How Is Digital Technology Transforming the Energy Sector?
10 min read
Today’s energy systems generate a huge amount of data. Combined with artificial intelligence (AI), this data makes it possible to better forecast consumption as well as the output of wind and solar plants, optimize grid operations, and predict potential failures. However, this digital transformation raises new challenges, ranging from cybersecurity to digital sufficiency. A useful resource to understand how data, networks, and algorithms now play a key role in managing modern energy systems.
© Li Zhipeng / Xinhua via afp - In Hohhot, China, a supervision center manages data centers powered by renewable energy.
Data: The New Resource for Energy Systems
Today, production, transmission, and distribution of energy involve a wide range of equipment capable of reporting their status in real time. Wind turbines, solar panels, batteries, substations, and industrial facilities are equipped with sensors that continuously record information about their operation.
This data makes it possible to monitor equipment performance, detect certain anomalies, and assist in managing the facilities. In modern energy systems, information has become a resource almost as important as energy itself.
This trend is particularly evident in electric power grids. Historically designed to transmit electricity, they are now also capable of transmitting data used to better understand energy usage and optimize the operation of the infrastructure.
Forecasting Energy Production and Consumption Using Data
One of the most promising applications of digital technology is forecasting. In an electric power system, it is essential to anticipate both electricity consumption and production to maintain a constant balance between supply and demand.
AI now makes it possible to analyze very large volumes of data: consumption history, weather forecasts, grid status, and output from facilities. This information can be used to improve forecasts and facilitate the integration of renewable energy, whose output varies depending on weather conditions. The identifies forecasting and optimization of power systems as one of the main areas of application for generative AI in the energy sector.
A frequently cited example is that of Google and DeepMind. In the United States, their algorithms have been applied to approximately 700 MW of wind power capacity. By combining weather forecasts and historical turbine data, the system can predict generation up to 36 hours in advance. According to DeepMind, this improved predictability has increased the value of the electricity generated by about 20 percent, thanks to better planning of electricity into the grid.
Using Predictive Maintenance to Anticipate Equipment Failures
In many industrial sectors, data is also used to monitor the condition of equipment.
Traditionally, maintenance was performed at regular intervals or after an equipment failure. Today, sensors make it possible to continuously monitor indicators such as temperature, vibrations, or machine performance. When unusual behavior is detected, maintenance can be scheduled before a failure occurs.
This approach, known as predictive maintenance, is particularly valuable in the energy sector. In wind farms, for example, analyzing data from supervisory control and data acquisition (SCADA) systems helps identify certain early warning signs of wear and tear or malfunction. Scientific research on the subject also highlights that maintenance accounts for a significant portion of wind turbine operating costs, which explains the growing interest in data analysis and machine learning tools.
The goal is simple: to minimize unplanned downtime and perform maintenance at the most appropriate time.
Smart Grids: Making Power Networks More Intelligent
The growth of renewable energy is gradually transforming how electric grids operate.
For a long time, grids were designed primarily to transmit electricity from large power plants to consumers. Today, consumers can generate, store, and consume their own electricity. This is the case, for example, with a household equipped with solar panels and a home battery.
To manage these more complex exchanges, grid operators are developing smart grids. Using sensors, digital tools, and communication systems, these grids enable better control of electricity flows and greater integration of renewable energy. The IEA emphasizes that generative IA could also help improve the operation of existing grids and facilitate the integration of renewable energy sources into power systems.
In these grids, data flows continuously between equipment, operators, and users. Once analyzed, this data provides the information needed to control the power system in real time.
Cybersecurity: Protecting Critical Infrastructure
Digital transformation brings with it a new challenge: cybersecurity.
Electric grids, industrial facilities, and control systems now rely on constant data exchanges. This interconnectivity creates new vulnerabilities. An intrusion attempt, malware, or unauthorized access could disrupt the operation of critical infrastructure.
Energy operators are therefore implementing numerous protective measures: access control, network monitoring, anomaly detection, and IT system segmentation.
The smarter the infrastructure becomes, the more its security becomes a strategic issue.
Digital Technology Helps Save Energy… but it Also Consumes it
Digital technologies can help improve the efficiency of energy systems. They enable better forecasting, optimize grid operations, and reduce certain losses.
But these tools themselves come at an energy cost. Data centers, communication networks, digital devices, and generative AI systems consume electricity to operate.
Today, the digital sector accounts for about 4% of global electricity consumption. In France, it accounts for about 11% of national electricity consumption.
This reality raises the question of digital sufficiency: how can we benefit from digital technologies while limiting their energy consumption and ?
As the IEA points out, AI could help improve the efficiency of energy systems, but its development also comes with an increase in computing power and electricity needs.
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