  Continued advances in PV and battery energy storage technologies have made
hybrid PV-battery systems an attractive prospect for residential energy
consumers. However the process to select an appropriate system is complicated
by the relatively high cost of batteries, a multitude of available retail
electricity plans and the removal of PV installation incentive schemes. In this
paper, an optimization strategy based on an individual customer's temporal load
profile is established to maximize electricity cost savings through optimal
selection of PV-battery system size, orientation and retail electricity plan.
Quantum-behaved particle swarm optimization is applied as the underlying
algorithm given its well-suited application to problems involving hybrid energy
system specification. The optimization strategy is tested using real-world
residential consumption data, current system pricing and available retail
electricity plans to establish the efficacy of a hybrid PV-battery solution.
