The demands placed on the electricity grid have changed across all grid levels due to the energy transition. An important basis for a sustainable energy future is the modernisation of the transmission grid. This doesn’t just involve expanding or renovating the infrastructure. Digitalisation offers the electricity sector new opportunities, such as through artificial intelligence (AI). A sub-field of information technology, it simulates intelligent human behaviour in the form of software applications on computers. At the same time, AI incorporates machine learning: the applications build knowledge through experience and then apply this knowledge. This allows them to respond to unknown scenarios.
The algorithm warns of weaknesses
The electricity sector can benefit from the opportunities provided by AI. They include proactively identifying weaknesses in the grid infrastructure, among other things. This type of maintenance technology is called predictive maintenance. It involves recording metering data from current grid operation, grid infrastructure master data and geoinformation or weather data in real time and transmitting it to a central database. A self-learning algorithm evaluates the acquired information and derives maintenance recommendations. In an optimum case, this allows weaknesses in the infrastructure to be predicted before they lead to negative effects or failures. As automated decision-making tools, AI helps the grid operator increase the reliability of the grid. At the same time, these kinds of software applications can improve the advance planning of grid construction measures in the future and result in their more targeted and effective implementation.
Managing renewable energies
The combination of infrastructure data and external data would also allow AI to better predict the production of energy. This is important for renewable energies whose production capacity fluctuates depending on the weather. Predicting the potential output based on current and historical data helps those responsible to better plan the operation of their systems. Predictability also has a positive impact on the electricity grid. The data from important energy producers would make it possible to improve grid operations planning.
In future, AI could actively intervene in managing the demand for electricity. When corresponding market signals are received, the targeted disconnection and connection of loads is used to respond to overly low or overly high demand. These kinds of interventions are possible in industries where production processes can be controlled, which allows the electricity input to be varied. This kind of load management can also help to compensate the fluctuating electricity generation of renewable energy. AI enables the associated processes to be further automated. In the area of demand management, work is also taking place on intelligent platforms which use incentives to influence the energy efficiency of end consumers. AI could also contribute to more stable electricity grids by automatically feeding the energy produced by thousands of households into the grid.