Abstract
Long-term energy evaluation of PV systems that use micro-inverter configuration (micro-inverter PV systems) is currently unclear due to the lacking of sufficient longitudinal measurement data and appropriate analysis method. The poor knowledge about impact and aging of micro-inverter PV system affects the comprehension and accuracy of PV design and simulation tools. In this paper, we propose a machine learning approach based on the mixed-effect model to compare and evaluate the electrical energy yield of micro-inverter PV systems. The analyzed results using a 5-year period data of PV stations located at Concord, Massachusetts, USA showed that there is no significant difference in yearly electrical energy yield of micro-inverter PV systems under shading and non-shading condition. This finding has confirmed the advantage of micro-inverter PV system over the other PV types. In addition, our work is the first study that identified the average degradation rate of micro-inverter PV of 3% per year (95% confidence intervals: 2% - 4.3%). Our findings in this study have extended substantially the comprehensive understanding of micro-inverter PV system.
Highlights
Photovoltaic system (PV) plays a key role in many renewable energy development plans
We aim to provide a comprehensive understanding about energy efficiency to the amount of generated energy of PV stations that use micro-inverter configuration based on a real dataset
In this study, we compared monthly and yearly yield of micro-inverter PV systems to evaluate the impact of shading
Summary
Photovoltaic system (PV) plays a key role in many renewable energy development plans. To encourage homeowner turning to PV system, many PV designing and planning tools have been released, for example PVSOL [5], PVsyst [6], PVsites [7], PVwatt [8], and Google Project Sunroof [9] These simulation tools have successfully provided an easy-to-use method to provide sufficient outputs about energy performance rooftop PV system and the additional information such as the Levelized Cost of Energy (LCOE) or Energy Payback period [10]–[12]. We aim to provide a comprehensive understanding about energy efficiency to the amount of generated energy of PV stations that use micro-inverter configuration based on a real dataset. Our contribution are: (i) proposing how to utilize mixed-effect model to evaluate the longitudinal PV dataset; (ii) evaluating the impact of shading condition to generated energy of micro-inverter PV system; and (iii) identifying the energy degradation rate at system level, which represents the aging of micro-inverter PV system over time.
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