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• 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]

[Display omitted] • Photovoltaic power curves are used to illustrate the forecast error propagation. • The power forecast accuracy of PV plants depend on their design parameters. • A tilt angle of 45° increases forecast errors by up to 49% compared to a horizontal plane. • Undersizing the inverters by a sizing factor of 1.5 reduces forecast errors by up to 25% • The expected forecast errors should be considered in the PV design optimization. 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° 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. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power is emission-free during operation. However, its life-cycle environmental impacts should be accounted for during the design phase. This paper presents the first methodology to calculate the product environmental footprint (PEF) and the levelized cost of electricity (LCOE) based on a unified technical modeling framework of ground-mounted, grid-connected PV power plants. To test the model, we determined the single- and multiobjective optima of 8 main balance-of-system design parameters applying a genetic algorithm for a case study of two geographical locations with distinct solar resources. Results show that impacts can be reduced by 1–13% in each of the 16 different PEF environmental impact categories. Unfortunately, the different types of impacts cannot be reduced simultaneously with a single design solution, calling for the use of a weighted environmental footprint as the most favorable single-score environmental indicator. Pareto-optimal design solutions for multiple objectives show that increasing the AC/DC ratio, row distance, tilt angle, and cable losses compared to the economic optimum is beneficial for carbon and environmental footprint reduction. By accepting a small cost increase, the majority of the potential impact reduction can be achieved. Trade-offs between economic and environmental objectives are analyzed by assigning a price tag to environmental impacts. Based on the proposed weighting scheme, regions with polluting electricity production mix should choose the most profitable plant design for the largest environmental benefit. In other places eco-design could lead to a 1% overall impact reduction for just 0.1% extra cost. Balancing power requirements, copper recycling scenarios, the uncertainty of ecological impacts, and combined land use are identified as future research areas. Image 1 • First study to integrate environmental footprint method in the design of PV plants. • Economic and environmental optimization results in fairly distinctive designs. • Environmental optimization leads to greater capacity factor but larger land area. • Weighting method between environmental and economic objective is proposed. • Taxing environmental impacts leads to a minor change in least-cost design. [ABSTRACT FROM AUTHOR]