  As the occurrence of extreme weather events is increasing so are the outages
caused by them. During such unplanned outages, a house needs to be provided
with an energy supply to maintain habitable conditions by maintaining thermal
comfort and servicing at least critical loads. An energy system consisting of
rooftop photovoltaic (PV) panels along with battery storage is an excellent
carbon-free choice to provide energy resiliency to houses against extreme
weather-related outages. However, to provide habitable conditions this energy
system has to provide not only for the non-air-conditioning (non-AC) load
demand but also for the turning on of the AC system which has a considerably
higher startup power requirement as compared to its rated power. Hence, an
intelligent automated decision-making controller is needed which can manage the
trade-off between competing requirements.
  In this paper, we propose such an intelligent controller based on Model
Predictive Control (MPC). We compare its performance with a Baseline controller
which is unintelligent, and a Rule-Based controller which has some
intelligence, based on three resiliency metrics that we have developed. We
perform extensive simulations for numerous scenarios involving different energy
system sizes and AC startup power requirements. Every simulation is one week
long and is carried out for a single-family detached house located in Florida
in the aftermath of Hurricane Irma in 2017. The simulation results show that
the MPC controller performs better than the other controllers in the more
energy-constrained scenarios (smaller PV-battery size, larger AC startup power
requirement) in providing both thermal comfort and servicing non-AC loads in a
balanced manner.
