Stochastic Optimization

To prevent long-term damage from cable overloads when user curtailment is insufficient, the authors propose a single-stage stochastic program to optimize smart grid topology reconfiguration under uncertain future energy demands. Their approach uses a Unified Overload Index to evaluate expected grid reliability and employs a simheuristic algorithm based on Variable Neighborhood Search—enhanced with variance reduction techniques—to efficiently solve the computationally intensive optimization problem. Evaluated using real-world data from a Luxembourg grid operator, the proposed method rapidly identifies robust countermeasures that minimize user disturbances and ensure the smart grid’s stability for the following day.

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.