Planning and operation of energy systems via risk-aware optimization

The primary objective of this project is to develop computational methodologies for risk-aware optimization and the implementation of these methodologies to optimize the operation of complex energy systems. Examples include renewable power generation systems, buildings, district cooling plants, water desalination, water storage and energy storage systems. In all of these examples, cost effective operation of the associated systems depends on the ability to forecast supply, and to forecast and control demand, and in light of these forecasts and control actions optimally schedule system operations. This necessitates the development of robust optimization methodologies that can suitably address forecast uncertainties, and effectively handle a large number of discrete and continuous variables. This project will specifically focus on developing such methodologies, and demonstrating their advantages in the context of model problems and practical applications.

(1) Formulation of risk-aware optimization problem. (2) Validation and performance assessment of numerical implementation. (3) Case study demonstration. (4) Manuscript summarizing development and computational experiments.