  Determining the optimal size and orientation of small-scale residential based
PV arrays will become increasingly complex in the future smart grid environment
with the introduction of smart meters and dynamic tariffs. However consumers
can leverage the availability of smart meter data to conduct a more detailed
exploration of PV investment options for their particular circumstances. In
this paper, an optimization method for PV orientation and sizing is proposed
whereby maximizing the PV investment value is set as the defining objective.
Solar insolation and PV array models are described to form the basis of the PV
array optimization strategy. A constrained particle swarm optimization
algorithm is selected due to its strong performance in non-linear applications.
The optimization algorithm is applied to real-world metered data to quantify
the possible investment value of a PV installation under different energy
retailers and tariff structures. The arrangement with the highest value is
determined to enable prospective small-scale PV investors to select the most
cost-effective system.
