A flexible and computationally effi cient analysis technique for designing and evaluating grid-connected photovoltaic (PV) systems is introduced, which establishes a direct relationship between the inputs to the system, temperatureand 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: an ambient conditions, a PV output, and a dc-ac conversion layer. It reveals important trends, otherwise obscured in the time-dependent view of the data.The time-independent analysis technique is applied to the problem of optimizing inverter e ciency to improve the performance of residential PV systems. A parallel two-inverter con guration is proposed, where one inverterhas a small rated power to handle the frequently occurring low-insolation conditions, while the other inverter is large enough to handle the high-insolation regime. The application of this new con guration leads to energy savingsas well as e ciency and reliability improvements. A feasibility study taking into account the additional investments required to implement the suggested inverter con guration reveals that applying it under the current electricity prices does not make sense from the economic perspective. However, the two-inverter con guration can become an interesting option in the future asenergy prices continue to rise and morenancial incentives for solar systems are introduced.
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Using time-independent analysis in optimizing inverter efficiency for grid-connected photovoltaic systems
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