Irradiance Variability Research Articles

Correlation analysis of the long-term interplay of cosmic rays, solar activity, and solar irradiance

Abstract This study presents a comprehensive analysis of the interplay between solar irradiance variability, solar activity, and cosmic rays over an extensive period from 1950 to 2023, well beyond the scope of previous research that focused on fewer solar cycles. Besides, using a robust dataset, we investigated the correlation between solar activity, represented by sunspot numbers, and the total solar irradiance, alongside the influence of cosmic rays. In this respect, our methodology incorporated sophisticated statistical techniques, including correlation analysis and linear regression, in order to elucidate the complex relationships among these variables. We identified a strong positive correlation between solar activity and total solar irradiance r ≈ + 0.95 , together with a significant anti-correlation between solar activity and cosmic rays r ≈ − 0.72 as well as cosmic rays and total solar irradiance r ≈ − 0.67 . As a consequence, the results of this study offer new insights into the long-term solar influence on climate through cosmic rays, enhancing our understanding of the Sun-Earth dynamic over prolonged periods. Likewise, the extended time series and the analytical framework developed in this study pave the way for further research into related phenomena, including the effects of cloud cover and potential links between cosmic rays and climate change.

Combining observations and simulations to investigate the small-scale variability of surface solar irradiance under continental cumulus clouds

Abstract. The amount of solar radiation reaching the Earth surface (surface solar irradiance, SSI) is critical for a variety of applications, ranging from surface–atmosphere interactions to solar energy. SSI is characterized by a large spatiotemporal variability, in particular in the presence of cumulus clouds. This results in complex spatial patterns of shadows and sunlight directly related to clouds' geometry and physical properties. Although key in many respects, the instantaneous spatial distribution of SSI remains largely unexplored. Here, we use unique observations from a dense network of pyranometers deployed during the HOPE field campaign to investigate the SSI spatial distribution. For cumulus scenes, bimodal distributions are found, with one mode corresponding to cloud shadows and the other to sunlit areas with enhanced SSI exceeding clear-sky values. Combining large-eddy simulations of cumulus clouds with Monte Carlo ray tracing, we demonstrate the capability of advanced numerical tools to reproduce the observed distributions and quantify the impact of cloud geometrical and physical properties on both modes. In particular, cloud cover strongly modulates their amplitudes, in addition to their position and width, which are also sensitive to cloud height, geometrical depth, and liquid water content. Combining observations and simulations, we also explore sampling strategies to estimate the SSI spatial distribution with a limited number of sensors, suggesting that 10 pyranometers integrated over 10 min can capture most details of the full distribution. Such a strategy could be used for future campaigns to further investigate SSI distributions and their impact on land–atmosphere exchanges or photovoltaic farm management.

Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Optimization Algorithms and Extreme Gradient Boosting Models

Microwave-assisted mechanical rock breaking represents an innovative technology in the realm of mining excavation. The intricate and variable characteristics of geological formations necessitate a comprehensive understanding of the interplay between microwave-induced rock damage and the subsequent deterioration in rock strength. This study conducted microwave irradiation damage assessments on 78 distinct rock samples, encompassing granite, sandstone, and marble. A total of ten critical parameters were identified: Microwave Irradiation Time (MIT), Microwave Irradiation Power (MIP), Longitudinal Wave Velocity prior to Microwave Treatment (LWVB), Longitudinal Wave Velocity post-Microwave Treatment (LWVA), Percentage Decrease in Longitudinal Wave Velocity (LWVP), Porosity before Microwave Treatment (PB), Porosity after Microwave Treatment (PA), Percentage Increase in Porosity (PP), and Uniaxial Compressive Strength following Microwave Treatment (UCSA). Utilizing the Pied Kingfisher Optimizer (PKO) alongside Extreme Gradient Boosting (XGBoost), we developed a PKO-XGBoost machine learning model to elucidate the relationship between UCSA and the nine additional parameters. This model was benchmarked against other prevalent machine learning frameworks, with Shapley additive explanatory methods employed to assess each parameter’s influence on UCSA. The findings reveal that the PKO-XGBoost model provides superior accuracy in delineating relationships among rock physical properties, microwave irradiation variables, microscopic attributes of rocks, and UCSA. Notably, PA emerged as having the most significant effect on UCSA, indicating that microwave-induced microscopic damage is a primary contributor to reductions in rock strength. Additionally, MR exhibited substantial influence; under identical microwave irradiation conditions, rocks with lower density demonstrated greater susceptibility to strength degradation. Furthermore, during microwave-assisted rock breaking operations, it is imperative to establish optimal MIT and MIP values to effectively diminish UCSA while facilitating mechanical cutting processes. The insights derived from this research offer a more rapid, cost-efficient approach for accurately assessing correlations between microwave irradiation parameters and resultant rock damage—providing essential data support for enhancing mechanical rock-breaking efficiency.

Assessment of clear-sky irradiance from 6S affected by local climatology of India

Accurate estimation of solar power generation is crucial for the effective planning and operation of solar energy sector. Solar potential can be estimated at a location from long-term historical irradiance data processed from atmospheric reanalysis datasets. The current study employs a clear-sky radiative transfer model (6S) to simulate the net surface irradiance at four distinct locations in India, comparing our model's output with mean monthly ground-based measurements of surface irradiance. Our findings reveal that the 6S model marginally underestimates solar irradiance, with potential deviations varying depending on the accuracy of input data. This research evaluates the influence of particulate matter concentration and relative humidity on the scattering and absorption of solar radiation. We also state the variability in surface irradiance across the country with the rainfall trends that enhance assessment accuracy. The study reports an annual deviation of 10–15 % in surface irradiance across the country, where rural settlements show lower deviations in simulations. The radiative transfer calculations mentioned in the study are simplistic yet beneficial for a priori evaluation of solar potential. Transmittance estimated from clear-sky models is further implemented in all-sky (with clouds), and thus, model accuracy is an important parameter. Additionally, we explore the trends in aerosol concentrations and the impact of local climatological factors on surface irradiance, providing insights critical for optimizing solar energy utilization.

Photovoltaic energy harvesting booster under partially shaded conditions using MPPT based sand cat swarm optimizer

Photovoltaic (PV) systems perform a vital role in addressing the worldwide energy crisis and fulfilling the escalating energy demand. The variability in irradiance, temperature, and unpredictable weather conditions possess a direct impact on the productivity of PV systems. Furthermore, the existence of partially shaded conditions intensifies the complexity of PV systems, resulting in significant power degradation. These conditions present significant challenges for PV systems to achieve maximum power output and produce optimal energy. To address the prevailing challenges, this study introduces a maximum power point tracking (MPPT) control methodology utilizing a sand cat swarm optimizer (SCSO). This ingenious strategy adapts the sand cat hunting style. The investigation centers on optimizing energy harvesting in PV systems, with a specific emphasis on enhancing precision, rapid convergence, and minimizing oscillations. The suggested SCSO performance is evaluated under a variety of weather situations, including both instances of partially shaded and uniform irradiance. The SCSO results are juxtaposed with other existing bio-inspired algorithms, such as grey wolf optimization (GWO), particle swarm optimization (PSO), and tunicate swarm algorithm (TSA). The proposed SCSO technique achieves 99.94 % tracking accuracy on average and shows superior performance, with faster tracking response and less power oscillation. Moreover, the proposed SCSO generates significantly more energy than the rest compared algorithms. The performance of the suggested method is further validated through a hardware-based experimental assessment, demonstrating an optimal level of tracking performance.

Hybrid Grey Wolf Optimizer for Efficient Maximum Power Point Tracking to Improve Photovoltaic Efficiency

Today, the demand for Renewable Energy (RE) sources has increased a lot; out of all Renewable Energy Sources (RES), Solar Energy (SE) has emerged as a better solution due to its sustainability and abundance. However, energy sources from the sun directly depend on the efficiency of the photovoltaic (PV) systems employed, whose efficiency depends on the variability of solar irradiance and temperature. So harvesting the maximum output from PV panels requires optimized Maximum Power Point Tracking (MPPT) systems. The traditional MPPT systems that involved Perturb and Observe (P&O) and Incremental Conductance (IncCond) are the most widely used models. However, those models have limited efficiency due to rapidly changing environmental conditions and their tendency to oscillate around the Maximum PowerPoint (MPP). This paper proposes a Hybrid Heuristic Model (HHM) called the Hybrid Grey Wolf Optimizer (HGWO) Algorithm, which employs the Genetic Algorithm (GA) model for optimizing the Grey Wolf Optimizer (GWO) algorithm for effectively utilizing MPPT in PV systems. The simulation decreases fluctuation, boosting how the system responds to shifts in the surrounding atmosphere. The framework evolved through several experiments, and its ability to perform was assessed concerning the results of different models for the factors that were considered seriously throughout several solar radiation and temperature scenarios. During all of the tests, the recommended HGWO model scored more effectively than the other models. This succeeded by accurately following the MPP and boosting the power supply.

Advancing short-term solar irradiance forecasting accuracy through a hybrid deep learning approach with Bayesian optimization

The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine-tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.

Solar irradiance variability around Asia Pacific: Spatial and temporal perspective for active use of solar energy

This study discusses solar irradiance variability in the spatial and temporal domains for the active use of solar energy. The spatial domain was calculated using the difference between one location and another in a focused small tile area. The principal component analysis (PCA) was used for this analysis in the spatial and temporal domain. The Umbrella Effect Index (UI) was calculated to determine the probability of an umbrella effect event within a specific time range. This study discusses solar irradiance variability using solar irradiance data analyzed every 10 min from high-temporal observations of geostationary satellites in the Asia-Pacific region. This study shows that solar heterogeneity around the Asia-Pacific ranges from 0–135 W/m2 with a high tendency in the area around the equator and highlands. In addition, this study also shows that the Asia Pacific region has umbrella effect events of approximately 50–2224 h/year with an index ranging approximately 0–0.34. This study presents the annual analysis and seasonal trends of solar irradiance variability over the entire study area. Determining seasonal trends is important because electricity demand fluctuates significantly as seasons progress. An integrated analysis of heterogeneity and UI revealed trends in spatiotemporal variations in solar irradiance. This approach provides useful information for optimizing and managing the locations for installing solar power plants. Moreover, an evaluation of existing solar power plants is presented. Evaluations based on spatiotemporal data reveal impossible characteristics using traditional approaches that use long-term simple averages or typical solar irradiance data. This research shows the distributed photovoltaic (PV) system in wide area can increase the stability of solar energy supply to the grid.

Efficiency and benchmarks for photovoltaic power generation amid uncertain conditions

Photovoltaic (PV) power generation systems are highly subject to weather and site conditions, thus, the construction of PV power plant projects must consider these uncertainties. This study analyzes the monthly electricity generation of 249 utility-scale PV power plants in Japan to evaluate their electricity generation efficiency. Applying the generic data envelopment analysis, benchmark values were identified for power generation from PV power plants. Furthermore, we implemented a Monte Carlo experiment to evaluate the impact of variability in solar irradiance and temperature on power generation efficiency. For our analysis, we considered three inputs—solar irradiance, temperature, and installed capacity—and electricity generation as the output. The results showed that inter-regional gap in the efficiency score between the west and north regions is 0.03, and this can be covered by a 0.1 increase in the DC/AC ratio. Additionally, variability in weather conditions affect both the efficiency of a power plant and production possibility frontier, in turn causing the benchmark values for a generic decision-making unit to vary. Increasing the generation capacity of power plants and operating them more efficiently is essential to expanding the use of renewable energy resources.

Modeling and mapping solar energy production with photovoltaic panels on Politecnico di Torino university campus

Educational institutions have significant impacts on the society and environment they are inhabiting, and they can have a big role in influencing various development fields, including sustainability. The environmental sustainability of universities was critically analyzed recently. These bodies can contribute to the sustainability of cities due to their social role in shaping the future generations. The aim of this work is to analyze Urban Building Energy Modeling with a place-based approach using the open-source software QGIS in predicting energy production with photovoltaic solar technologies on the rooftops of the central university campus of Politecnico di Torino. This modeling can help in assessing the energy security and affordability of current and future sustainable scenarios considering their impact on climate change. This study evaluates the accuracy of urban scale QGIS-based energy modeling with a comparison of measured data available from the monitoring activity of LivingLab of Politecnico di Torino, the free tool PVGIS, and the web tools of ENEA. The QGIS modeling accuracy depends on the different precisions of the Digital Surface Model used to describe the built environment (i.e., 1 m or 5 m) and the climate input data (monthly and annual diffuse-to-global radiation and Linke turbidity factor). Moreover, this assessment can be used to map the results of new photovoltaic systems improving the energy and environmental performance of university campuses. The results of this work shed light on the significance of different input data for energy simulation tools at neighborhood-urban scale. The result shown accuracies in PV production of 10 to 37% with different spatial resolutions of the 3D built environment and of 14 to 15.2% for temporal resolution of solar irradiation variables.

Estimation of Spectral Solar Irradiance in the Ecliptic Plane Using Synthetic Solar Surface Magnetograms

AbstractThe primary external energy input to planetary atmospheres comes from solar radiation. Accurate estimates of solar irradiance are needed in order to understand atmospheric conditions at other planets. Many studies have interpolated Earth‐based irradiance measurements at other planets, disregarding the evolution of solar features, which are responsible for the irradiance variability. We present an approach for assessing solar irradiance at other planets by using synthetic full surface magnetograms of the Sun, generated by the Surface Flux Transport Model, and then converted into sunspots and faculae areas. Following the Spectral And Total Irradiance REconstruction approach, we compute the irradiance as the sum of contributions from sunspots, faculae, and the quiet Sun. We calculate the S‐index, which is a proxy for near‐ultraviolet irradiance, and the total solar irradiance (TSI), which is the wavelength‐integrated value. We demonstrate that a simple phase shift of the Earth‐observed TSI does not produce reliable estimates in the ecliptic. By comparing and correlating our results with the interpolated measurements, we find that the two models agree in the S‐index variability, because faculae have a long lifetime, so the effect of the solar rotation becomes less important. Conversely, the methods disagree strongly in the TSI, since sunspots have a short lifetime, and the interpolation does not take into account their emergence and evolution on the far‐side of the Sun, as it only uses the information of the disc visible for an Earth‐bound observer.

Evaluation of a concentrated solar power plant under meteorological and climatological forcing

The performance of renewable energy-conversion systems depends strongly on the background climate and ambient meteorological conditions. However, common approaches to simulating such systems use aggregated climatic data as forcing. Given strong variability in the climate, along with possible future long-term changes, it is important to understand the trade-offs between high-resolution forcing data and representative data. Here we simulate a concentrated solar power plant driven by hourly-resolved reanalysis data using both air and water-based cooling systems. These simulations allow us to analyze plant performance under realistic meteorological variability at two locations. In addition, we simulate the plant under average climatic conditions, where all sub-seasonal and interannual variability has been removed. These simulations are cheaper to run, but do not represent extreme values in the forcing. Our analysis shows that variability in the direct normal irradiance (DNI) is a critical factor for simulating the plant realistically. We show that where (and when) DNI variability is low, average forcing provides a realistic picture of power output. Average forcing can also provide a good estimate of how the plant will perform on average. However, in cases where understanding how robustly the plant performs, it is critical to include realistic variability in the input data.

The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery.

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry-clear, humid-hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).

Analysis of Supercapacitors in Renewable Energy Systems for Managing Power Fluctuations

Environmental decarbonization drives the world to find better ways to generate and store energy. Sustainable energy in the form of solar and wind is explored with the use of conventional energy storage systems (batteries) to close the gap. Green energy generation is weather-dependent, leading to power output fluctuations, and the short-term variability in irradiance adversely affects the system’s energy output and reliability. Standalone operation of a generating system necessitates a storage energy unit that manages transient loading and effectively shares power between the load and energy storage. This article presents an approach to managing energy fluctuations when renewable energy sources fluctuate, this occurs when short-term variability in irradiance, and transient loading occurs. The approach uses supercapacitors as a short-term energy storage solution. The proposed configuration has the following key advantages: effective power sharing, rapid charge, and discharge cycles in supercapacitors result in voltage restoration under transient conditions. The performance of the system is confirmed in MATLAB/Simulink. The proposed study confirms that integrating supercapacitors with PV systems does provide significant power oscillation management when sudden variations occur in renewable sources. A practical test was performed using smaller components following the same concept, and the results supported the hypothesis. The results show that the output voltage is managed when oscillation in irradiance occurs. This is seen in the simulation without supercapacitors when the output voltage fluctuates between 100 and 450 V within the first second of the simulation; however, with the introduction of SC, the voltage becomes stable and maintains an output of 620 V within 0.02 seconds. This is further supported by the experimental outcomes where almost 50% of the solar panel is covered and the output voltage is maintained.

Evaluation and Bias Correction of the ERA5 Reanalysis over the United States for Wind and Solar Energy Applications

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets. After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist that limit its direct applicability for a wind and solar energy analysis. Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low. During winter, the ERA5-derived solar power is biased high by 23% on average, while on an annual basis, the ERA5-derived wind power is biased low by 20%. ERA5-derived solar power errors are found to have consistent characteristics across the contiguous United States. Errors for the shortest duration and most extreme solar negative anomaly events are relatively small in the ERA5 when completely overcast conditions occur in both the ERA5 and observations. However, longer-duration anomaly events on weekly to monthly timescales, which include partially cloudy days or a mix of cloudy and sunny days, have significant ERA5 errors. At 10 days duration, the ERA5-derived average solar power produced during the largest negative anomaly events is 62% greater than observed. The ERA5 wind speed and derived wind power negative biases are largely consistent across the central and northwestern U.S., and offshore, while the northeastern U.S. has an overall small net bias. For the ERA5-derived most extreme negative anomaly wind power events, at some sites at 10 days duration, the ERA5-derived wind power produced can be less than half of that observed. Corrections to ERA5 are derived using a quantile–quantile method for solar power and linear regression of wind speed for wind power. These methods are shown to avoid potential over-inflation of the reanalysis variability resulting from differences between point measurements and the temporally and spatially smoother reanalysis values. The corrections greatly reduce the ERA5 errors, including those for extreme events associated with wind and solar energy droughts, which will be most challenging for electric grid operation.

Observed patterns of surface solar irradiance under cloudy and clear‐sky conditions

AbstractSurface solar irradiance varies on scales as small as seconds or metres. This variability is driven mostly by wavelength‐dependent scattering by clouds, and to a lesser extent by aerosols and water vapour. The highly variable nature of solar irradiance is not resolved by most atmospheric models, yet it affects, most notably, the land–atmosphere coupling and the quality of solar energy forecasting. Characterising variability, understanding the mechanisms, and developing models capable of resolving it accurately requires spatially and spectrally resolved observational datasets of solar irradiance at high resolution, which are rare. In 2021, we deployed a network of low‐cost radiometers in the Field Experiment on submesoscale spatio‐temporal variability in Lindenberg (FESSTVaL, Germany) and Land surface Interactions with the Atmosphere over the Iberian Semi‐arid Environment (LIAISE, Spain) field campaigns to gather data on cloud‐driven surface patterns of irradiance, including spectral effects, with the aim of addressing this gap in observations and understanding. We find in case studies of cumulus, altocumulus, and cirrus clouds that these clouds generate large spatiotemporal variability in irradiance, but through different mechanisms and at different spatial scales, ranging from 50 m to 30 km. Spectral irradiance in the visible range varies at similar scales, with significant blue enrichment in cloud shadows, most strongly for cumulus, and red enrichment in irradiance peaks, particularly in the case of semitransparent clouds or near cumulus cloud edges. Under clear‐sky conditions, solar irradiance varies significantly in water‐vapour absorption bands at the minute scale, due to variability in atmospheric moisture in the boundary layer. With this study, we show that observing detailed spatiotemporal irradiance patterns is possible using a relatively small, low‐cost sensor network, and that such network observations can provide insight and validation for the development of models capable of resolving irradiance variability.

Method for Spatiotemporal Solar Power Profile Estimation for a Proposed U.S.–Caribbean–South America Super Grid under Hurricanes

Solar photovoltaic (PV) generation technology stands out as a scalable and cost-effective solution to enable the transition toward decarbonization. However, PV solar output, beyond the daily solar irradiance variability and unavailability during nights, is very sensitive to weather events like hurricanes. Hurricanes nucleate massive amounts of clouds around their centers, shading hundreds of kilometers in their path, reducing PV power output. This research proposes a spatiotemporal method, implemented in MATLAB R2023b coding, to estimate the shading effect of hurricanes over a wide distribution of PV solar plants connected to a high-voltage power infrastructure called the U.S.–Caribbean–South America super grid. The complete interconnection of the U.S., the Caribbean, and South America results in the lowest power valley levels, i.e., an overall percentual reduction in PV power output caused by hurricane shading. The simulations assess the impact of hurricanes in 10 synthetic trajectories spanning from Texas to Florida. The Caribbean would also experience lower power valleys with expanded interconnectivity schemes. The U.S.–Caribbean–South America super grid reduces Caribbean variability from 37.8% to 8.9% in the case study. The proposed spatiotemporal method for PV power profile estimation is a valuable tool for future solar power generation expansion, transmission planning, and system design considering the impact of hurricanes.

Correlations between Total and Spectral Solar Irradiance Variations

We compare short-term (seven solar rotations), wavelength-dependent temporal variations in spectral solar irradiance (SSI) with those from the total solar irradiance (TSI). Using space-based measurements, we empirically find good correlations across most of the visible and near-infrared (NIR) spectral range, suggesting that the TSI time variability can provide a useful estimate of SSI variability. These empirically determined correlations are consistent with physics-based bolometric variations, providing a straightforward wavelength-dependent parameterization of the SSI variability given a known change in the TSI. Using a solar-irradiance model to distinguish the facular and sunspot contributions, which are responsible for nearly all the irradiance variability on timescales longer than a day, we confirm these results and determine the correlation contributions due to each magnetic activity type individually. The correlations determined from the model agree in functional form to those of the empirical data, although we do note differences near opacity minimum (1.6 μm). Our results provide a simple TSI-based estimate of the time dependence of the spectral solar variability across the ultraviolet to NIR spectral regions, with the TSI accounting for 94% of the variability in the SSI over the 400–1200 nm range.

Blending of a novel all sky imager model with persistence and a satellite based model for high-resolution irradiance nowcasting

High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts).In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy.ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10km around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods.Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT≤11min ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5-13% over the respective optimal individual method.

On sizing of battery energy storage systems for PV plants power smoothing

The high variability of solar irradiance causes fluctuations in the generation of photovoltaic (PV) power plants. This characteristic affects power system operation, thus, strategies are necessary to mitigate the intermittent characteristics of PV power plants. Several countries adopt grid codes incorporating ramp rate (RR) limitations for PV power injection into the grid, also known as power smoothing control. In this sense, battery energy storage systems (BESS) with coordinated RR control algorithms are commonly applied to mitigate current fluctuations from the PV system to the grid. Therefore, studies on the effect of local solar irradiance on BESS requirements for PV power smoothing applications are very important. It is also worth highlighting that in the literature there is a gap in the evaluation of aspects of battery lifetime, BESS volume, price and BESS oversizing for selecting an ideal battery model, among a catalog of batteries, constituting an industrial concern. Therefore, this work presents a detailed BESS sizing methodology, evaluating aspects of the energy requirements, power, and discharging rate (C-rate), based on the mission profile (featuring a customized solution for each project). In addition, the effect of the battery temperature control on the BESS sizing is evaluated. The case study is based on a 1 MW PV system, assessed using a one-year mission profile of solar irradiance and ambient temperature from Goiânia-Brazil. The PV plant is connected to a 440 V grid associated with a BESS that mitigates oscillations of 10%/min. The BESS sizing is evaluated in terms of BESS volume, lifetime, and oversizing factor for power and energy. The proposed methodology reduces the BESS volume by 57.14% in relation to the traditional BESS design of literature.