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• 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]

[Display omitted] • Cloud enhancement is suppressed in lower resolution averaged radiation datasets. • Averaged low-resolution data underestimate the cost of photovoltaic power by up to 3%. • Hourly data overestimate the optimal inverter sizing factor, tilt angle and row spacing. • Aggregation by sampling can better protect the radiation distribution than averaging. • Uncertainties resulting from the data resolution increase with the diffuse fraction. The accuracy of the simulation and optimization of ground-mounted photovoltaic plants depends on the reliability of the meteorological datasets, which is affected by their source, length, and resolution. Quantifying the effect of the irradiance data temporal resolution on the optimal design parameters and the expected profitability is important to improve the credibility of photovoltaic design simulations. The optimal values of nine important design parameters, the annual energy yield, and the levelized cost of electricity are calculated for 50 Baseline Surface Radiation Network stations by an automatic photovoltaic optimization method based on datasets with six different resolutions created by two aggregation methods. The aggregation by averaging suppresses the high irradiance values resulting from the transient cloud enhancement effect, while the aggregation by sampling a single value from the middle of each aggregation interval can retain the original distribution of minute-resolution data. The optimization based on averaged hourly data underestimates the levelized cost of electricity by up to 3%, overestimates the inverter sizing ratio by 0.05 on average, and the tilt angle by up to 5° compared to the high-resolution datasets. The datasets aggregated by sampling provide more reliable results even at lower resolutions; therefore, it can be an effective technique for accurate photovoltaic optimization without the long calculation time of the minute-resolution simulation. [ABSTRACT FROM AUTHOR]

• Irradiance separation and transposition modeling are the two most critical steps. • 13% MAE, 12% RMSE and 23–33% skill score improvement is possible by model selection. • Detailed physical model chains, inlc. reflection and shading, have the lowest MAE. • Simple models have the lowest RMSE, but high bias and underdispersed forecast. • Wind speed has only marginal effect on physical photovoltaic power forecasting. Forecasting the power production of grid-connected photovoltaic (PV) power plants is essential for both the profitability and the prospects of the technology. Physically inspired modelling represents a common approach in calculating the expected power output from numerical weather prediction data. The model selection has a high effect on physical PV power forecasting accuracy, as the difference between the most and least accurate model chains is 13% in mean absolute error (MAE), 12% in root mean square error (RMSE), and 23–33% in skill scores for a PV plant on average. The power forecast performance analysis performed and verified for one-year 15-min resolution production data of 16 PV plants in Hungary for day-ahead and intraday time horizons on all possible combinations of nine direct and diffuse irradiance separation, ten tilted irradiance transposition, three reflection loss, five cell temperature, four PV module performance, two shading loss, and three inverter models. The two most critical calculation steps are identified as irradiance separation and transposition modelling, while the inverter models are the least important. Absolute and squared errors are two conflicting metrics, as the more detailed models result in the lowest MAE, while the simplest ones have the lowest RMSE. Wind speed forecasts have only a marginal effect on the PV power prediction. The results of this study contribute to a deeper understanding of the physical forecasting approach in the research community, while the main conclusions are also beneficial for PV plant owners in preparing their generation forecasts. [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]

A high-quality solar power forecasting system that strictly adheres to grid regulations is valuable for system operators to formulate strategies for power system scheduling. Some grid operators, therefore, enact penalty schemes for the forecasts submitted by photovoltaic (PV) plant owners, as a means to fortify truthful and high-quality forecast submissions. From the perspectives of both plant owners and grid operators, this study inquires into the quality-to-value mapping of solar forecasts in the context of power system day-ahead scheduling. A physics-based solar power forecasting method is presented, which consists of two steps. Firstly, ensemble numerical weather prediction (NWP) is summarized into point forecasts. Then irradiance is converted to power via a physical model chain. The results reveal that the two-step physics-based forecasting method has an advantage over a winning method in Global Energy Forecasting Competition 2014 in terms of several accuracy measures. Subsequently, the economics of solar forecasting is quantified through performing day-ahead scheduling on a modified IEEE 30-bus system with PV and battery storage. It is demonstrated that, by respecting the statistical theory on consistency and elicitability when extracting point forecasts from NWP ensembles, both power system operators and PV plant owners can benefit profoundly in terms of cost savings. The former sees fewer needs for reserves, while the latter is less penalized. The data and Python code used to produce the results are also provided to enhance the reproducibility of this work. [Display omitted] • Solar power forecasts are integrated into a power system day-ahead scheduling framework. • The statistical theory on consistency and elicitability is used to summarize ensemble NWP. • Achieved 5% cost savings on power system reserve needs. • Achieved up to 82% cost savings on penalty of submitted forecasts. [ABSTRACT FROM AUTHOR]