The ongoing transition toward a zero-emission energy sector and increasing penetration level of intermittent and non-dispatchable renewable energy sources cause several technical challenges for power system operators and planners. On the other hand, recent progress in high-performance computing and the invention of new optimization algorithms suggest very promising tools to address these technical challenges. In this seminar, the application of two large-scale optimization algorithms in power and energy systems will be discussed.

First, a new global solver (Global-TEP) is proposed that can find the globally optimal solution of power system expansion planning with AC network representation, which is a large-scale non-convex problem, in reasonable runtime. The performance of Global-TEP is compared with the state-of-the-art global solvers SCIP and COUENNE, as well as with an SDP-based solver proposed in 2019. As illustrated by numerical test cases, Global-TEP outperforms other solvers significantly.

Second, a novel stochastic optimal device sizing model is proposed for zero energy buildings (ZEBs), which are defined as buildings that generate as much renewable-based energy as they consume annually. Two parallel computing-based solution algorithms (i.e., parallelism in the algebraic level using Schur complement decomposition and parallelism in the problem (scenario) level by Progressive Hedging) are applied to solve the stochastic model. Using the real historical data, numerical studies on the Woodward Library building, located on the University of British Columbia campus, illustrate the efficacy of the solution algorithms to handle large-scale nonlinear programming models with more than 32.8 million variables, which is the largest one reported in the literature so far.

Mahdi Mehrtash is a Senior Member of IEEE and an Assistant Research Professor at the Johns Hopkins University (JHU). Before joining JHU, he was a Postdoctoral Research Fellow at the University of British Columbia, Vancouver, Canada. He received his B.Sc., M.Sc., and Ph.D. degrees in Electrical Engineering, all with the First-Rank Honor and with a concentration on Power Engineering. He has more than ten years of research and teaching experience in the field of power system operation and planning. His main research interests are power system optimization, global optimization, stochastic optimization, zero-energy buildings, and electricity market.