The energy transition opens up new opportunities for a sustainable energy supply, but at the same time poses considerable challenges for feed-in management under the Renewable Energy Sources Act (EEG). In 2023, 301 hours with negative electricity prices were recorded in Germany, which is due to an oversupply of electricity. This situation arises in particular when there is a high level of wind and solar power production without corresponding off-take.
Thanks to its ability to process data and recognise patterns, artificial intelligence (AI) plays a key role in feed-in management in accordance with the EEG. Various AI developments and use cases in the energy sector are currently being implemented to help optimise the feed-in management of photovoltaics, wind energy and hydropower.
Potential of artificial intelligence in feed-in management
Renewable energies in particular, whose generation is highly weather-dependent and variable, require new approaches to prevent grid congestion. A study from 2022 shows that around 8.1 terawatt hours of electricity from renewable energies were curtailed due to grid bottlenecks.
Together with redispatch, these cost-intensive measures are part of congestion management, which is necessary to ensure grid stability. The corresponding regional wind power and PV shutdowns and compensation particularly affect small and medium-sized renewable energy plants. In addition, plants under the Combined Heat and Power Act (KWKG) are also affected.
As a result, artificial intelligence is becoming increasingly important in the energy sector. AI systems can intelligently factor in historical data and current weather forecasts in order to recognise grid bottlenecks at an early stage. Using automated, self-learning algorithms, AI can be used in the energy industry to process extensive real-time data and optimise the feed-in management of photovoltaics, wind energy and other renewable energies.
The symbiosis of AI and climate: AI-supported forecasts for solar and wind energy
AI applications can be used for various renewable energy systems to accurately forecast energy feed-in and optimise the use of available energy:
1. Feed-in management for PV electricity:
AI models can perform detailed analyses of atmospheric conditions to produce more accurate forecasts of cloud formation. In addition, sensor data collected by 5G-based drones can improve the accuracy of forecasts for solar installations.
One example of this is the use of AI to analyse fine grains of sand and ash particles in the atmosphere, which can influence solar radiation. These precise forecasts enable more efficient planning of solar power production and help to avoid grid bottlenecks.
2. Feed-in management for wind energy:
The accuracy of wind power production can be significantly improved by AI. AI systems analyse historical wind data together with current weather conditions to make more accurate predictions for wind power generation.
In regions where the wind often changes direction, such precise forecasts can be particularly advantageous. This enables better feed-in management of wind energy and minimises the risk of grid overloads.
3. Other renewable energies:
AI can also help to optimise the generation and feed-in of other renewable energies such as biomass or hydropower by creating accurate forecasts and improving the management of energy flows.
For example, AI can predict the amount of water in reservoirs and its impact on energy production, which leads to better planning and utilisation of hydropower.
Greener prospects: Future trends in AI and climate
The future of feed-in management will be strongly characterised by the integration of artificial intelligence and the consideration of climatic factors. AI can be used to make energy systems more efficient and flexible. AI algorithms analyse weather data and consumption patterns in order to optimise the feed-in of renewable energies such as solar and wind power. This leads to a more reliable energy supply and minimises dependence on fossil fuels.
Another advantage of AI in the energy sector is the prediction of grid loads. This allows operators to react to potential bottlenecks at an early stage and take appropriate measures. In addition, reliable end-to-end connectivity solutions enable the use of AI-supported smart grid applications. Overall, AI-supported solutions offer promising prospects for a more sustainable and efficient energy future.
