Recent Advances in Computational Methods for the Discrete and Continuous Optimization of Energy Systems

In this talk we give an overview of recent models and algorithms for the discrete and continuous optimization of a variety of challenging applications in Energy Systems. We provide an overview of applications of deterministic models based on mixed-integer linear/nonlinear programming (MILP/MINLP), Generalized Disjunctive Programming (GDP) and global optimization to highlight the progress that has been made in the application of these optimization techniques to energy systems. We first consider applications of MILP that include optimization of hydrogen supply chains for vehicle use, and long term planning of electric power systems with high penetration of renewables. Next, we consider applications of MINLP that include optimization of shale gas infrastructures. We then consider recent algorithms for rigorous global optimization for which we consider applications in optimal process water networks that involve reuse and recycle, and optimal design of centralized and distributed manufacturing facilities for biomass production. All the examples illustrate the expanding scope of the proposed optimization models and algorithms.