A Decision-making Framework Based on Artificial Neural Networks and Intelligent Agents for Transmission Grid Operation

Security margins have been reduced in restructured and deregulated power systems, and as a result, these systems have been operated close to their security limits. Therefore, it is of utmost importance that power system operation be tracked in a real-time fashion, making decisions as fast as possible to ensure operating points within security limits. In this context, this article proposes a practical decision-making framework for transmission grid operation featuring artificial neural networks and intelligent agents. In this framework, the system operating point is tracked by means of voltage stability margins estimated by artificial neural networks,while the decision-making process is supported by means of intelligent agents. The output of this framework is a qualitative answer that supports system operators in making decisions to enhance security margins. A 6-bus test-system and the CIGRE 32-bus system were used for validating the neural network approach for voltage stability margin estimations; the proposed framework was validated with the IEEE 300-bus system. Results show that such a framework can be readily applied to support decisions aimed at ensuring secure system operating points.