Green ammonia, a solution for renewable energy storage and transportation
Sep 13, 2024
Sustainable
The European Commission defines Artificial Intelligence (AI) as software systems created by people that, when faced with a complex objective, operate in the physical or digital realm by perceiving their environment, acquiring and interpreting structured or unstructured data, reasoning on the knowledge, processing that information, and deciding on the best actions to achieve their goal.
AI is a key component of the fourth industrial revolution, and as in other sectors, it is already being applied in the energy industry. Let’s explore some aspects of Artificial Intelligence and how it can help meet Europe’s decarbonisation goals.
Not only can it be used, but it already is, and its role will be crucial in achieving a more sustainable future. We are currently in a period where energy systems are becoming increasingly complex as demand grows and decarbonisation efforts intensify.
AI’s role will be crucial in achieving a more sustainable future
In such a data-rich field, there is a growing need for enhanced information sharing and more powerful tools, such as those offered by AI, to plan and operate evolving energy systems.
This need arises just as AI capabilities are advancing rapidly. Since 2010, as machine learning models have become more sophisticated, the computational power required to develop them has doubled approximately every five to six months.
Moreover, AI helps to simplify processes aimed at improving energy efficiency and facilitating the transition to renewable energy.
Artificial Intelligence, along with the use of robots and drones, enables faster, safer, and more efficient operations and inspections in the environments where infrastructures are located.
Moreover, innovative technologies such as AI, Building Information Modelling (BIM), and digital twins make the operation of infrastructures significantly more efficient.
Digital twins are also very useful for creating simulations of infrastructures, allowing for performance optimisation through detailed scenarios and analyses in virtual environments.
AI algorithms can also very accurately predict the likelihood of incidents occurring in installations, making it easier to implement preventive measures before problems arise. In the event of a failure, AI analyses the causes, severity, and potential consequences, quickly determining the necessary actions to be taken. This improves safety and reliability while also reducing maintenance costs.
AI is indeed useful for optimising operations and improving decision-making processes. For instance, AI algorithms are used to predict gas demand and consequently adjust transportation and storage operations. Additionally, AI helps to identify patterns in energy consumption that can lead to opportunities for implementing more sustainable practices.
In other domains, such as the production and supply of various energy products, the application of AI is already a reality. This is based on a fully connected Industry 4.0, with real-time data processing capabilities, which allows for maximising production and distribution capacity, and continually improving efficiency.
AI algorithms analyse data from renewable energy sources such as solar panels and wind turbines, predict energy generation, adjust demand, and improve system performance. By incorporating factors such as weather patterns and historical data, AI delivers more accurate forecasts, ensuring the efficient utilisation of renewable resources.
Artificial intelligence is a valuable asset for developing a hydrogen economy.
Artificial intelligence is a valuable asset for developing a hydrogen economy
AI can help predict renewable energy production and adjust demand accordingly. In the case of hydrogen, which is produced by the electrolysis of water using renewable electricity, AI can improve the efficiency of this process by optimising hydrogen production and reducing costs. Additionally, AI can identify optimal locations for electrolysis plants and forecast future demand for green hydrogen.
It plays a crucial role in improving demand predictions and optimising new production sites.
AI will be particularly valuable during the initial phases of feasibility analysis and conceptualisation for projects like H2Med and the Spanish Hydrogen Backbone, on which Enagás is working. It provides essential data for investment decisions and supports economic, technical, and environmental analyses through simulations.
AI-powered energy management systems have led to significant advancements in improving efficiency in both buildings and industries. Machine learning algorithms detect patterns and anomalies in energy usage, resulting in enhanced energy efficiency and substantial energy savings.