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Owing to the persisting hype in pushing toward global carbon neutrality, the study scope of atmospheric science is rapidly expanding. Among numerous trending topics, energy meteorology has been attracting the most attention hitherto. One essential skill of solar energy meteorologists is solar power curve modeling, which seeks to map irradiance and auxiliary weather variables to solar power, by statistical and/or physical means. In this regard, this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve. Solar power curves can be modeled in two primary ways, one of regression and the other of model chain. Both classes of modeling approaches, alongside their hybridization and probabilistic extensions, which allow accuracy improvement and uncertainty quantification, are scrutinized and contrasted thoroughly in this review. [ABSTRACT FROM AUTHOR]

Inspecting the literature, much effort has been placed on the verification of irradiance forecasts from numerical weather prediction (NWP) models, as such forecasts are thought to have profound implications on the photovoltaic (PV) power forecasts, which in turn affects grid operators' confidence in integrating such power into the electricity grid. However, perhaps due to the proprietary nature of PV plants and lack of access to state-of-the-art NWP model output, only few have had the chance to conduct head-to-head comparisons of global mesoscale and regional downscaled NWP models, in terms of how their irradiance forecast inaccuracies propagate to PV power forecasts. In this regard, this work presents such a study, in which irradiance and PV power forecasts from the European Centre for Medium-Range Weather Forecasts' High-Resolution (HRES) and Météo-France's Application of Research to Operations at Mesoscale (AROME) models are thoroughly verified against the ground-based measurements from 32 research-grade radiometry stations and 94 actual PV plants in Hungary. A wide range of techniques and case studies concerning verification is herein considered, including variance ratio analysis, Murphy–Winkler decomposition, point-versus-areal verification, and seasonal verification. Despite that the results are too numerous to be summarized in a few sentences, the overarching observation from the verification exercise is that the performance of irradiance forecasts can only be used to infer that of PV power forecasts to a certain extent, which contrasts the conventional wisdom. • Global ECMWF outperforms regional AROME for global horizontal irradiance forecasting. • The advantage of ECMWF is reduced if photovoltaic power forecasting is concerned. • Both numerical weather prediction models have the same accuracy for regional forecasting. • Photovoltaic power forecast errors are the highest in winter. [ABSTRACT FROM AUTHOR]

[Display omitted] • Easy-to-use machine learning model for hourly renewable production and load profile modeling. • Novel variance-correction method to improve the reliability of the modeled profiles. • Probabilistic energy supply simulation based on 42 years of meteorological data. • Detailed evaluation and novel categorization of Dunkelflaute events. • Uncertainty analysis of the share of photovoltaic, wind and nuclear electricity. The increasing penetration of weather-dependent renewable energy generation calls for high-resolution modeling of the possible future energy mixes to support the energy strategy and policy decisions. Simulations relying on the data of only a few years, however, are not only unreliable but also unable to quantify the uncertainty resulting from the year-to-year variability of the weather conditions. This paper presents a new method based on artificial neural networks that map the relationship between the weather data from atmospheric reanalysis and the photovoltaic and wind power generation and the electric load. The regression models are trained based on the data of the last 3 to 6 years, and then they are used to generate synthetic hourly renewable power production and load profiles for 42 years as an ensemble representation of possible outcomes in the future. The modeled profiles are post-processed by a novel variance-correction method that ensures the statistical similarity of the modeled and real data and thus the reliability of the simulation based on these profiles. The probabilistic modeling enabled by the proposed approach is demonstrated in two practical applications for the Hungarian electricity system. First, the so-called Dunkelflaute (dark doldrum) events, are analyzed and categorized. The results reveal that Dunkelflaute events most frequently happen on summer nights, and their typical duration is less than 12 h, even though events ranging through multiple days are also possible. Second, the renewable energy supply is modeled for different photovoltaic and wind turbine installed capacities. Based on our calculations, the share of the annual power consumption that weather-dependent renewable generation can directly cover is up to 60% in Hungary, even with very high installed capacities and overproduction, and higher carbon-free electricity share targets can only be achieved with an energy mix containing nuclear power and renewable sources. The proposed method can easily be extended to other countries and used in more detailed electricity market simulations in the future. [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]

Despite the maturity of ensemble numerical weather prediction (NWP), the resulting forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools have become popular. Among those tools, quantile regression (QR) is highly competitive in terms of both flexibility and predictive performance. Nevertheless, a long-standing problem of QR is quantile crossing, which greatly limits the interpretability of QR-calibrated forecasts. On this point, this study proposes a non-crossing quantile regression neural network (NCQRNN), for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing. The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer, which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer, through a triangular weight matrix with positive entries. The empirical part of the work considers a solar irradiance case study, in which four years of ensemble irradiance forecasts at seven locations, issued by the European Centre for Medium-Range Weather Forecasts, are calibrated via NCQRNN, as well as via an eclectic mix of benchmarking models, ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models. Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration, amongst all competitors. Furthermore, the proposed conception to resolve quantile crossing is remarkably simple yet general, and thus has broad applicability as it can be integrated with many shallow- and deep-learning-based neural networks. [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]

• Technical, economic and environmental modeling frameworks are proposed. • Economic and environmental optima of hybrid renewable energy systems are different. • Local climatic conditions highly influence the impacts and the optimal design. • Single- and multi-objective optimization both have advantages and disadvantages. • Installing photovoltaics is the cheapest way to decrease environmental impacts. The rapid spread of renewables made it essential to design optimal hybrid renewable energy systems (HRES) with the distinctive economic and environmental impacts of each technology in mind. According to a comprehensive literature review, very few studies consider life-cycle environmental impacts in small-scale hybrid renewable energy system optimization. This paper aims to fill this gap by providing a multi-objective design framework for household-scale systems based on the technical modeling of several typical components. Solar photovoltaic, wind turbine, solar heat collector, heat pump, heat storage, battery, and as a novelty, heat insulation thickness are considered. Backup power is either drawn from the grid or produced by a diesel generator in grid-connected and off-grid scenarios, respectively. Single objective optimization using genetic algorithm resulted in the least cost and the least environmental footprint options in a case study of three different locations across Europe. Then, Pareto-optimal solutions between the two extremities were explored with a multi-objective genetic algorithm. Single objective results show substantial differences between environmental and economic optima, while multi-objective optimization proved to be an efficient tool to investigate the trade-offs between the two conflicting goals. Solar photovoltaics is proved to be the most competitive technology to reduce environmental impacts in the case of grid-connected systems. Off-grid systems, however, benefit the most from a balanced mix of different renewable energy sources. Life-cycle impacts in the design of systems involving renewables is proven to be relevant while potential applications of the framework are also revealed. Further research areas, as well as the limitations of the methodology are identified in the conclusion. [ABSTRACT FROM AUTHOR]