  The need for resiliency of electricity supply is increasing due to increasing
frequency of natural disasters---such as hurricanes---that disrupt supply from
the power grid. Rooftop solar photovoltaic (PV) panels together with batteries
can provide resiliency in many scenarios. Without intelligent and automated
decision making that can trade off conflicting requirements, a large PV system
and a large battery is needed to provide meaningful resiliency. By using
forecast of solar generation and household demand, an intelligent decision
maker can operate the equipment (battery and critical loads) to ensure that the
critical loads are serviced to the maximum duration possible. With the aid of
such an intelligent control system, a smaller (and thus lower cost) system can
service the primary loads for the same duration that a much larger system will
be needed to service otherwise.
  In this paper we propose such an intelligent control system. A model
predictive control (MPC) architecture is used that uses available measurements
and forecasts to make optimal decisions for batteries and critical loads in
real time. The optimization problem is formulated as a MILP (mixed integer
linear program) due to the on/off decisions for the loads. Performance is
compared with a non-intelligent baseline controller, for a PV-battery system
chosen carefully for a single family house in Florida. Simulations are
conducted for a one week period during hurricane Irma in 2017. Simulations show
that the cost of the PV+battery system to provide a certain resiliency
performance, duration the primary load can be serviced successfully, can be
halved by the proposed control system.
