Energy, Environment and Water Research Center

Energy, Environment and Water Research Center

Abstract

One of the fundamental challenges in developing policies and analyzing opportunities in the area of energy and sustainability is the necessity to consider integrated models that  include physical, engineering, technological, and economic systems. These integrated models have dynamics that evolve on different temporal and spatial scales. The inclusion of dynamics with multiple scales is both computationally and mathematically challenging. Integrated models have a large number of variables, and more importantly contain an even larger number of interactions across time and space. The complexity of these systems grows more steeply with problem size than modern supercomputers can cope with. Optimizing or simulating such systems on a computer requires a huge amount of memory and processing power. The traditional methods of decision making under uncertainty cannot be directly applied in this multiscale setting.  We outline a tentative framework to address these challenges using a multiscale modeling methodology. The issue of multiple scales is addressed using the framework of singular perturbation theory for the control of continuous time problems. We use a case study from power systems modeling to illustrate how the framework can be applied in practice. 

Short Bio

Panos Parpas completed his PhD in 2006 on stochastic optimization at the Department of Computing of Imperial College London. From 2006 to 2008 he was a research associate at Imperial College working on stochastic programming and robust modeling.  He is currently a Cyprus Institute Post Doctoral Fellow at MIT's Energy Initiative where he works on developing integrated multiscale models for decision making under uncertainty in the area of energy systems modeling. He is an associate editor of Computational Management Science. 

For more information contact Dr. Nick Polydorides, tel. +357 22208600 or email n [dot] polydorides [at] cyi [dot] ac [dot] cy