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• Environmental footprint methodology is applied to a utility-scale solar energy plant. • A case study of a grid-connected 20 MWp photovoltaic system in Hungary is presented. • Impacts on climate, human health, ecosystem and natural resources is assessed. • The environmental footprint of electricity is reduced by 75% compared to the grid mix. • A simplified life-cycle model linked to plant design parameters is validated. Balance-of-system components represent a growing share of environmental impacts in photovoltaic utility-scale solar energy systems. There are, however, no effective methods to carry out screening type life-cycle assessment during the planning phase of the power plant to minimize these impacts. This paper uses the European Commission's novel product environmental footprint methodology to explore the life-cycle impacts of a case study in Hungary and applies two life-cycle models with different system boundaries and assumptions. The first model represents a full-fledged assessment with a detailed inventory, while the other, simplified model can be directly linked to the main plant design parameters. Our results show that: a) the production of photovoltaic modules account for around half of the total aggregated environmental impacts, b) compared to the Hungarian grid mix, the new power plant reduces the environmental footprint by 75%. It is also shown that the simplified model captures the overwhelming majority of impacts, thus, it is applicable during the eco-design of similar systems. [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]