  Smart devices, storage and other distributed technologies have the potential
to greatly improve the utilisation of network infrastructure and renewable
generation. Decentralised control of these technologies overcomes many
scalability and privacy concerns, but in general still requires the underlying
problem to be convex in order to guarantee convergence to a global optimum.
Considering that AC power flows are non-convex in nature, and the operation of
household devices often requires discrete decisions, there has been uncertainty
surrounding the use of distributed methods in a realistic setting. This paper
extends prior work on the alternating direction method of multipliers (ADMM)
for solving the dynamic optimal power flow (D-OPF) problem. We utilise more
realistic line and load models, and introduce a two-stage approach to managing
discrete decisions and uncertainty. Our experiments on a suburb-sized microgrid
show that this approach provides near optimal results, in a time that is fast
enough for receding horizon control. This work brings distributed control of
smart-grid technologies closer to reality.
