Releasing the Computational Bottleneck in Sustainable Grid Security Planning

Power flow analysis is a foundational tool in power system planning, yet its computational intensity often creates a bottleneck that limits the speed and scalability of automated network planning processes. As power systems grow in complexity with increasing integration of renewables, distributed energy resources, and electrification, the need for faster, more intelligent planning tools becomes critical, where power flow analysis is a key element in this change. The current key challenges include the high computational cost of traditional existing approaches, limited scalability for large networks, and inflexibility in adapting to dynamic planning scenarios. To address these, several promising paths are being explored, including parallel computing to harness modern hardware and cloud support, machine learning-based models to approximate simulation results efficiently, and adaptive simulation strategies that tailor complexity to specific planning contexts. These approaches aim to significantly reduce execution time while maintaining analytical accuracy, enabling more responsive and data-driven planning workflows. The research goal is to develop an innovative approach to obtaining load flow results within an acceptable computational time and high accuracy. This research will benchmark the state-of-the-art methods, weighing their pros and cons to identify the best path forward. This will be achieved via a vast number of load flow simulations on a large-scale grid. The outcome of the benchmarking will be used as the foundation for the innovative approach to enhance the accuracy and reduce computational time.

Hamad Bin Khalifa University (HBKU)