Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow

Julie Mulvaney-Kemp, Salar Fattahi, and Javad Lavaei, Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow, conditionally accepted for IEEE Transactions on Control of Network Systems, 2020.

Abstract: This paper analyzes solution trajectories for optimal power flow (OPF) with time-varying load. Despite its nonlinearity, time-varying OPF is commonly solved every 5-15 minutes using local-search algorithms. Failing to obtain the globally optimal solution of power optimization problems jeopardizes the grid’s reliability and causes financial and environmental issues. The objective of this paper is to address this problem by understanding the optimality behavior of OPF solution trajectories. An empirical study on California data shows that, with enough variation in the data, local search methods can solve OPF to global optimality, even if the problem has many local minima. To explain this phenomenon, we introduce a backward mapping that relates the time-varying OPF’s global solution at a given time to a set of desirable initial points. We show that this mapping could act as a stochastic gradient ascent algorithm on an implicitly convexified formulation of OPF, justifying the escape of poor solutions over
time. This work is the first to mathematically explain how temporal data variation affects the complexity of solving power operational problems.