Distributed Economic Model Predictive Control of an Electric Power System Using ALADIN

This paper presents a Distributed Economic Model Predictive control (DEMPC) for the electric power distribution system using the augmented lagrangian alternating direction inexact newton (ALADIN) algorithm. Specifically, this DEMPC solves the Alternating Current Optimal Power Flow (ACOPF) problem over a receding time horizon. The ACOPF problem has been at the heart of many electric power transmission system market operations for decades. Generally, it is a non-linear, non-convex large-scale optimization problem that determines the optimal operation of electric generation, transmission and distribution networks to meet demand while respecting physical system constraints. However, the ACOPF in its traditional form has several limitations when it is applied to emerging electric power distribution system markets that include large amounts of variable renewable energy resources which demand significant ramping capabilities. More specifically, such distribution systems require optimization algorithms that better address the inherent dynamic characteristics of the grid and scale to address the explosion of actively controlled devices at the grid's edge.