Abstract

A flexible and computationally efficient technique for designing and evaluating grid-tied residential photovoltaic (PV) systems is introduced, which establishes a direct relationship between the inputs to the system, temperature and irradiance, and system performance criteria. For a given year, temperature and irradiance data are rearranged to form a statistical distribution, eliminating thereby the direct time-dependence. The proposed technique decomposes the PV system into three separate layers: ambient conditions, PV output, and dc-ac conversion layers. It reveals important trends, otherwise obscured in the time-dependent view of the data. To demonstrate the applicability of this technique, case studies for optimizing inverter efficiency of residential PV systems in Tennessee and Colorado, are considered.

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