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Continued advances in PV and battery energy storage technologies have madehybrid PV-battery systems an attractive prospect for residential energyconsumers. However the process to select an appropriate system is complicatedby the relatively high cost of batteries, a multitude of available retailelectricity plans and the removal of PV installation incentive schemes. In thispaper, an optimization strategy based on an individual customer's temporal loadprofile is established to maximize electricity cost savings through optimalselection of PV-battery system size, orientation and retail electricity plan.Quantum-behaved particle swarm optimization is applied as the underlyingalgorithm given its well-suited application to problems involving hybrid energysystem specification. The optimization strategy is tested using real-worldresidential consumption data, current system pricing and available retailelectricity plans to establish the efficacy of a hybrid PV-battery solution.

Optimal Selection of Small-Scale Hybrid PV-battery Systems to Maximize Economic Benefit Based on Temporal Load Data