithms, broaden their scope to cradle-to-grave assessments, and utilize prospective methods for better anticipation of future environmental impacts. • This paper explores the use of Life Cycle Assessment (LCA) in the design of green hydrogen systems. • It reviews recent studies about Multi-Objective Optimization (MOO) including LCA for renewable energy systems. • It examines optimization with environmental criteria for hydrogen systems. • It discusses balancing detail and efficiency in LCA and optimization. • It recommends advanced decision support tools for better integration of LCA and optimization.