The work by the Berkeley research group comprising of Julie Mulvaney-Kemp, Salar Fattahi, and Prof Javad Lavaei was selected to receive the 2020 Energy, Natural Resources and the Environment (ENRE) Student Best Paper Award at the INFORMS Annual Meeting. The paper was titled “Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow” and aimed to understand the local and global optimality behaviors of solution trajectories for non-convex optimal power flow problems solved through local-search algorithms.
Berkeley IEOR congratulates the research team for the award! The abstract for the paper is below and the original paper can be accessed here.
Abstract — This paper analyzes solution trajectories for optimal power flow (OPF) with time-varying load. Despite its nonconvexity, it is common to solve time-varying OPF sequentially over time using simple local-search algorithms. We aim to understand the local and global optimality behaviors of these local solution trajectories. An empirical study on California data shows that local solution trajectories initialized at different points may converge to the time-varying global solution of the data-driven OPF, even if the problem has multiple local solutions throughout time. That is, these trajectories can avoid poor solutions. To explain this phenomenon, we introduce a backward mapping that relates a neighborhood of the time-varying OPF’s global solution at a given time to a set of desirable initial points. We show that this proposed backward mapping could act as a stochastic gradient ascent algorithm on an implicitly convexified formulation of OPF, which justifies the escaping of poor solutions over time.Mulvaney-Kemp, Julie, Salar Fattahi, and Javad Lavaei. Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow. 4 Mar. 2020. Web.