What are the main challenges facing today’s power grids, and how can stability be improved in the face of growing complexity and renewable integration?
The biggest challenge we’re facing today is volatility. With more renewables in the mix – like wind and solar – the grid becomes more unpredictable. Traditional control mechanisms aren’t fast or flexible enough to cope with fluctuations. we’re also seeing a shift toward real-time monitoring systems, often combined with decentralized control approaches. These systems allow operators to react instantly and maintain grid stability by actively utilizing available flexibilities—such as demand response, battery storage, and distributed generation. It’s a step toward a more proactive energy infrastructure—one that doesn't just react to problems, but anticipates and prevents them.
How can decentralised and autonomous grid control help manage increasing demand and intermittent renewable energy generation?
Decentralisation is a more agile way to manage the grid, and it’s crucial for ensuring that we can keep up with rising demand while maintaining reliability. When every node in the grid can make decisions based on real-time data, the entire system becomes more resilient and adaptive. This is especially important as renewable generation can vary not only daily but minute by minute.
Using AI and digital twins, we can simulate grid scenarios in real time and adjust energy distribution accordingly. These technologies enable dynamic reconfiguration of grid segments and localized balancing of supply and demand. For example, if there’s a sudden drop in solar output in one region, the system can automatically draw power from storage or reroute energy to compensate – without waiting for central instructions. However, these responses are inherently limited by the underlying grid topology and infrastructure constraints. The effectiveness of such actions depends on regional connectivity, transmission capacity, and how flexibilities are distributed across the network.
What role does flexibility management play in integrating renewables efficiently and maintaining a stable grid?
Scalable flexibility management is what allows us to shift from a centralized, one-way grid to a dynamic, multi-directional energy network. The idea is to not just generate energy flexibly, but also to consume it intelligently.
By using AI-driven load forecasting and demand-side management, we can align consumption with availability. For instance, if wind production is expected to peak in the evening, large industrial consumers can schedule operations accordingly. In smart grids household energy usage can be adjusted – turning on or off systems like heating, cooling, or adaptation of the electric vehicle charging rate.
This ability to respond to changing conditions dynamically is what allows us to integrate more renewable energy without risking overloads or inefficiencies. The grid becomes a living system – constantly adjusting to keep everything in balance.
Can you describe how AI, digital twins and data integration strengthen the resilience of our energy infrastructure?
The combination of AI and digital twins is a game-changer. Think of a digital twin as a virtual replica of the grid that behaves exactly like the real one. When we feed it real-world data from sensors and meters, it allows us to simulate scenarios, test responses, and make predictive adjustments – before anything goes wrong.
Here, AI-based fault detection on basis of a digital twin comes into play. By analyzing monitored fault signatures with machine learning, we can identify the location of a fault with high precision. This means operators no longer need to rely on broad, potentially dangerous switching operations for localisation that adds risk triggering cascading failures. In the case of temporary faults, this detailed analysis also helps uncover weak spots or early indicators of potential future issues, allowing for preventative maintenance and better long-term planning.
This improves both fault mitigation and system planning. If a potential issue is spotted – like a region approaching capacity or an anomaly in voltage – we can model the impact and deploy solutions in advance. It also helps us plan for larger systemic risks, like cyberattacks or equipment failures, by testing resilience strategies in a virtual environment.
Looking ahead, what do you see as the future of energy infrastructure and smart grid technologies?
The future is autonomous, adaptive, and deeply data-driven. I believe we’re moving toward an energy system where every component—from solar panels to substations—operates with embedded intelligence. AI will handle not just optimization but also security, anomaly detection, and self-repairing protocols.
We’ll also see tighter integration between sectors: buildings, mobility, and industry will interact with the grid as active participants. For example, electric vehicles will serve as mobile storage units, feeding energy back when the grid needs it.
And ultimately, energy systems will become more decentralized. Communities and even individual households will produce, store, and trade energy locally. Technology will make it possible — but it is the collaboration between research, industry, politics and society that will really drive the transition.
