  This paper presents a distributed optimization algorithm tailored to solve
optimization problems arising in smart grids. In detail, we propose a variant
of the Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN)
method, which comes along with global convergence guarantees for the considered
class of linear-quadratic optimization problems. We establish local quadratic
convergence of the proposed scheme and elaborate its advantages compared to the
Alternating Direction Method of Multipliers (ADMM). In particular, we show
that, at the cost of more communication, ALADIN requires fewer iterations to
achieve the desired accuracy. Furthermore, it is numerically demonstrated that
the number of iterations is independent of the number of subsystems. The
effectiveness of the proposed scheme is illustrated by running both an ALADIN
and an ADMM based model predictive controller on a benchmark case study.
