POWER Powering Germany’s


next-generation grid Why AI-driven grid optimisation is vital for improving the country’s grid


By Clifford Ondieki, power systems specialist, collaborator of CWIEME Berlin


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ccording to the energy think tank Ember, 59 per cent of Germany’s electricity was generated from clean sources in 2025, with wind and solar alone accounting for 45 per cent. This seismic shift from centralised fossil-based generation to decentralised and weather-dependent renewables  modern grid management. In this changing landscape, AI-driven grid management is essential.


According to the German wind energy association, Bundesverband WindEnergie  saw the highest number of offshore wind turbines commissioned since 2017. It’s easy to underestimate how this move toward decentralised sources rewrites the rules of grid operation. Legacy power systems were designed around a small number of large, controllable generators, delivering predictable output. Today, Germany’s grid must instead coordinate millions of decentralised assets, from onshore wind farms to rooftop solar, batteries and EVs – all operating with different levels of intermittency. Without effective management, this  higher system costs and increased pressure on grid stability. Congestion management, balancing reserves and asset maintenance are no longer challenges that manual intervention alone can address. They require real-time insight, predictive capability and adaptive control – areas where AI and machine learning are beginning to play a decisive role in modern grid operations.


The trouble with traditional grid management


Even as grids become more decentralised, electricity demand continues to accelerate.  industry is increasing consumption, while  AI-driven services has created entirely new layers of load.


This has created a catch-22 for grid planners: how do we balance the needs of rapidly growing demand and new technologies with the infrastructure already in place, much of which was never designed for today’s operating conditions? Traditional management relied on  generators, guided by historical data and  directions and renewable generation 


12 APRIL 2026 | ELECTRONICS FOR ENGINEERS


demand can spike unpredictably. Manual approaches alone can no longer keep up. Here’s the important caveat. The challenge isn’t just adding infrastructure – it’s about better using the information we already have and adapting operations in real time under growing system uncertainty.


  transforming how grids are operated. For  storage, consumption and network sensors, AI can forecast renewable output, anticipate spikes in demand and identify where congestion is likely to occur. The German Government has recently tried to address the latter issue, proposing a bill enabling renewable energy developers to pay for connecting to the grid, replacing the current 


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