  Due to high power in-feed from photovoltaics, it can be expected that more
battery systems will be installed in the distribution grid in near future to
mitigate voltage violations and thermal line and transformer overloading. In
this paper, we present a two-stage centralized model predictive control scheme
for distributed battery storage that consists of a scheduling entity and a
real-time control entity. To guarantee secure grid operation, we solve a robust
multi-period optimal power flow (OPF) for the scheduling stage that minimizes
battery degradation and maximizes photovoltaic utilization subject to grid
constraints. The real-time controller solves a real-time OPF taking into
account storage allocation profiles from the scheduler, a detailed battery
model, and real-time measurements. To reduce the computational complexity of
the controllers, we present a linearized OPF that approximates the nonlinear
AC-OPF into a linear programming problem. Through a case study, we show, for
two different battery technologies, that we can substantially reduce battery
degradation when we incorporate a battery degradation model. A further finding
is that we can reduce battery losses by 30% by using the detailed battery model
in the real-time control stage.
