The development of critical green hydrogen infrastructure requires balancing efficiency and resilience within an increasingly complex energy system. While the centralised model optimises costs and scale, it introduces systemic vulnerabilities; the distributed model enhances resilience but brings greater operational complexity. In this context, the hybrid model emerges as the most robust approach, with artificial intelligence (AI) playing a key role in anticipating risks, optimising operations and strengthening security.
Introduction
The energy transition based on green hydrogen involves not only production capacity, but also the creation of critical infrastructure capable of ensuring security of supply, operational continuity and adaptability to disruptions. The central question is clear: how should a resilient system be designed — centralised, distributed or hybrid?
Centralised Model
The centralised model is based on large industrial hubs, often located in areas with high availability of renewable energy and strong logistical capacity. This approach enables cost reduction and production scaling, facilitating industrial integration and export. However, the concentration of assets creates specific points of failure, making the system vulnerable to technical incidents, extreme weather events and external threats. Dependence on intermediary infrastructure, such as power grids and transport systems, increases the risk of cascading failures.
Distributed Model
The distributed model relies on decentralised production units located close to consumption. This approach reduces logistical dependency and strengthens resilience through redundancy and risk decentralisation. However, the multiplicity of nodes increases operational complexity, makes coordination more difficult and expands cybersecurity challenges. In addition, each unit may be individually less robust compared to large centralised infrastructure.
Hybrid Model
The hybrid model combines centralised hub with distributed networks, enabling a balance between efficiency and flexibility. This approach reduces dependence on critical points and ensures service continuity even in scenarios of partial failure. In this context, resilience ceases to be an isolated characteristic and becomes an emergent property of the system, based on redundancy, diversity and adaptability.
The Role of Artificial Intelligence in Energy Resilience
The complexity of hybrid systems requires new management approaches. AI plays a central role by enabling:
Failure anticipation and predictive maintenance: Advanced models identify risks before they occur, reducing operational failures.
Simulation and dynamic planning: Digital twins enable the testing of complex scenarios and the optimisation of strategic infrastructure decisions.
Real-time optimisation: Intelligent systems continuously adjust production, storage and distribution based on energy availability and demand.
Cyber resilience: AI detects anomalous behaviour and anticipates cyberattacks, strengthening the security of distributed systems.
Conclusion
The future of green hydrogen depends on the ability to design infrastructure that is resilient by design. The hybrid model, supported by AI, provides the most solid foundation to ensure efficiency, security and adaptability. More than physical infrastructure, it will be the ability to integrate intelligence, anticipate risks and continuously evolve that will determine the sustainability and viability of a hydrogen-based energy system.
