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• Five data availability scenarios are compared for different physical model chains. • Nominal power and module orientation are the most critical design parameters. • Unknown tilt and azimuth angles and row spacing should be estimated. • Absolute error is more sensitive to missing design parameters than squared error. • Physical power calculation is encouraged even if some design parameters are unknown. Accurate photovoltaic (PV) power forecasts are essential for the grid integration of the technology. Most solar forecasting studies deal only with solar irradiance forecasting, which highlights the importance of the irradiance to power conversion methods to enable the practical utilization of the advanced irradiance forecasting techniques. The PV power output is commonly calculated with physical model chains that rely on the main design parameters of the PV plants. This study presents an analysis of how the unavailability or uncertain knowledge of the design parameters affects the physical power forecast accuracy. The analysis is performed by verifying the power forecasts created by the bests of 32,400 model chains for 16 PV plants in Hungary for five data availability scenarios based on numerical weather prediction data. The results show that even though the knowledge of all relevant design parameters ensures the lowers errors, the critical parameters are only the nameplate capacities and the module orientation. The increase in mean absolute error and root mean square error is only 1.3% and 0.5%, respectively, if only these crucial parameters are known compared to the baseline scenario. If necessary, the tilt and azimuth angles can also be inferred, where the resulting inaccuracy depends on the estimation errors. The results reveal that the physical model chains can be used with a decent accuracy for power forecast calculations even if the design parameters are unknown, which highly broadens the applicability of the physical method. [ABSTRACT FROM AUTHOR]

• Hay transposition model is modified to account for the row shading in PV plants. • Distribution of all irradiance components along the width of PV rows is modelled. • Diffuse irradiance masking is responsible for 50–80% of total shading losses. • Effect of row spacing and module arrangement on the shading losses is quantified. • 1 × 20 module arrangement is the best for inverters with multiple MPPT. Ground-mounted PV plants with multiple parallel mounting structure rows became the most common type of PV systems, where the shading of the adjacent rows results in significant energy losses. This paper presents a detailed modelling method of the inter-row shading to calculate irradiance distribution along the width of the PV rows. A modification of the Hay transposition model is proposed to account for the shading of the sky diffuse, circumsolar, and ground-reflected irradiance components. The effect of the uneven irradiance on the power output is considered by the cellwise calculation and aggregation of the I-V characteristics. The method is demonstrated by a case study, where the annual energy production and the reflection, shading, and cable losses are calculated for different string arrangements and row spacings. The proposed detailed shading model is compared to a simple shading calculation, where only the shading of the beam irradiance is considered. This simple method underestimates the shading losses by 50–80%, which underlines the importance of the proper calculation of the diffuse irradiance masking losses. The electrical mismatch losses due to the shading are compared for different string arrangements, where the best arrangement is also influenced by the number of the maximum power point trackers. As a general conclusion, the modules at the same height should be connected to the same string to minimise the shading losses. The presented method can improve the accuracy of PV simulations and the optimisation of the shading-related design parameters, like the module orientation and row spacing. [ABSTRACT FROM AUTHOR]

• The optimization of 10 important design parameters of a GCPV plant is introduced. • The IRR objective function includes both technical and economic aspects. • Genetic algorithm is suitable and effective to find the global optimum. • Optimal configurations are presented for six locations worldwide. • The effects of decreasing PV module costs on optimal plant design are analysed. The increasing penetration of photovoltaic (PV) technology calls for the development of an effective method for optimization of grid-connected photovoltaic power plants. This paper presents a simultaneous optimization method of ten important design parameters of a PV plant, including the module power, inverter sizing, support structure dimensions, cable losses, module orientation and row spacing. A mathematical PV performance model taking into account the important effects and losses and an economic cost model were developed and presented in detail. The objective function is the internal rate of return and the optimization is performed by a genetic algorithm. The results show that the proposed models and method are capable to optimize the grid-connected PV plant and provide reliable results after a 6–7 min calculation time. The method was demonstrated in detail for a Hungarian location, including the losses and cost structure of the optimal plant configuration. The optimization was also performed for 5 additional sites around the world to assess the effect of location and meteorology. The impact of the decreasing PV module prices on the optimal design is calculated to identify the expected future trends in PV plant design. The presented optimization method can be utilized to facilitate the optimal design of commercial PV plants and for research purposes. [ABSTRACT FROM AUTHOR]