The electricity sector is shifting towards an increasing integration of renewable energy sources. Despite their advantages in reducing emissions, they give rise to problems associated with power grid security. Distributed Static Series Compensators (DSSC) may play an important role in improving the security of the operations of transmission systems, through corrective control actions aimed at relieving network congestions in real-time.

The resulting Optimal Power Flow model (OPF) is a complex mixed-integer non-linear optimization problem (MINLP) combining different objective functions. The problem needs to be solved in real-time to determine the corrective actions within an acceptable timeframe.

A hybrid methodology combining Artelys Knitro for solving non-linear programming problems (NLP) and a k-means clustering algorithm allows to generate close-to-optimal control actions in a reasonable computational time for realistically large systems.