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The main purpose of this paper is to observe the effect PV variation of solar temperature and irradiance on different conditions and on the inverter output for a grid-connected system. Majorly temperature& solar irradiation effects the performance of a grid connected inverter, also on the photo-voltaic (PV) electric system. The simulation based study was carried out in order to evaluate the variation of inverter output with the variation of solar temperature and irradiance with the variation in climate. The analysis of Grid-connected inverter and their performance at various seasons and conditions is investigated. Solar power plant for a year. In solar power plant efficiency of inverter is also considered to calculate overall losses so, the inverter efficiency and plant performance are considered in this paper using MAT Lab software. In summer season the inverter performed efficiency is decreased because of peak temperature value and slightly increased with the increase in irradiance.

Electricity demand is increasing mainly due to population expansion and the continuous supply of electricity in the residential, industrial, and service sectors. This energy is a critical factor of economic growth, and clean energy resources must be utilized and substituted to reduce the effects of climate change and move toward sustainable development. Site selection and construction of renewable power plants help to diversify the country's energy basket. This study aims to locate and evaluate a photovoltaic solar power plant's potential in Khuzestan province, using Fuzzy-Boolean logic and Analytical Hierarchy Process (AHP) decision analysis based on GIS. The main criteria for selecting the optimal locations include climatic (annual horizontal solar irradiance, sunshine hours, air temperature, relative humidity, windspeed), economic (the proximity to roads, transmission lines, substations, urban areas, and villages), orography (slope, orientation, and altitude) and the environment. According to the results, 0.12% of the province's area has excellent suitability, 25.66% has suitable suitability, and 59.46% lacks any suitability for installing a photovoltaic system. The annual electric power generation potential using single-crystalline silicon panels and polycrystalline silicon panels is calculated as 3189.54 and 2453.49TWh, respectively. Izeh, Bandar-e-Mahshahr, and Bagh-eMalek cities are the most suitable areas for photovoltaic solar farms in province.

Forecasting solar irradiance is of utmost importance in supplying renewable energy efficiently and timely. This paper aims to experiment five variants of recurrent neural networks (RNN), and develop effective and reliable 5-minute short term solar irradiance prediction models. The 5 RNN classes are long-short term memory (LSTM), gated recurrent unit (GRU), Simple RNN, bidirectional LSTM (Bi-LSTM), and bidirectional GRU (Bi-GRU); the first 3 classes are unidirectional and the last two are bidirectional RNN models. The 26 months data under consideration, exhibits extremely volatile weather conditions in Jinju city, South Korea. Therefore, after different experimental processes, 5 hyper-parameters were selected for each model cautiously. In each model, different levels of depth and width were tested; moreover, a 9-fold cross validation was applied to distinguish them against high variability in the seasonal time-series dataset. Generally the deeper architectures of the aforementioned models had significant outcomes; meanwhile, the Bi-LSTM and Bi-GRU provided more accurate predictions as compared to the unidirectional ones. The Bi-GRU model provided the lowest RMSE and highest R2 values of 46.1 and 0.958; additionally, it required 5.25*10-5 seconds per trainable parameter per epoch, the lowest incurred computational cost among the mentioned models. All 5 models performed differently over the four seasons in the 9-fold cross validation test. On average, the bidirectional RNNs and the simple RNN model showed high robustness with less data and high temporal data variability; although, the stronger architectures of the bidirectional models, deems their results more reliable.

A precise and practical power generation forecasting method for outdoor photovoltaic (PV) arrays and its performance evaluation are essential for advancements in solar power generation. Specifically, accurate temperature measurements of each of the PV modules in an array, the entire outdoor PV array, and the power generation system are crucial regardless of climatic and irradiance conditions. However, outdoor PV temperatures are often affected by environmental factors, such as wind direction, wind speed, solar irradiance, and outdoor air temperature fluctuations. Hence, the temperature data vary greatly depending on the measurement point of the module, which is affected by the above environmental factors. The variations of and uncertainties in temperature often extend over long arrays of PV modules common to large-scale PV systems. Therefore, it is necessary to establish a straightforward and reproducible inspection method to determine a single and robust representative PV module temperature for an array. In this study, we have established one such inspection method for outdoor PV arrays. It was observed that the temperature at the center of the array was closest to the average temperature regardless of the measurement period and climatic conditions; thus, it can be considered as the unbiased and robust representative temperature for the PV array. Moreover, lower irradiance conditions are better for accurate and repeatable PV module temperature measurements. The mean temperature differences of the PV array from its average measured at the center of the array were 0.2 °C for low irradiance and 0.5 °C for high irradiance. The temperature difference observed will have 0.1% to 0.2% impact on the power estimation for monitoring purposes.

Accurate prediction of solar energy is an important issue for photovoltaic power plants to enable early participation in energy auction industries and cost-effective resource planning. This article introduces a new deep learning-based multistep ahead approach to improve the forecasting performance of global horizontal irradiance (GHI). A deep convolutional long short-term memory is used to extract optimal features for accurate prediction of the GHI. The performance of such deep neural networks directly depends on their architectures. To deal with this problem, a swarm evolutionary optimization method, called the sine-cosine algorithm, is applied and advanced to automatically optimize the network architecture. A three-phase modification model is proposed to increase the diversity of population and avoid premature convergence in the optimization mechanism. The performance of the proposed method is investigated using three datasets collected from three solar stations in the east of the United States. The experimental results demonstrate the superiority of the proposed method in comparison to other forecasting models.

Renewable energy has received a lot of attention in the previous two decadeswhen it comes to meeting electrical needs in the home, industrial, andagricultural sectors. Solar forecasting is critical for the efficient operation,scheduling, and balancing of energy generation by standalone and grid-connected solar PV systems. A variety of models and methods have beendeveloped in the literature to forecast solar irradiance. This paper provides ananalysis of the techniques used in the literature to forecast solar irradiance.The main focus of the study is to investigate the influence of meteorologicalvariables, time horizons, climatic zone, pre-processing technique, optimiza-tion & sample size on the complexity and accuracy of the model. Due to theirnonlinear complicated problem solving skills, artificial neural network basedmodels outperform other models in the literature. Hybridizing the two modelsor performing pre-processing on the input data can improve their accuracyeven more. It also addresses the various main constituents that influence amodel’s accuracy. The paper provides key findings based on studied literatureto select the optimal model for a specific site. This paper also discussesthe metrics used to measure the efficiency of forecasted model. It has beenobserved that the proper selection of training and testing period also enhancethe accuracy of the model.

The power output of photovoltaic (PV) systems is chiefly affected by climate and weather conditions. In that, PV farm requires accurate weather data, particularly, solar irradiance, in order to predict its power output as a means to improve solar energy utilization. Nevertheless, publicly available datasets which consist both power and weather data are exceptionally few. This may be a combined effect of data propriety and cumbersome collection procedure. And the rarity of such data greatly hinders the progress of solar PV research. Indeed, most solar energy meteorology applications, such as solar forecasting or PV performance evaluation, can benefit from multi-source high-quality datasets. In view of that, we release a PV power output dataset (PVOD), which contains metadata, numerical weather prediction data, and local measurements data from 10 PV systems located in China. In PVOD, a Python toolkit with basic functions for data access and preprocessing is provided. Additionally, a case study on PV power output estimation is depicted to demonstrate the potential usage of the dataset.

In this work, a day-ahead to week-ahead solar irradiance prediction mechanism based on convolutional Long Short-Term Memory (LSTM) model is proposed. The system takes hourly irradiance data from several days previous to the prediction day as the input. Then, features are extracted from the input data using one dimensional convolutional filters. The extracted features from different days are concatenated and serve as the input of the LSTM network. The output of the LSTM is further concatenated with selected original data to emphasize its importance and enhance the prediction results. Afterwards, a fully connected layer is used to produce the final prediction output. The proposed framework can be trained using a relatively small amount of training data within the duration of only two months. Therefore, it has the advantage of being applicable in the initial deployment phase when the amount of training data is limited. The proposed system has been validated using a highly challenging dataset collected in Taiwan with tropical and subtropical marine island climate.

In this paper, the incident solar radiation on horizontal and tilting surfaces was measured of a year using a pyranometer on the roof of Tanta University's Faculty of Science in Tanta city which is locating at Latitude (30° 41`). The average of daily global hourly solar radiation values on horizontal surfaces (H) and the global hourly solar radiation values on inclined surfaces (Ht) had been measured in the south direction of the sun for the meteorological variables. Miguel et al. correlation was used to determine hourly values of the beam and diffuse components of solar radiation (SR) from global solar irradiance. In order to attain best solar module performance throughout the year, the solar energy devices must be tilted south with a tilting angle equal to Tanta city latitude. The maximum value of Ht was 1174.15 Wm-2 in April and the minimum value of Ht was 816.11Wm-2 in October. The performance of 100 W solar silicon photovoltaic (PV) module in outdoor condition can be studied by depending on the incident values of H and Ht. The obtained results showed that the efficiencies of the 100 W solar PV module in winter, spring, summer and autumn were 4.29, 3.78, 7.56 and 4.58 %, respectively. The Maximum power point (Pmax) was calculated in July and its value was 45.98 W at solar noon and the minimum power point (Pmin) was calculated in October with value equal to 18.56 W at solar noon.

Solar photovoltaic (PV) has established itself as a fairly promising, fast-growing renewable energy source. The main determinants of solar PV deployment are thought to be physical and climatic factors – such as latitude and solar irradiance, not to mention terrain and built environment features – as well as socio-economic drivers — such as population density, household size, and education level. Besides, peer effects and neighborhood effects are found to affect the willingness to adopt solar photovoltaic systems strongly. This study aims to set up robust space–time models, which enable us to investigate the drivers of solar PV deployment using fine-grained spatial and temporal data. We use space–time auto-regressive models (STAR) with several exogenous covariates that are expected to explain the installed solar PV capacity. STAR models require the specification of spatial weight matrices (W). As in regular lattice data, we select causal (lower triangular) W matrices so that the consistency of least-squares (LS) estimators is warranted. We show that they can be extended to robust LS estimators, which are necessary because of strong outlier contamination. Models are tested on the Italian municipal data of residential and industrial PV plants installed under the support schemes in force between 2006 and 2011. Empirical results confirm the important role played by the space–time dynamic components. Significant exogenous predictors are found in the domains of the physical features (elevation and land area), demography (population), built environment (residential buildings), and socio-economic aspects (income, employment rate, commuter workers).

High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions.

Conventional models to estimate solar irradiance and photovoltaic power rely on physics and use empirical correlations to handle regional climate and complex physics. Recently, machine learning emerges as an advanced statistical tool to construct more accurate correlations between inputs and outputs. Although machine learning has been applied for modeling solar irradiance and power, no study has reported the accuracy improvement by machine learning compared to conventional physical models. Hence, this study aims to compare the accuracies of physical and machine learning models at each step of solar power modeling, i.e., modeling of global horizontal irradiance, direct normal irradiance, global tilted irradiance, and photovoltaic power. Comparison results demonstrated that machine learning models generally outperform physical models when input parameters are appropriately selected. Machine learning models more significantly reduced the mean bias difference (MBD) than the root mean square difference (RMSD). For global horizontal irradiance and photovoltaic power, machine learning models led to substantially unbiased estimations with 0.96% and 0.03% of MBD, respectively. Among machine learning algorithms, long short-term memory and gated recurrent unit were more recommendable. However, the physical model for solar power estimation was more efficient to reduce RMSD because of their ability to consider constant parameters as input.

A novel intelligent hybrid model is proposed in this paper for accurate prediction and modeling of solar power plants using the wavelet transform package (WTP) and generative adversarial networks (GANs). In the proposed model, the wavelet transform is deployed for decomposing the solar energy signal into the sub-harmonics followed by the statistical feature selection analyses. The GAN model as a deep learning approach is proposed for learning each sub-frequency and predicting the future of the solar energy in the short-time window. Due to the high complexity of the solar irradiance data when training, an evolutionary algorithm based on dragonfly algorithm (DA) is suggested to train the generative and discriminator networks in the GAN. Moreover, a three-phase adaptive modification is suggested to enhance the search capabilities of the DA optimization. The efficiency and appropriate performance of the proposed model is examined and compared with the most successful models such as artificial neural networks (ANNs), support vector machine (SVM), time series, auto-regressive moving average (ARMA), and original GAN on several benchmarks for varied forecast time horizons. The simulation results on the datasets of two regions show the mean absolute percentage error (MAPE) of 0.0282 and 0.0262, when 1-pace forecast horizon, for the two regions which increase up to 0.0531 and 0.0631 for 6-pace forecast horizon. Moreover, the root mean absolute error (RMSE) is 0.0473 and 0.0479 for the two regions at 1-pace forecast horizon which increased up to 0.0895 and 0.0946 for 6-pace forecast horizon. These results show the more precise performance of the proposed forecast deep learning as well as the more optimal performance of the modified DA over the other algorithms shown in the results.

In this article we aim to maximize the net energy a solar-powered spacecraft gains when performing a maneuver. The net energy can be defined as the integral of the power supplied by the solar panels minus the power used by the attitude control system, and is important since energy is a scarce resource in space. Previous research on optimal attitude control has focused on optimization with respect to other costs, such as time-optimal control and optimal attitude control with respect to the integral of the square of the input. The energy flow depends on both the power spent on actuation and the power received from the solar panels. Thus, the optimal attitude control problem should be formulated in such a way that the attitude of the spacecraft relative to the Sun during the maneuver is included in the calculations. This paper proposes a cost function based on net power to address this problem, introducing a new cost function that incorporates the incoming energy from the solar irradiance and the outgoing energy due to actuation. A simulation study comparing an optimal control solution of the proposed net power cost function using IPOPT in CasADi is presented for a 6U CubeSat equipped with solar cell arrays, where the net power based optimal control maneuver is shown to compare favorably to a sun-pointing PD controller.

This article investigates the discrepancy between the theoretical and the experimental optical characterisation results of a Fresnel Lens - silicon on glass (SOG), as a primary optical component toward UHCPV of >3000 suns design ( Shanks et al., 2018 ). All the equations were elaborated for single- and multi-junction solar cells, emphasising the performance when the focal spot area is larger or lesser than the solar cell area. This simple prediction approach of optical characterisation has shown a strong agreement between the theoretical and experimental results of the multi-junction solar cell with a discrepancy of 2% at 7.7 W (77 suns) and 6% on the average cross a solar irradiance on the cell from 3.1 W to 7.7 W corresponding to 31 suns – 77 suns in concentration ratio. A theoretical analysis of the optical performance for a 1/4 of the system grouping three optical interfaces is performed to estimate the optical loss and its influence on the optical efficiency and optical concentration ratio.

Single wall carbon nanotubes (SWCNTs) have long been proposed as a candidate to enhance existing photovoltaic materials by providing an extension to the light absorbed in the infrared. In this work, we discuss the validity of this statement for silicon solar cells and couple a bifacial silicon solar cell to an organic SWCNT cell in a 4-terminal stack. Single chiral species of (6,5), (7,6), and (10,3) are used in the organic cell and the overall contribution to photocurrent from the SWCNTs is discussed in terms of the diameter dependent quantum efficiency, film thickness, the transmittance of silicon, the spectral overlap of the SWCNT's absorbance with the solar irradiance spectrum and the maximum obtainable current density in the infrared.

The performance of solar trough collectors can be enhanced by optimizing the mirror profile. Accordingly, a method for optimizing the trough mirror profile is introduced in the present work. This method aims at enhancing the amount of reflected solar irradiance onto the receiver tube. Optimum profiles “Opt1” and “Opt2” were obtained by implementing multi-objective multi-dimensional optimization. Results show that the “Opt1” profile enhances the overall efficiency of the trough collector by 8.2% compared to an original parabolic trough collector (PTC). This performance improvement was also recorded at different trough angles. The trough depth was reduced by up to 89% for the “Opt1” profile. Therefore, analysis of the wind loads was performed for the PTC and the optimized profiles. This analysis shows that the “Opt1” and “Opt2” profiles have higher wind loads compared to the PTC whenever the wind is blowing across the trough backside. The “Opt1” profile and the PTC have the same wind loads at the local solar noon. The “Opt1” profile has lower wind loads compared to “Opt2” and the PTC whenever the wind is facing the trough front side. Accordingly, adding shields on the backside of the trough is recommended for investigation in a future work.

The solar power generation (SPG) prediction is indispensable to establish a reliable and secure power grid. The intelligent and knowledgeable techniques are required to forecast the most nonlinear and volatile SPG due to its dependency on fluctuating weather conditions (solar irradiance and temperature). In this work, an optimized Extreme Learning Machine (ELM) is employed to forecast real-time SPG of Chhattisgarh state of India by conceding weather conditions. The performance of ELM approach is enhanced by exploring relevant parameters such as weights, biases, and numbers of hidden layers. It requires computational techniques which are proficient enough to deal with high dimensional and complex problems. Teaching Learning Based Optimization (TLBO) technique is modified with two novel approaches to enhance the exploration and exploitation proficiency of TLBO algorithm. The collaboration of modified TLBO (MTLBO) and optimizable ELM technique is implemented to forecast SPG for four different case studies such as an hour ahead, a day ahead, a month ahead and three months ahead forecasting. The performance measures such as mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), mean arctangent absolute percentage error (MAAPE), and correlation of determination (R2) are used to demonstrate the performance of proposed approach. Diebold-Mariano (DM) test and forecasting effectiveness are employed to hypothetically corroborate the capability of MTLBO based ELM model to outperform different optimization based ELM, ELM (with randomly fixed weights and biases) and ANN models. The simulation results contribute the evidence of excel performance of proposed approach for SPG forecasting.

This article focuses on applying a deep learning approach to predict daily total solar energy for the next day by a neural network. Predicting future solar irradiance is an important topic in the renewable energy generation field to improve the performance and stability of the system. The forecast is used as a support parameter to control the operation duty-cycle, data collection or communication activities at energy-independent energy harvesting embedded devices. The prediction is based on previous hourly-measured atmospheric pressure values. For prediction, a back-propagation algorithm in combination with deep learning methods is used for multilayer network training. The ability of the proposed system to estimate the daily solar energy is compared to the support vector regression model and to the evolutionary-fuzzy prediction scheme presented in previous research studies. It is concluded that the presented neural network approach gave satisfying predictions in early spring, autumn, and winter. In a particular setting, the proposed solution provides better results than a model using the support vector regression method (e.g., the MAPE value of the proposed algorithm is 0.032 less than the MAPE value of support vector regression method). The time and computational complexity for neural network training is considerable, and therefore it was assumed to train the network on an external computer or a cloud, where only the network parameters have been obtained and transferred to the embedded devices. Web of Science 27 5 25 18

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

One of the common failures in photovoltaic modules is the degradation of the ethylene-vinyl acetate (EVA) encapsulant due to prolonged ultraviolet exposure and other environmental stress factors, such as temperature and humidity. Experimental studies have shown that significant reduction in the optical transmission due to EVA degradation leads to loss in the available power by more than 50%. In this article, a novel approach to predict the early degradation of EVA encapsulant is proposed by correlating EVA degradation with short-circuit current ( I SC). An electrical circuit simulator, simulation program with integrated circuit emphasis (SPICE), is used to evaluate the short-circuit current obtained under varying optical transmission caused by EVA discoloration. The simulation follows three steps: simulation of the transmitted solar spectrum; simulation of the spectral short-circuit current density; and simulation of the current-voltage ( I–V ) curve to obtain short-circuit current ( I SC), maximum power output ( P max), open-circuit voltage ( V OC) and fill factor. Results show that the reduction in short-circuit current due to EVA degradation differs from the reductions expected due to a spectrally-uniform reduction of intensity of the solar irradiance. Both types of variation are linear, however, the slope due to EVA degradation is larger than the slope obtained for normal intensity variations in the solar irradiance. This model, when applied in conjunction with solar irradiance measurements, can predict early onset of EVA encapsulant failure, thereby enabling preventative measures to be taken.

The optimization of the installation characteristics of photovoltaic (PV) generators guarantee greater generation of electric energy and a better distribution of solar irradiation of the PV modules; on the other hand, to determine the sizing factor- SFI, one must take into account the saturation losses of the AC output during conditions of high irradiance and overheating of the inverters. Based on the behavior of the measured solar irradiance, there are several rapid oscillations, and over the days, most of the measured curves are superior to the clear sky model, caused by a transient effect of the atmospheric boundary layer. Two photovoltaics systems–SFCR, were analyzed using the same model and inverter power but with a different inverter design factor. The SFCR were installed in southern Brazil 30 km apart. According to the inverter operating temperature monitoring data, it was also possible to prove the occurrence of the overtemperature to which the inverter with lower SFI is subjected, reaching operating temperatures on the order of 80 °C. SFCR A had higher solar radiation of 4.2% over the year compared to SFCR B; however, SFCR B had a higher final yield of 11.86%. In this way, PV systems with undersized inverters will be losing electricity generation, in addition to reducing their useful life due to component stress, resulting in inverter changes over the life of the PV system.

We propose a methodology for synthetic generation of solar irradiance (shortwave flux) and air temperature time series using copula functions. The use of copulas for the simulation gives flexibility to represent the serial stochastic variability of the solar irradiation and the air temperature affecting the photo-voltaic (PV) panel energy output. Moreover, it allows to have more control on the desired properties of the time series, not only in the temporal and cross-dependencies, but also in the marginal distributions. We use mixtures of zero mass adjusted density distributions to assess the nature of solar irradiance, alongside vector generalized linear models for the bivariate time series time marginal distributions. We found that the copula autoregressive methodology used, including the zero mass characteristics of the solar irradiance time series, accurately models the stochastic phenomena. Experimental analysis with observed data substantiates the usage and convenience of the proposed methodology to model solar irradiance time series and solar energy across the northern hemisphere, southern hemisphere and equatorial zones. These results will improve the understanding of the fluctuating nature of solar irradiance and also help to understand the underlying stochastic process of photo-voltaic energy production.

Concentrated photovoltaic (CPV) has been identified as an effective method to further enhance the efficiency of photovoltaic cells. Previous studies on CPV mainly focused on III-V multi-junction cells. Nevertheless, III-V CPV technology is mainly used in niche applications due to its high cost. Here, we use metal-halide perovskite solar cell (PSC) to demonstrate a concentrated photovoltaic-thermoelectric tandem device. The thermoelectric generator (TEG) is utilized to reduce the effect of heat generation under concentrated solar irradiance. Our tandem system achieved a peak power conversion efficiency (PCE) of 25.0% at a solar concentration of 3 suns. This efficiency exceeded that of the single PSC by ~4.7%. Our work proves that by controlling the heat flow in concentrated PSC-TEG tandem system, the redundant heat produced by upper PSC can be effectively reused. This tandem structure provides a promising approach to improve the efficiency and stability of PSC under low-concentrated solar irradiation.

This paper presents the optimal placement and sizing (OPAS) of active power filter (APF) considering photovoltaic distributed generation (PVDG) and nonlinear load to control the harmonics as per IEEE-519 standard limits. For the varying nature of PVDG output, the injection of harmonics into the distribution system is thoroughly investigated by incorporating hourly solar irradiance data. In this paper, a novel extended nonlinear load position based APF current injection (ENLPCI) technique is proposed to find the number of APFs and optimal buses for placement of APFs. The optimal placement for APF is calculated here for three distinct cases: a) only one state for minimum number of APFs, b) more than one state with different total harmonic distortion in voltage (THDv) with the same number of APFs and c) more than one state with the same THDv with the same number of APFs. The size and cost of APF are calculated and compared using grey wolf optimization (GWO) and its adaptive version (AGWO) algorithms for each hour. Further, the ENLPCI is also validated by GWO and AGWO algorithms for the optimal results. The proposed algorithm is tested on the IEEE-69 bus test system. The results show that the variation of solar irradiance remarkably affects the OPAS of APF. The AGWO performed better compared to GWO to find the optimal cost of APF.

International audience; Solar irradiance data from high-quality ground-based measurements are primordial for different solar energy applications. In order to achieve the required accuracy, quality control procedures are of great benefit. A variety of approaches have been proposed. In this sense, some approaches propose a visual representation of the routine, while others only provide a time series of binary flag values, and do not propose any specific visualization of the flagged data as opposed to non-flagged ones. In this regard, the present paper puts forward a complete routine including several quality control procedures for solar irradiance measurements by providing visual support for these different approaches. The visual tool in question was validated using five years research data with 10 minutes resolution of the global, diffuse and direct components of solar irradiation collected from three ground-based weather stations in Morocco. This visual tool puts forth a more precise idea of the measurement quality by detecting various errors, such as time shifts, outliers identification; either with one or two components, or consistency tests between the three components of solar radiation when available. The proposed tool can be regarded as a means of improving the detection rate of abnormal data as a first step in diagnosing the prominent causes of error.

This study provides a life cycle cost comparison of four different integrated systems powered by solar energy to provide electricity, water, and cooling for a self-sufficient greenhouse complex. System-1 and System-2 have a vapor absorption cooling system, while System-3 and System-4 have a vapor compression cooling system to provide the required cooling. All the systems have a multi-stage flash desalination unit driven by solar thermal energy to provide freshwater. PV or PV/T modules are installed to meet the electricity demands of the greenhouses. A new greenhouse roof concept is introduced in System-2 and System-4, where there are spectrum selective nanofluids as the working fluid of the PV/T units. This spectrum selective nanofluid absorbs solar radiations having wavelength greater than 1400 nm while flowing through the roof of the greenhouse, and thus reducing the cooling load of greenhouses. The thermal energy of PV/T is utilized to pre-heat the sea water entering the desalination unit for improved process efficiencies. The life cycle cost analysis indicates that the systems employing nanofluid are less costly than the regular systems due to better efficiency and lower energy requirements, especially in cooling. The reductions in life cycle cost of 22.3% and 12.8% are obtained for System-2 and System-4 as compared to System-1 and System-3, respectively. The evacuated tube collectors constitute more than 60% share of the life cycle cost of System-1 and System-3, while PV and PV/T modules have more than 50% share in the life cycle cost for System-2 and System-4. The ambient temperature and solar irradiance have a significant impact on the life cycle cost of all systems. The minimum values for the levelized cost of electricity and cooling are found to be 0.033 $/kWh and 0.015 $/kWh, respectively, for the System-3. System-2 has a minimum cost value of 1.45 $/m3 for the levelized cost of freshwater.

Although solar photovoltaic systems do not emit greenhouse gases (GHG) during their operation lifetime, their manufacturing, transportation, and construction involves energy use and GHG emissions. In this study a life cycle assessment as well as economics analysis of the solar modules from manufacturing to delivery of the final product were evaluated. A multi-criteria analysis (MCA) ranking methodology for solar photovoltaic module manufacturing countries by an importing country based on the modified VIKOR technique was proposed. Uganda, a developing country in sub-Saharan Africa was considered as the country that intends to import solar photovoltaic modules from six (6) selected overseas module manufacturing countries. The results revealed that the solar PV system would have an energy payback time and GHG payback time in the range of 1.32–3.28 years and 0.69–3.19 years, respectively, due to the high solar irradiance received in Uganda. The importation cost incurred are in the range of 37.10–46.38c$/Wp for a functional unit of 50 kW capacity solar photovoltaic system. The MCA ranking results indicated that Brazil is the best choice module manufacturing country for Uganda to import modules from. The proposed methodology is replicable and applicable in any country for the decision-making process.

In this paper, a new study has been carried out to analyze the behavior of four different Photovoltaic (PV) module technologies such as the Mono-crystalline silicon, the Multi-crystalline silicon, the amorphous silicon and the Telluride Cadmium as sub-systems from which a 400kWp pilot plant have been constructed at Ghardaia (The northern-central Sahara desert of Algeria). In this work, the results obtained from experimental data acquisition over a period of one year have been analyzed and discussed in terms of plant power production. In addition to that, performance indicators comprising the reference yield, the final yield, the performance ratio, the capacity factor and a newly suggested factor called the power ratio have been used to assess the sub-systems energetic performances. The performances of the studied sub-systems have been compared, then, to those of other systems located in countries regarded as having similar radiometric and climatic conditions. An additional study have been conducted to evaluate the effect of such important climatic and operating parameters as wind speed, ambient temperature, solar irradiance and module temperature on the energetic performances of the studied sub-systems. The experimental results obtained from monitoring of the studied sub-systems indicate that the plant performs best when it is operated under irradiance comprised between 5.56 kWh/m2 and 7.04 kWh/m2 and ambient temperature between 2 °C and 45 °C. Based on energy production and thermal stability results, the present study has allowed the identification of the most efficient and the best suitable of the aforementioned PV technologies for such arid-regions conditions.

In modern times, the instant improvement of changeable energy generations especially as of wind and solar energy resources in the power grid led with these generations developing a significant resource of ambiguity with behavior of load however living being the major variable source. It is crucial for the economic scheduling in the generation and load balance of the generating units and in trades of electricity market. The forecasting of energy managed to alleviate several challenges to encounter which rises the resource uncertainty. Solar and wind power prediction is observing a intensifying interest from the community research and numerous investigations working on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. Therefore, this work presents a Resilient Back Propagation Neural Network (RBPN) model to produce solar and wind power Short Term Forecasting (STF) and monitoring using Internet of Things (IoT).However, STF is very complex to handle due to the random and nonlinear characteristics of solar irradiance and wind speed under changeable weather conditions. But the proposed Resilient Back Propagation Neural Network (RBPN) is suitable for STF modeling and also the proposed forecasting system is directly connected to IEEE-9 bus to reduce Total Harmonics Distortion (THD) and also reduce the power quality issues in various conditions, such as voltage unbalance control, active and reactive power control. The performance of proposed forecasting system is validated through both hardware and simulation, the simulation is developed by using Matlab Simulink software. In the proposed system, the sensitivity analysis for varied variables and the model comparison with the aspect of proper selection incorporating the persistence and multiple linear regression models. Therefore, execution of the Internet of Things (IoT) in the supervising of solar PV and wind forecasting system was recommended and its implementation was analyzed. Suggested system comprised of data acquisition, data gateway, and smartphone application demonstration.

Solar PVs are mostly built on uncultivated land. However, the increase in land values due to the increasing world population, the lack of suitable areas for potential PV plants, especially in the land-scarce countries, and the increasing energy need led researchers to seek new solutions. At this point, floating solar power plants emerge as a good alternative with their advantages such as not occupying land area and reducing water evaporation by covering the water surface. In this study, a floating photovoltaic power plant with 120 kWp installation power was installed on Buyukcekmece Lake, and the effect of the microclimate data on the produced energy of the system was investigated. Since the energy produced by PV panels is highly dependent on climate effects and there may be many climatic variations depending on the geographical conditions, experimental measurements have been made annually in this study and the results have been analyzed in order to contribute to the researches in this field. From the obtained results, it is seen that the most important factor that positively affects the energy produced is solar irradiance, while specific humidity, wave loads, and module temperature have a negative effect.

Solar power plants are growing tremendously to manage the ever-growing demand for power production and supply in a sustainable manner. Solar irradiance forecasting, being a time series problem, can aid in the planning and design of solar power plants. This work have developed a deep learning forecast model for accurately predicting future values of solar irradiance for given location. The locations have been selected with the help of visual information, which indicates the intensity of solar irradiance. This visual information has been provided by NASA’s Prediction of Worldwide Energy Resources (POWER) project. The proposed model utilizes convolutional layers for extraction of internal representations of solar irradiance time-series data along with the attention-based LSTM network for the identification of temporal dependencies. The study is conducted on solar datasets from two locations for a period of 36 years taken from NASA’s Prediction of Worldwide Energy Resource (POWER) project archive of Renewable Energy. Experiments were conducted with comparisons against various deep learning models. From the study conducted, we analyze the effect of different factors, such as model, optimizer, horizon, to decide upon the final forecast result. It has been observed that the proposed model performed with an R2 score of more than 50 percent which indicate excellent forecast performance. Experimental analysis is also conducted for bias and variance as performance evaluators along with other indicators. The bias and variance components also show superior performance of the proposed model. The convolutional and the attention components enhance the performance of the LSTM model with the bias variance mechanism providing improved generalizability.

This paper presents a detailed target-oriented performance assessment of a 232.5 kWp grid-connected solar PV (GCPV) system installed on a commercial building in Malaysia. Technical and meteorological data for 2019, in 5-min intervals, was retrieved from the solar data logger. The GCPV system generated about 301.5 MWh of energy in 2019, almost achieving the stated target of 305.0 MWh. While most months in 2019 successfully achieved the set monthly target, some did not due to low solar irradiance. The Performance Ratio (PR), Capacity Utilization Factor (CUF), system efficiency, and Levelized Cost of Energy (LCOE) were found to be 85.4%, 14.85%, 9.15%, and 0.396 MYR/kWh, respectively. The PR of the present GCPV system was higher than other GCPV systems surveyed. Approximately 177 metric tons of CO2 emissions were saved during the year after installing the GCPV system. In addition, performance models, based on the key factors of solar irradiation and PV module temperature that affect the yield and power output of the GCPV system, were developed and tested statistically. The findings of this study should be of use to solar energy customers and stakeholders.

Owing to integrating photovoltaic solar systems into power networks, accurate prediction of solar irradiance plays an increasingly significant role in electric energy planning and management. However, the existing hybrid models ignore the influence of other factors except for the irradiance time series and adopt a single branch independent network structure, which may lead to decrease prediction accuracy. In this paper, a novel multivariate hybrid deep neural model named WPD–CNN–LSTM-MLP for 1-h-ahead solar irradiance forecasting is proposed. The novel WPD–CNN–LSTM-MLP model is based on a sophisticated multi-branch hybrid structure with multi-variable inputs, which the multi-branch hybrid structure combines wavelet packet decomposition (WPD), convolutional neural network (CNN), long short-term memory (LSTM) networks, and multi-layer perceptron network (MLP), and the multi-variable inputs include hourly solar irradiance and three climate variables, namely: temperature, relative humidity, and wind speed and their combination. The new model extracts the inherent characteristics of multi-layer inputs sufficiently, overcomes the shortcomings of traditional models, and achieves more accurate forecasting results. The performance of the model is verified by actual data from Denver, Clark, and Folsom, the United States. Comparative studies of traditional individual back propagation neural network, support vector machine, recurrent neural network, LSTM, the climatology–persistence reference forecasts method and the proposed LSTM-MLP model, CNN-LSTM-MLP model, and WPD–CNN–LSTM model reveal that the proposed WPD–CNN–LSTM-MLP deep learning model has better prediction accuracy in hourly irradiance forecasting.

Solar energy is clean energy that will reduce the dependency on fossil fuels and provide us an efficient sustainable source of energy. This paper showcases how effective and efficient use of solar energy through a hybrid photovoltaic-thermal solar system combining with domestic hot water, space heating, and cooling in India, near the Tropic of Cancer using performance analysis of solar irradiance is best for solar application due to higher solar radiation over the year. Typical photovoltaic solar cells have an overall efficiency of 20% and the hybrid photovoltaic system, it is able to provide 60% in heating and around 50–55% in cooling provision in city area households. For single households in different cities of India, the efficiency of a hybrid solar thermal system has been estimated based on weather and location. Suitable photovoltaic solar collectors are selected and a highly efficient system is designed for that particular region. Hence, it can be an efficient ideal model for combined domestic hot water, space heating, and cooling for the tropical region. The solar irradiance of different locations near tropical regions varies in different months of the year and this has been analyzed using the data and an estimated design of the solar hybrid system has been carried out.

Uncertainty in predicting solar energy resources introduces major challenges in power system management and necessitates the development of reliable probabilistic solar forecasts. As the first part of the development of probabilistic forecasts based on the Weather Research and Forecasting model with solar extensions (WRF-Solar), this study presents a tangent linear approach to identify input variables responsible for the largest uncertainties in predicting surface solar irradiance and clouds. A tangent linear analysis is capable of efficiently investigating sensitivities of output variables with respect to various input variables of WRF-Solar because this approach avoids the computational burden of perturbing the initial conditions of individual input variables. We develop tangent linear models (TLMs) for six WRF-Solar physics packages that control the formation and dissipation of clouds and solar radiation, and we evaluate the validity of TLMs using a linearity test. The tangent linear sensitivity analysis is conducted under various scenarios based on satellite observations and model simulations to consider realistic input conditions. A simple method is used to quantify the impact of the uncertainty of input variables on the output variables from the TLMs. The results demonstrate that uncertainties in the output variables that are the focus of this study—including global horizontal irradiance, direct normal irradiance, cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes—are highly sensitive to uncertainties in 14 input variables. This study indicates that the tangent linear method can identify key variables of physics modules in WRF-Solar that can be stochastically perturbed to generate ensemble-based probabilistic forecasts.

The design and location of solar energy technologies are based chiefly on the availability of solar resource. Specialized measurement equipment and solar resource assessment techniques are mandatory in order to identify appropriate locations for deploying solar systems while minimizing risk and increasing the return rate of the site. These needs have given the scientific community a motivation to design highly reliable and cost-competitive sensors. This work presents a novel methodology to estimate solar irradiance components using a low-cost sky-imager system. Captured images are processed to correct the spherical aberration of the dome and to achieve a pixel-radiance model, allowing each pixel of the image to be considered as an individual source of irradiation. The performance of the proposed model is analyzed and compared in four different weather scenarios against a secondary standard solarimetric station, obtaining a normalized mean bias error for direct normal irradiance, global horizontal irradiance and diffuse horizontal irradiance of 4.53%, −6.55% and 19.62% for a sunny day, 0.79%, −6.13% and 7.00% for a partially sunny day, 1.96%, −6.58% and −0.73% for a partially cloudy day, 100%, −10.48% and −0.65% for a cloudy day, respectively. According to the experimental results, the system stands out for its performance, easy setup, low-cost components and a robust estimation in all possible weather scenarios.

In order to enable the transition towards renewable energy sources, probabilistic energy forecasting is of critical importance for incorporating volatile power sources such as solar energy into the electrical grid. Solar energy forecasting methods often aim to provide probabilistic predictions of solar irradiance. In particular, many hybrid approaches combine physical information from numerical weather prediction models with statistical methods. Even though the physical models can provide useful information at intra-day and day-ahead forecast horizons, ensemble weather forecasts from multiple model runs are often not calibrated and show systematic biases. We propose a post-processing model for ensemble weather predictions of solar irradiance at temporal resolutions between 30 minutes and 6 hours. The proposed models provide probabilistic forecasts in the form of a censored logistic probability distribution for lead times up to 5 days and are evaluated in two case studies covering distinct physical models, geographical regions, temporal resolutions, and types of solar irradiance. We find that post-processing consistently and significantly improves the forecast performance of the ensemble predictions for lead times up to at least 48 hours and is well able to correct the systematic lack of calibration., 32 pages, 16 figures, 1 table

The heliocentric orbital dynamics of a spacecraft propelled by a solar sail is affected by some uncertainty sources, including possible inaccuracies in the measurement of the sail film optical properties. Moreover, the solar radiation pressure, which is responsible for the solar sail propulsive acceleration generation, is not time-constant and is subject to fluctuations that are basically unpredictable and superimposed to the well-known 11-year solar activity cycle. In this context, this work aims at investigating the effects of such uncertainties on the actual heliocentric trajectory of a solar sail by means of stochastic simulations performed with a generalized polynomial chaos procedure. The numerical results give an estimation of their impact on the actual heliocentric trajectory and identify whether some of the uncertainty sources are more relevant than others. This is a fundamental information for directing more accurate theoretical and experimental efforts toward the most important parameters, in order to obtain an accurate knowledge of the solar sail thrust vector characteristics and, eventually, of the spacecraft heliocentric position.

Surface Solar Irradiance (SSI) is required for solar energy planning and adoption, and is a fundamental parameter in modelling weather, climate, ecosystem and agricultural activities. Herein a time series based radiative transfer model was developed to simultaneously retrieve properties of clouds, aerosols and surface albedo, which were in turn used to calculate the components of SSI: i.e., global, direct and diffuse irradiance. The calculated results were calibrated across the Australian continent against in-situ measurements to account for minor factors (e.g., water vapor) not considered by the physics-based method. Detailed validation of the SSI components was performed against three years of in-situ measurements at 11 sites across Australia, at a range of time scales (i.e., instantaneous, hourly, daily and monthly) and under both all-sky and cloudy-sky conditions. The main advantage of the present method is the reliable separation of the direct and diffuse components with consistently low biases (~4 W/m2) at all four time scales, while still maintaining relatively low RMSE (root-mean-square-error) and MAE (mean-absolute-error). Once calibration has been performed the model does not require any ancillary data when implemented operationally: that is the model only requires geostationary satellite data. This model may be implemented across the globe using widely available next generation geostationary satellite data with a handful of ground-data for calibration.

Conventional PV solar cells are reaching a limit in practical efficiency; the next step in commercial PV is a low-cost, high efficiency tandem. A mono-crystalline Cadmium Zinc Telluride (CZT) cell that can act as the top cell in a c-Si/CZT four terminal hybrid tandem with a conversion efficiency of 25%–31% is achievable. The CdZnTe mono-crystalline top cell, with thickness about 3 μm, can convert the AM 1.5 spectrum from approximately 300 nm–700 nm with efficiency >17% while passing the red and near IR to the silicon bottom cell. The incremental manufacturing cost to the original c-Si in volume production is more than offset by the value of increased efficiency, which lowers the cost per peak watt to levels equal to or better than obtained today in conventional silicon cells. The CZT top cell is designed to have a band-gap of 1.73eV–1.80eV, which is well matched to the c-Si bandgap of 1.1eV, giving a theoretical maximum efficiency of approximately 40%. The CZT single crystal cell can be more than 90% transparent at wavelengths longer than 750 nm. By band-gap tuning compounds in the CdTe family, using zinc to optimize a hybrid tandem, the efficiency and economics of conventional c-Si (including multi-crystalline), which constitutes 90% of the PV market, is greatly improved. In volume production a LCOE target of $0.02/kWh, consistent with the DOE Solar Target for utility PV, is achievable in locations receiving good solar irradiance.

The optimization of a solar sail-based orbital transfer amounts to searching for the control law that minimizes the flight time. In this context, the optimal trajectory is usually determined assuming constant solar properties. However, the total solar irradiance undergoes both long-term (solar cycles) and short-term variations, and recent analyses have shown that this may have an impact on solar sailing for missions requiring an accurate thrust modulation. In this regard, the paper discusses a strategy to overcome such an issue by suitably adjusting the thrust vector in order to track a reference, optimal, transfer trajectory. In particular, the sail propulsive acceleration magnitude is modified by means of a set of electrochromic material panels, which change their optical properties on application of a suitable electric voltage. The proposed control law is validated with a set of numerical simulations that involve a classical Earth-Mars, orbit-to-orbit, heliocentric transfer.

Due to increase in global warming, it is required to choose an alternative renewable energy source for the electricity generation. Among various renewable energy sources (RES), photo-voltaic energy is one of the most accessible source of energies. But the conversion rate of solar PV cell is about 25 % to 40 % of solar irradiation level. In Solar Photovoltaic (PV) system, to improve and maximize the operating efficiency level, Maximum Power Point Tracking (MPPT) techniques were required. Because of the change in the level of solar irradiance, and the nature of dynamic temperature, this MPP tracking will be highly important to make the solar PV system (SPS) to operate at higher efficiency level. This MPPT method is mainly categorized into three different types such as direct method, indirect method and intelligent method. This paper will gives and overview about various MPPT methods employed for solar PV system. Various controlling algorithms were discussed in this section for a better understanding.

International audience; Optimal management for solar energy systems requires quality data to build accurate models for predicting the behavior of solar radiation. Solar irradiance and environmental data are provided by satellite and in-situ measurements. It is usual that satellite measurements present high temporal resolution with limited spatial resolution, and in-situ measurements provide high accuracy but significant missing data. This paper proposes a methodology based on machine learning algorithms that: i) takes the best of both data sources to obtain an improved spatio-temporal resolution, known as site-adaptation; and ii) provides highly accurate forecasting solar-radiation models based on deep learning on the improved data. Through a study case with real data, we show the benefits of using the proposed methodology based on machine and deep learning techniques to integrate data from different sources and to construct precise solar radiation forecasting models in regions where solar energy systems are required. Results show that machine learning models for site-adaptation performed up to 38% better than traditional methods.

With the widespread adoption of renewable energy sources in the smart grid era, there is an utmost requirement to develop prediction models that can accurately forecast solar irradiance. The stochastic nature of solar irradiance considerably affects photo-voltaic (PV) power generation. Since weather conditions have a high impact on solar irradiance; therefore, we need weather-conscious forecasting models to boost predictive accuracy. Although Recurrent Neural Networks (RNNs) has shown considerable performance in time-series forecasting problems, its sequential nature prohibits parallelized computing. Recently, architectures based on self-attention mechanism have shown remarkable success in natural language programming (NLP), while being computationally superior. In this paper, we propose an RSAM (Robust Self-Attention Multi-horizon) forecasting architecture, which mainly works in two parts: First, multi-horizon forecasting of solar irradiance using multiple weather parameters; Second, prediction interval analysis for model robustness using quantile regression. A self-attention based Transformer model belonging to the family of deep learning models has been utilized for multi-variate solar time-series forecasting. Using the National Renewable Energy Laboratory (NREL) benchmark datasets of two different sites, we demonstrate that the proposed approach exhibit enhanced performance in comparison to RNN models in terms of RMSE, MAE, MBE, and Forecast skill at each forecasted interval.

Biomass-derived alcohols such as 5-Hydroxymethylfurfural represent a promising intermediary energy and species carriers for hydrogen and value‐added chemical production. Solar-driven reforming of biomass to 5-HMF at the desired reaction condition (e.g., at ~150 °C and ~5 bars) offers a sustainable route, but the lack of a rooftop-based solar biomass reactor represents a substantial impediment. In this work, a unique, low-profile “winged” solar concentrator with an evacuated tube reactor was designed for the renewable production of 5-HMF. The viability of this design was examined in detail, using sucrose as the feedstock for 5-HMF production in the presence of sulfuric acid. The optical study indicated that the winged structure significantly increased the effective concentration ratio (by ~42%) and that the design has a broader acceptance angle (from 60° to 90°) compared to CPC alone. During outdoor experiments, the wings boosted the thermal efficiency from 18% to ~25% when operating at 150 °C, and achieved a maximum 5-HMF yield of ~21 mol % (under a solar irradiance of ~800 W/m2). In parallel, a transient computational fluid dynamics model was developed to further elucidate the heat and mass transfer phenomena inside the reactor and to investigate a broader range of operating conditions and reactor design parameters. The numerical and experimental results were in agreement that a specific energy consumption for 5-HMF production of ~23 kWh/kg was achievable and that a full-scale system (~3 L reactor volume, 2.1 m2 gross area) can produce ~0.45 kg of 5-HMF per day, with an average daily solar input of 5 kWh/m2.

A systematic comparison of the performance and economics of concentrated solar power plants using various heat transfer fluids including molten salts and synthetic fluids (Dowtherm A, Syltherm, HELISOL, Malotherm and Therminol VP1) is carried out. First, a process systems approach is used to determine the yield of the thermodynamic cycle using different thermal fluids, their operating conditions and the water consumption. Next, an economic evaluation is performed. For reference Almeria, to the South of Spain, is the location selected because of the high solar irradiance. The efficiency of the regenerative Rankine cycle increases with the maximum operating temperature. The average power production increases from 16 MW using Malotherm to 25 MW using the molten salts, while the water consumption decreases from 3.6 L/kWh to 2.1 L/kWh. A higher efficiency results in more competitive electricity prices that decrease from 0.21 €/kWh (Malotherm) to 0.12 €/kWh (molten salts) with the maximum operating temperature. Furthermore, the effect of the maximum temperature on power and water consumption are computed towards a pre-evaluation of novel fluids under development.

This paper develops an incident energy-based model for fixed multi-row bifacial PV systems considering ground albedo and different configuration parameters, including the inclination (tilt) and orientation angles, the height off the ground, and the horizontal distance between PV rows. The various view factors (VFs) involved in the study, sky model, and the angular position of the sun are included in the model. The proposed analytical model is capable of evaluating the solar irradiance reaching each side of a bifacial PV array at any time and location. The impacts of the configuration parameters on the total energy achievable on the front and back surfaces are studied in detail. Finally, simulations are performed on the IEEE 69-bus test network to show how bifacial PVs can help tackle the Duck curve issue caused by conventional mono-facial PVs.

Objectives: To ensure efficient operation of solar modules and temperature mode in accordance with standard test conditions range within 20-30◦C. Methods: A mathematical model was developed using the laws of photoelectricity, heat and mass transfer, which provides improvement in the efficiency of solar power plants. Findings: This study presents the theoretical analysis of different embodiment for systems of solar modules using physical and mathematical models. The main design and functional characteristics of solar modules were calculated: temperature and efficiency depending on internal parameters and ambient environment. Dependence between temperature and efficiency of solar modules for different values of air temperature, emissivity factor of solar cells, and efficiency of solar power plants, were determined and presented in the form of diagrams. It was found that solar modules have the highest heating temperature at solar irradiance of Еc=1200 W/m2, air temperature of Тa=50◦C, and that the solar modules have the lowest heating temperature at Тa=30◦C and e =0.8, with a decrease in the heating temperature from 111 to 38oС, the efficiency decreases at an air temperature of 50oС and at the emissivity factor of e =0.8 and 0.3 and makes from 4.2 to 10.3%. The dependences were calculated for different latitudes φ: 56o(Moscow), 45o(Krasnodar), 35.5o(Eslamshahr, Iran), 31.6o(Bechar, Algeria), 13.1o(Chennai, India). Solar modules with cooling devices were installed in the Istra district of the Moscow region and bench tests, to specific calculated parameters, were carried out under full-scale conditions. Conclusions: It was found that the efficiency of solar panels increased significantly due to the use of systems with heat-exchange tubes, and the losses, when using an antigravity heat exchanger for cooling photovoltaic cells, reduced by 6-7 times. Novelty: Usage of an anti-gravity heat exchanger to cool the photovoltaic cells, to maintain the optimal operating temperature prevent electrical distortion due to extreme temperatures. Keywords: Solar panels; air temperature; cooling; emissivity factor; solar irradiance; antigravity tubes; temperature loads; performance improvement

Understanding the dynamic response of a solar fuel processing system utilizing concentrated solar radiation and made of a thermally-integrated photovoltaic (PV) and water electrolyzer (EC) is important for the design, development and implementation of this technology. A detailed dynamic non-linear process model is introduced for the fundamental system components (i.e. PV, EC, pump etc.) in order to investigate the coupled system behavior and performance synergy notably arising from the thermal integration. The nominal hydrogen production power is ∼2 kW at a hydrogen system efficiency of 16–21% considering a high performance triple junction III-V PV module and a proton exchange membrane EC. The device operating point relative to the maximum power point of the PV was shown to have a differing influence on the system performance when subject to temperature changes. The non-linear coupled behavior was characterised in response to step changes in water flowrate and solar irradiance and hysteresis of the current-voltage operating point was demonstrated. Whilst the system responds thermally to changes in operating conditions in the range of 0.5–2 min which leads to advantageously short start-up times, a number of control challenges are identified such as the impact of pump failure, electrical PV-EC disconnection, and the potentially damaging accentuated temperature rise at lower water flowrates. Finally, the simulation of co-generation of heat and hydrogen for various operating conditions demonstrates the significant potential for system efficiency enhancements and the required development of control strategies for demand matching is discussed.