Combinatorial Optimization

First, the author models the deterministic overloading prevention problem using a combinatorial optimization approach to suggest immediate reconfiguration actions, such as load curtailment or fuse switching, when an overload is imminent. Second, the research addresses future uncertainties by employing Monte Carlo Simulation and a simheuristic algorithm to evaluate and optimize the grid’s stability over a planning horizon. Finally, the dissertation introduces a machine learning monitoring system utilizing ND-trees to learn smart meter failure patterns, which helps operators accurately distinguish between harmless communication disruptions and critical grid issues.

The authors formulate the overloading prevention problem as a Multiobjective Mixed Integer Quadratically Constrained Program that aims to maximize the number of connected users while minimizing physical cabinet visits and fuse alterations. By employing a state-of-the-art exact mathematical solver, the method calculates the most appropriate combination of remotely curtailing user power and physically switching grid fuses. Evaluations based on real-world data from a Luxembourg grid operator demonstrate that this automated approach rapidly suggests optimal countermeasures, significantly reducing the need to completely disconnect users during potential overloads.