M. Kezunovic, J. Cremer, L. Duchesne, M. Markovic, P. Vergara Barrios
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The distributed paradigm toward more flexible operation of the electricity infrastructure may find its new foundation in the active real-time self-management of the many distributed energy resources. There, interestingly, the recent progress of developing AI-based algorithms for distributed self-organized agents part of the network can consider system stability constraints, and manage network congestion through their energy market participation. However, reinforced AI-based control may lack verifiable guarantees, transparency, and privacy, and can have inherent inaccuracies. In response, the system operators finally responsible for the reliable operation of the grid need advanced state estimation techniques using collected measurements so they can prevent instabilities. Processing these collected measurements is unfortunately not straightforward as the amount of data is increasing exponentially which is why advanced federated data processing techniques are currently in development. There, also for system operators, AI-based algorithms are very promising in the estimation of system states, the detection of active devices without intrusion, and the forecasts of some state variables such as demand or distributed renewable power injection. In this context, this panel will discuss the recently successful applications of AI methods such as multi-agent reinforcement learning, federated learning, and deep learning.
Dr. Jochen Cremer