Enhancing Smart Grid Resilience and Reliability by Using and Combining Simulation and Optimization Methods
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.