Optimization is indispensable to engineering and natural sciences. Despite the tremendous advancements in the field of optimization, often we are still faced with optimization models that are too complicated to solve efficiently. Alternatively, the tractable ones are hopelessly insufficient to describe our problems at hand. In this research project we propose to address these challenges with a combined methodology drawing innovations from optimization and valuable insights from the underlying applications.
More specifically, we aim to develop highly efficient optimization algorithms motivated from application-specific insights. We also plan to develop more relevant optimization models by exploiting the fundamental properties of the underlying applications. Our proposed methodology is soundly based on our recent successes in optimization for smart grid applications including power system resilience analysis, fault detection and isolation and demand response signal utilization.
The results of this project will enable the use of the increasingly prevalent sensing facilities and distributed decision making in power systems. They will serve as cornerstones for the transition from the legacy power system of today to the more resilient and flexible smart grid in the future. Further, our proposed results have the potential to shed light on the fundamental properties of well-known difficult optimization problems, by our combined optimization and structure exploiting analysis.
Key words: Smart grid, optimization