The ESysE option focuses on computational and data-analytic methods applied to the design and analysis of energy systems and infrastructures. The additional courses in this option provide critical foundations in optimization, simulation, statistical analysis, and systems-based approaches. The option will provide a consistent and rigorous track for graduate students whose research focuses on methodological innovations at the interface of energy science/engineering, energy economics, operations research methods, and statistical and data-analytic methods.
Possible examples of research projects include game-theoretic models of electricity markets that present the physical constraints of electricity flow, uncertainty in system parameters, and require innovative mathematical programming solution techniques; machine learning algorithms for characterizing toxicological risks; designs for integrating renewable generation technologies into the electric power system that account for the time and weather dependent performance of the renewable technologies, explicitly consider uncertainties in system properties, and represent the operations and constraints of the other generation and demand resources in the grid to balance cost-effectiveness, reliability, and resilience to range of potential system shocks.
