The challenge of decarbonisation is becoming increasingly urgent in a world facing climate change. The adoption of renewable energies is one of the main solutions to reduce greenhouse gas emissions and improve global sustainability. THowever, intermittent production of energy from renewable sources such as solar and wind is a significant barrier to the efficiency of the energy system. In this scenario, artificial intelligence (AI) emerges as a fundamental resource, able to optimize the management of renewable energies, improve the reliability of networks and promote the integration of renewables into the global energy mix.
According to the Digitalization & Decarbonization Report 2024, prepared by Energy&Strategy of the School of Management of the Politecnico di Milano, artificial intelligence can play a crucial role in the process of decarbonisation. The report highlights how AI can be used in three key areas of the energy sector: Generation Forecast, Grid Stability and Demand Response. These three areas represent the areas where AI can concretely support renewable energy management and improve the overall performance of the energy system.
Artificial intelligence as an accelerator for decarbonisation
The link between artificial intelligence and decarbonisation has been widely debated. On the one hand, AI is seen as a key accelerator for achieving sustainability goals, due to its ability to optimize energy resource management, reduce waste and improve consumption and production forecasts. On the other hand, there are concerns about the environmental costs of increasing energy consumption related to the use of artificial intelligence itself. In fact, like any other technology, AI also involves consumption, which in some cases could reduce its positive impact on carbon emissions.
Despite these concerns, the potential of artificial intelligence in decarbonization is evident. At the European level, it is estimated that the introduction of digital solutions and AI can directly contribute to a reduction in emissions of 18%, with an indirect impact of around 35%. This means that, if properly implemented, digital and AI can play a key role in achieving sustainability goals, helping to reduce more than 50% of global emissions.
The three strategic areas of artificial intelligence in renewable energy management
The Digitalization & Decarbonization Report highlights three key areas where AI can support renewable energy management: Generation Forecast, Grid Stability and Demand Response. Let us look in more detail at how AI works in these areas.
1. Generation Forecast: Improving forecasts for renewable energy generation
One of the main obstacles in managing renewable energies is their intermittent nature. Solar and wind energy production is highly variable and dependent on weather conditions, making it difficult to predict precisely when and how much energy will be produced. Artificial intelligence, through advanced algorithms, can significantly improve the accuracy of energy generation forecasts.
In particular, ensemble algorithms (which combine the results of different models to improve robustness) have proved particularly effective in predicting solar and wind energy production. These models are able to distinguish between structural events, such as an exceptionally sunny or windy period of time, and contingent events, such as a sudden change in weather. Thanks to these advances, the forecast of renewable generation could improve by up to 30%, with positive effects on the entire energy system.
2. Grid Stability: stabilization of electricity networks
Another significant challenge related to the adoption of renewable energies is the management of electricity grid stability. Traditional grids are designed to operate on constant energy sources, such as fossil fuels. But when variable renewables come into play, imbalances between energy production and demand can occur, jeopardising the stability of the grid.
Artificial intelligence can play a crucial role in improving the stability of electricity grids. Through the use of machine learning algorithms, it is possible to monitor in real time the network variables, predicting and correcting any imbalances. For example, AI can handle instability phenomena such as small-signal stability (stability to small signal fluctuations) and voltage stability (stability of voltage), ensuring a continuous and safe operation of the networks.
For more critical events, such as sudden network fluctuations or changes in consumption loads, AI can adopt advanced models that incorporate the time dimension, thus improving the network’s ability to respond to unforeseen events. The use of sequential algorithms allows to manage the stability of the network even in dynamic situations, such as those caused by sudden changes in weather conditions or energy consumption.
3. Demand Response: Optimization of energy consumption
The concept of demand response refers to the ability to adapt energy demand to the availability of renewable production. The idea is to shift energy consumption to match solar and wind production better, reducing the need for non-renewable energy sources such as fossil fuels.
Artificial intelligence can make a significant contribution in this area, by analysing energy consumption patterns and optimising resource use. Using machine learning techniques, AI can predict peak demand and suggest changes in consumer behavior. For example, it can be used to manage the load on homes and industries by optimising the timing of energy use so that consumption is aligned with renewable energy production.
The impact of EU policies and regulations on AI
Artificial intelligence not only benefits from decarbonisation policies, but can also help to promote more sustainable solutions through a sound regulatory framework. The European Green Deal and other regulations, such as the Data Act, the Chip Act and the AI Act, have been designed to ensure that technological innovation contributes to sustainability while regulating the use of AI in a responsible and safe way.
The EU is implementing policies that promote digital governance for sustainability, with measures to encourage the deployment of smart technologies such as AI, which can optimise energy efficiency and reduce emissions. However, it is essential that regulations evolve to take account of continuing technological developments, ensuring that emerging technologies are used in a way that maximises environmental benefits and reduces the risks associated with growing consumption.
The role of public authorities and companies
Public administrations and businesses play a key role in the implementation of digital technologies for decarbonisation. At the urban level, the adoption of intelligent solutions based on AI, IoT and digital twins is becoming more widespread. These technologies enable more efficient management of resources, from public lighting to waste management and water control.
In addition, many companies, especially those operating within the ESG (Environmental, Social, and Governance) framework, are integrating advanced technological solutions to improve their environmental performance. The introduction of AI, IoT and digital twins has led to a significant increase in technology projects, especially in the areas of operations and human resources. However, despite the adoption of these technologies, more attention needs to be paid to measuring environmental impact in order to ensure that benefits are indeed quantifiable and measurable.
Conclusions: AI as the foundation of the energy transition
Artificial intelligence is proving to be a key resource for renewable energy management and achieving decarbonization targets. Its application in areas such as energy generation forecasting, grid stability and demand management is a practical solution to optimise the entire energy system. With its predictive and adaptive capabilities, AI can make a substantial contribution to the energy transition by making renewable sources more reliable and easier to integrate into existing energy systems.
However, for these benefits to be fully realised, an integrated approach including appropriate public policies is needed, Governance of digital technologies for sustainability and continuous commitment by companies to measure and report on the environmental benefits achieved. Only with a collective commitment and long-term vision will it be possible to fully exploit the potential of artificial intelligence in decarbonisation and creating a more sustainable and resilient energy system.
