IEOR Assistant Professor Javad Lavaei has won the INFORMS ENRE Energy 2016 Best Publication Award for his paper titled "Convex Relaxation for Optimal Power Flow Problem: Mesh Networks." Lavaei authored the paper along with Somayeh Sojoudi, Assistant Project Scientist at UC Berkeley and Ramtin Madani, Assistant Professor at the University of Texas at Arlington.
This is Javad's second major award this year. Earlier, he was recognized with the Donald P. Eckman Award by the American Automatic Control Council for being an outstanding young engineer in the field of automatic control.
The abstract for Javad's winning publication can be found below. The full text for the paper can be found here.
Abstract—This paper is concerned with the optimal power flow (OPF) problem. We have recently shown that a convex relaxation based on semidefinite programming (SDP) is able to find a global solution of OPF for IEEE benchmark systems, and moreover this technique is guaranteed to work over acyclic (distribution) networks. The present work studies the potential of the SDP relaxation for OPF over mesh (transmission) networks. First, we consider a simple class of cyclic systems, namely weakly-cyclic networks with cycles of size 3. We show that the success of the SDP relaxation depends on how the line capacities are modeled mathematically. More precisely, the SDP relaxation is proven to succeed if the capacity of each line is modeled in terms of bus voltage difference, as opposed to line active power, apparent power or angle difference. This result elucidates the role of the problem formulation. Our second contribution is to relate the rank of the minimum-rank solution of the SDP relaxation to the network topology. The goal is to understand how the computational complexity of OPF is related to the underlying topology of the power network. To this end, an upper bound is derived on the rank of the SDP solution, which is expected to be small in practice. A penalization method is then applied to the SDP relaxation to enforce the rank of its solution to become 1, leading to a near-optimal solution for OPF with a guaranteed optimality degree. The remarkable performance of this technique is demonstrated on IEEE systems with more than 7000 different cost functions.