Journal
APPLIED ENERGY
Volume 323, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.119598
Keywords
Photovoltaic modeling; Solar forecasting; Multi-objective optimization; Error propagation; Irradiance-to-power conversion
Categories
Funding
- Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund [BME- NVA-02]
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This study analyzes the impact of PV plant design parameters on the additional error of irradiance-to-power conversion. The results show that design parameters significantly influence PV power forecast errors, suggesting the importance of considering these factors in PV power forecast verification and design optimization. A tilt angle of 45 degrees and undersizing the inverters by a 1.5 sizing factor have notable effects on forecast accuracy.
The growth of the installed photovoltaic (PV) capacity calls for accurate power forecasts, which are commonly calculated from irradiance forecasts using physical model chains. This study analyses the impact of the PV plant design parameters on the additional error of the irradiance-to-power conversion using three approaches. First, PV power curves are plotted for different design parameters to illustrate the amplification of errors and to enhance the understanding of the presented phenomena. Second, six forecast accuracy metrics are calculated as a function of the design parameters for the irradiance observation and forecast data of ten stations from five climate zones and two forecast providers. Third, the Pareto-optimal tradeoff between the annual energy production and the absolute forecast errors are obtained by the NSGA-II multi-objective optimization algorithm. The results reveal that the design parameters highly influence the PV power forecast errors, which is important to consider in all studies presenting PV power forecast verification. A tilt angle of 45 degrees increases the error metrics up to 49% compared to a horizontal plane, and undersizing the inverters by a 1.5 sizing factor lead to an average error decrease up to 25%. Including the expected forecast errors as an objective during their design optimization process is important to maximize the net revenues of PV plants, especially if the unit imbalance costs are more than 50% of the electricity prices.
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