## 6 Discussion
Hydrogen systems, including electrolysers, hydrogen storage tanks, and fuel cells, play a vital role in integrating renewable energy sources, optimising power generation, and providing backup power during emergencies. This method employs a rolling-horizon-based approach, meaning that the scheduling decisions are made in short intervals, often referred to as time steps, and updated periodically as new information becomes available. This dynamic approach allows flexibility in adapting to changing conditions, which is essential for maintaining resilience in power grids. When combined with [40]fls proactive rolling-horizon-based scheduling of hydrogen systems for resilient power grids, machine-learning techniques can further strengthen the overall resilience of the grid infrastructure. Integrating machine learning with the scheduling method allows for enhanced self-healing capabilities in power grids. Machine-learning algorithms can analyse real-time data from sensors and smart metres to detect anomalies or potential faults in the grid. These algorithms can identify abnormal behaviour and trigger proactive actions by continuously monitoring grid parameters, such as voltage levels, frequency deviations, or load variations. The machine-learning models, trained using historical data, can predict potential faults or disturbances in the power grid before they occur. The models can generate early warnings or alerts for system degradation or vulnerability by analysing patterns and trends. The scheduling algorithm can then use this information to initiate appropriate responses, such as reconfiguring the grid or activating backup systems, to mitigate the impact of potential disruptions. Resilience methods are vital in dealing with uncertainties due to the failure of cyber-physical systems. [PERSON]. [41] argues that a proper modelling tool must be used to develop such methods. The modelling tool described by [PERSON] in [41] should be able to capture the characteristics of asynchronous, synchronous, and current events in cyber-physical systems. Petri nets are another class of modelling tool for this purpose, of which many variants have been proposed over the past decades and are widely used in developing different system applications. A nominal model of a class of cyber-physical system and an uncertainty model to test the system's robustness and resilience was proposed [14]. For the nominal model, [PERSON]. [31] used discrete timed Petri nets as the cyber world models to describe the production process of a class of the cyber-physical system with different types of tasks and sets of distinct types of resources. Constructing the cyber world model of the cyber-physical system was easily performed using a bottom-up approach. The bottom-up approach starts with creating the cyber world model of each resource type and task subnet. Discrete timed Petri nets describe each resource and task subnet. Each task subnet describes the production process workflow, and each resource subnet describes the activities or operations the specific type of resources could perform. Self-healing functionality is increasingly improving the critical infrastructure resilience of countries at this very moment despite it being a very recent technological development. The research direction in this area indicates further acceleration of the functionality in private areas, especially in deploying the 5G networks. The cyber-physical self-healing technology no doubt has the potential to revolutionise systems security. All of which would not have been possible without the opportunities presented by machine-learning algorithms. ML algorithms are advancing speedily because of the collaborative nature of modern software development through the wide industry acceptance and adoption of open-source libraries and packages. This phenomenon aids the seamless translation of the core ML language to other languages that provide a variety of choices for developers.