  Rooftop solar photovoltaic (PV) panels together with batteries can provide
resiliency to blackouts during natural disasters such as hurricanes. 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 utilizing the flexibility of various household
demands, an intelligent system can ensure that critical loads are serviced
longer than a non-intelligent system. As a result a smaller (and thus lower
cost) system can provide the same energy resilience that a much larger system
will be needed otherwise.
  In this paper we propose such an intelligent control system that uses a model
predictive control (MPC) architecture. 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 two rule based controllers, a simple
all-or-none controller that mimics what is available now commercially, and a
Rule-Based controller that uses the same information that the MPC controller
uses. The controllers are tested through simulation on a PV-battery system
chosen carefully for a small single family house in Florida. Simulations are
conducted for a one week period during hurricane Irma in 2017. Simulations show
that the size of the PV+battery system to provide a certain resiliency
performance can be halved by the proposed control system.
