Large-Scale Optimization Problems in Energy Systems: Applications of Decomposition Techniques

Most of decision-making problems within the real world energy markets such as operation, planning and regulatory actions need to be first properly modeled through the mathematical optimization tools, and then solved in an efficient manner from computational point of view. However. the nature of such optimization problems is generally large and uncertain. which leads to significant computational complexity and intractability. In practice, most of those problems are significantly simplified and then solved using conventional optimization techniques. This course presents several mathematical tools based on decomposition techniques and distributed optimization to develop efficient solution methods, which are compatible with the practical requirements of those large-scale problems. In particular, the foIIowing techniques will be covered: Lagrangian relaxation (LR), augmented LR, alternating direction method of multipliers (ADMM), and Benders decomposition.

Jalal Kazempour is currently a postdoctoral fellow at the Department or Electrical Engineering, Technical Unisersity of Denmark (DTU), Copenhagen, Denmark. He received his B.Sc. degree from the University of Tabriz, Tabriz, Iran, in 2006. The M.Sc. degree from Tarbiat Modares University, Tehran, Iran, in 2009, and the Ph.D. degree from the University of Castilla.La Mancha, Ciudad Real, Spain, 2013, all in Electrical Engineering. He was a visiting fellow at the Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA, in 2014. His research interests include power systems, electricity markets, optimization, and Its applications to energy systems.