03 June 2025
The integration of grid automation into global power grids has brought major advancements in both reliability and efficiency. Self-fixing grid systems, driven by artificial intelligence, now make it possible to detect and address faults in real-time, which helps reduce outage timing and disruptions across networks. Grid automation through automated load balancing has also become a crucial enabler in allowing electricity to be distributed more effectively, reducing congestion, and enhancing overall power quality.
With artificial intelligence at the core of these grid automation systems, energy networks can now adapt dynamically to demand fluctuations, ensuring a more stable and responsive supply. At the COP28 climate summit in Dubai, more than 130 national governments pledged to triple global renewable energy capacity, aiming to hit at least 11,000 GW by 2030. This global move is in line with the Paris Agreement’s objective to keep the global temperature rise under 1.5°C. Projections show that by 2028, renewable sources will generate 42% of global electricity, with wind and solar PV together making up 25% of that share. Among these, solar is expected to contribute 50% and wind 26%, underscoring their growing dominance over other renewable technologies.
When it comes to frequency stability, the role of Battery Energy Storage Systems (BESS) stands out. The system demonstrates a notable 0.59% improvement in frequency nadir, reaches a settling frequency of 49.87 Hz, and shows a reduced Rate of Change of Frequency (RoCoF) at 0.117 Hz/s. These performance indicators highlight how BESS strengthens the grid’s dynamic response and its ability to withstand transient disturbances.
An optimized approach to sizing BESS capacity further adds value. By moving beyond just frequency sensitivity, the energy capacity required is reduced from 11.70 MWh to 8.77 MWh. This refined method results in significant cost savings, reinforcing the financial feasibility of the solution.
Strategic placement of BESS is another critical factor. The proposed framework identifies the most effective locations by considering frequency response, voltage stability, and reactive power requirements. This targeted strategy ensures that localized issues are tackled efficiently, leading to enhanced overall grid stability.
For next-gen energy systems like microgrids and virtual power plants, real-time monitoring and control remain essential. AI technologies show strong promise in boosting how these systems are monitored, managed, and optimized. With the increasing adoption of distributed and renewable energy sources and a global push toward net-zero emissions, grid automation through AI integration is set to play a pivotal role in building smarter, more resilient energy infrastructures.
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