In my keynote "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference, I discussed the role and potential of self-organization in the smart grid. Since power systems are critical infrastructure, applying a distributed, bottom-up control structure is a delicate step. On the one hand, self-organizing systems provide very attractive properties like scalability, adaptability and robustness. On the other hand, due to non-linear interactions in the system, small changes that appear insignificant can have severe consequences. To ensure secure operation, this is not the most-wanted property. However, sometimes one gets such systems without a choice. An example is the effect of the unbundling process of power generation and system operation to create a free energy market - a decision that was taken to support distributed on-site power generation with renewable energy sources. At the UCTE grid, the largest synchronous grid in the world in terms of generation capacity, the decreased the scope of control for the Transmission System Operators (TSOs) due to the liberalization of the power market has caused the TSOs to develop a new way of understanding of the system in order to carefully and knowledgeable interact with the system. So we got a self-organization system without explicitly designing the system for being one. Another possibility where self-organization can explicitly support the smart grid is the utilization of smart meters in a way that supports the utilities while still keeping control and private information locally at the customer's side.
To enable self-organization for the smart grid, it is necessary to provide models, proofs, case studies, etc. showing that self-organizing approaches work. For finding self-organizing algorithms we provide our part: with the open-source software tool FREVO, we can evolve agent behavior for self-organizing systems using an automated process based on artificial evolution.
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