Search Results (85)

The accurate short-term forecasting of global horizontal irradiance (GHI) is essential to optimizing the operation and integration of solar energy systems into the power grid. This study evaluates the performance of the Weather Research and Forecasting (WRF) model in predicting GHI over a 48 h forecast horizon at an Italian site: the ENEA Casaccia Research Center, near Rome (central Italy). The instantaneous GHI provided by WRF at model output frequency was post-processed to derive the mean GHI over the preceding hour, consistent with typical energy forecasting requirements. Furthermore, a decomposition model was applied to estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from the forecasted GHI. These derived components enable the estimation of solar energy yield for both concentrating solar power (CSP) and photovoltaic (PV) technologies (on tilted surfaces) by accounting for direct, diffuse, and reflected components of solar radiation. Model performance was evaluated against ground-based pyranometer and pyrheliometer measurements by using standard statistical indicators, including RMSE, MBE, and correlation coefficient (r). Results demonstrate that WRF-based forecasts, combined with suitable post-processing and decomposition techniques, can provide reliable 48 h predictions of GHI and DNI at the study site, highlighting the potential of the WRF framework for operational solar energy forecasting in the Mediterranean region. Full article

Efficient utilization of solar energy in equatorial regions depends on accurately determining the optimal tilt and azimuth angles of fixed photovoltaic (PV) systems. This study presents a three-dimensional vector-based methodology that employs Direct Normal Irradiance (DNI) to estimate the mean direction of incident solar flux. Hourly DNI data from 2020–2024 for the city of Guayaquil, Ecuador, were transformed into spatial vectors and integrated to obtain a resultant vector representing the average orientation and elevation of direct solar radiation. The analysis yielded an optimal tilt angle of 5.73° and an azimuth of 59.15°, values consistent with Guayaquil’s equatorial latitude and previous studies conducted in tropical environments. The low tilt angle reflects the persistently high solar elevation typical of equatorial zones, while the slight northeastward orientation deviation corresponds to the asymmetric diurnal distribution of solar irradiance. The main contribution of this work lies in providing a geometrically rigorous and computationally efficient approach capable of synthesizing the directional behavior of solar flux without relying on complex transposition models. The proposed method enhances the optimization of PV system design, urban energy planning, and renewable microgrid modeling in data-scarce contexts, supporting the sustainable development of solar energy in equatorial regions. Full article

Hybrid geothermal–solar systems leverage complementary resources to enhance efficiency, dispatchability, and low-carbon supply. This review compares mainstream configurations (solar-preheating configurations, solar-superheating configuration, and other emerging concepts) and reports typical performance gains—thermal efficiency of 5–80% and exergy efficiency up to ~60%—observed across resource contexts. Findings indicate that preheating routes are generally preferable under medium direct normal irradiance (DNI) and operation-and-maintenance (O&M)-constrained conditions, while superheating routes become attractive at high DNI with thermal storage; integrated multigeneration systems can deliver system-level benefits for multi-energy parks and district applications. In addition, this paper identifies technical bottlenecks—source matching, storage dependence, and the absence of a unified evaluation—and summarizes control/optimization strategies, including emerging advanced artificial-intelligence algorithms. In addition, the review highlights a standardized comprehensive performance evaluation framework, which covers thermal and exergy efficiency, net power output, complexity, the levelized cost of electricity (LCOE), reliability, and storage. Finally, according to the research status and findings, future research directions are proposed, which pave the way for more effective exploitation of geothermal and solar energy. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

The development of ultrahigh-concentration photovoltaic (UHCPV) systems plays a pivotal role in advancing sustainable solar energy technologies. As the demand for clean energy grows, the need to align concentrated photovoltaic (CPV) system design with high-efficiency solar cell production becomes critical for maximizing energy yield while minimizing resource use. Despite some experimental efforts in UHCPV development, there remains a gap in integrating Fresnel lens-based systems with the comprehensive thermal modeling of key components in improving system sustainability and performance. To bridge this gap and promote more energy-efficient designs, a detailed numerical model was established to evaluate both the thermal and optical performance of a UHCPV system. This model contributes to the sustainable design process by enabling informed decisions on system efficiency, thermal management, and material optimization before physical prototyping. Through COMSOL Multiphysics simulations, the system was assessed under direct normal irradiance (DNI) ranging from 400 to 1000 W/m². Optical simulations indicated a high theoretical optical efficiency of ~93% and a concentration ratio of 1361 suns, underscoring the system’s potential to deliver high solar energy conversion with minimal land and material footprint. Moreover, the integration of thermal and optical modeling ensures a holistic understanding of system behavior under varying ambient temperatures (20–50 °C) and convective cooling conditions (heat transfer coefficients between 4 and 22 W/m².K). The results showed that critical optical components remain within safe temperature thresholds (<54 °C), while the receiver stage operates between 78.5 °C and 157.4 °C. These findings highlight the necessity of an effective cooling mechanism—not only to preserve system longevity and safety but also to maintain high conversion efficiency, thereby supporting the broader goals of sustainable and reliable solar energy generation. Full article

The Tibet Autonomous Region presents immense potential for concentrated solar power (CSP) development, driven by its exceptional solar irradiance levels (e.g., a peak DNI exceeding 2100 kWh/m²/day). This positions it as a strategic contributor to China’s 2060 carbon neutrality target and aligns with global energy transition imperatives. However, CSP deployment in this region faces challenges stemming from unique high-altitude geographic characteristics, a complex terrain, and extreme climatic conditions—including pronounced diurnal temperature fluctuations, high wind speeds, and heavy winter snowfall. Additionally, traditional site-selection models inadequately address these region-specific environmental constraints. To address these limitations, an integrated framework combining geographic information systems (GIS) and multi-criteria decision-making (MCDM) is proposed in this study. A localized evaluation system is developed, incorporating four novel high-altitude-specific indicators: the average and maximum wind speed and the average and maximum snow depth. Criteria weights are determined through a hybrid approach integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM), while candidate sites are prioritized using the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) ranking method. The case study results demonstrate that region-specific environmental factors exert a significantly stronger influence on site suitability than traditional solar resource indicators (e.g., direct normal irradiance) under Tibet’s extreme climatic conditions, emphasizing the necessity of localized evaluation frameworks. The proposed methodology not only provides a robust scientific foundation for CSP site selection in high-altitude regions with environmental complexities but also establishes a replicable framework for optimizing multiple trade-offs in renewable energy systems under geographically complex conditions. Full article

This investigation attempts to develop a tower solar collector-based system designed for the cogeneration of cooling and desalination. The traditional organic Rankine cycle (ORC) integrated with the ejector refrigeration cycle generates limited power and cooling at a single temperature. Acknowledging their limitations, our present study uses an organic flash cycle (OFC) supported by solar heat combined with the two-phase ejector cycle and the reverse osmosis (RO) desalination unit. Since the OFC turbine is fed with two extra streams of fluid, therefore, it provides greater power to run the compressor of the ejector and pumps of the RO unit, resulting in the production of cooling at two different temperatures (refrigeration and air conditioning) and a higher mass flow rate of fresh water. A mathematical model is employed to assess the impact of coil curvature ratio, Rib height, and direct normal irradiation (DNI) on the temperature of the collector’s oil outlet. ANSYS-FLUENT conducts numerical simulations through computational fluid dynamics (CFD) analysis. The results indicate an ultimate increase in oil outlet temperature of 45% as the DNI increased from 450 to 1000 W/m² at a curvature ratio of 0.095 when employing the 1st Rib. Further, a steady-state energy and exergy analysis is conducted to evaluate the performance of the proposed cogeneration, with different design parameters like DNI, coil curvature ratio, rib height, and OFC turbine inlet pressure. The energetic and exergetic efficiencies of the cogeneration system at DNI of 800 W/m² are obtained as 16.67% and 6.08%, respectively. Exergetic assessment of the overall system shows that 29.57% is the exergy produced as cooling exergy, and the exergy accompanied by freshwater, 68.13%, is the exergy destroyed, and 2.3% is the exergy loss. The solar collector exhibits the maximum exergy destruction, followed by the ejector and RO pumps. Integrating multiple technologies into a system with solar input enhances efficiency, energy sustainability, and environmental benefits. Full article

Relative to other renewable energy technologies, concentrated solar power (CSP) is only in the beginning phases of large-scale deployment. Its incorporation into national grids is steadily growing, with anticipation of its substantial contribution to the energy mix. A number of emerging economies are situated in areas that receive abundant amounts of direct normal irradiance (DNI), which translates into expectations of significant effectiveness for CSP. However, any assessment related to the planning of CSP facilities is challenging because of the complexity of the associated criteria and the number of stakeholders. Additional complications are the differing concepts and configurations for CSP plants available, a dearth of related experience, and inadequate amounts of data in some developing countries. The goal of the work presented in this paper was to evaluate the practical CSP implementation options for such parts of the world. Ambiguity and imprecision issues were addressed through the application of multi-criteria decision-making (MCDM) in a fuzzy environment. Six technology combinations, involving dry cooling and varied installed capacity levels, were examined: three parabolic trough collectors with and without thermal storage, two solar towers with differing storage levels, and a linear Fresnel with direct steam generation. The in-depth performance analysis was based on 4 main criteria and 29 sub-criteria. Quantitative and qualitative data, plus input from 44 stakeholders, were incorporated into the proposed fuzzy analytic hierarchy process (AHP) model. In addition to demonstrating the advantages and drawbacks of each scenario relative to the local energy sector requirements, the model’s results also provide accurate recommendation guidelines for integrating CSP technology into national grids while respecting stakeholders’ priorities. Full article

Conventional decomposition models (empirical and numerical decomposition models) estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from global horizontal irradiance (GHI) based on empirical correlations or physical equations. These models are designed for long-term averaged data, typically at an hourly or longer timescale, making them less suitable for real-time estimations with shorter time intervals. To address this limitation, this study applies a data-driven approach utilizing multi-directional irradiance measurements and develops a DNI estimation model based on a Deep Neural Network (DNN). The proposed CUBE-i system estimates DNI using irradiance measurements from five directional pyranometers. The measurement data were obtained from the NREL site in Golden, Colorado, USA. The proposed method demonstrates high estimation accuracy at a 1 min resolution, achieving R² = 0.997 and RMSE = 20.2 W/m². Furthermore, in estimating both direct and diffuse irradiance on a horizontal plane, the model outperforms conventional empirical decomposition models (Erbs, Reindl, Watanabe), achieving up to five times lower RMSE and higher R² values. While further considerations regarding sensor accuracy, applicability to different regions, and installation requirements are necessary, this study validates the feasibility of real-time DNI estimation using a compact and cost-effective pyranometer system. This advancement enhances its potential for widespread applications in solar energy systems, building energy management, meteorology, and environmental research. Full article

The estimation of solar radiation plays an important role in different fields such as heating, agriculture and energy. At present, most studies focus on clear-sky models; it is relatively difficult to quantify the obstruction of radiation by clouds, which makes the calculation of irradiance in cloudy weather more challenging. This paper proposes a method for calculating solar irradiance in cloudy weather, which consists of two parts: radiation and cloud. In the radiation part, clear-sky radiation and the distribution of all-sky irradiance under different haze conditions are studied. In the cloud part, a cloud transmittance model based on ground-based cloud images is studied. Then, combined with the radiation model, the calculation of Global Horizontal Irradiance (GHI) in cloudy weather is achieved. After testing, rRMSE of the clear-sky model for calculating Direct Normal Irradiance (DNI) and GHI is 4.48% and 5.62% respectively, the rRMSE of the all-sky model is 2.28%, and the rRMSE of the cloudy irradiance model is 16.74%. Full article

The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R², and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R² values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman. Full article

The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m²/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability. Full article

In the context of Concentrated Solar Power (CSP) technology, this paper presents a comparison between the Parabolic Trough Collector (PTC) and the Linear Fresnel Collector (LFC), considering both evacuated and non-evacuated receiver tubes. The comparison was carried out in terms of the Levelized Cost of Electricity (LCOE) considering a reference year and four locations in the world, characterized by different levels of direct normal irradiation (DNI) from 2183 kWh/m²/year to 3409 kWh/m²/year. The LCOE depends on economic parameters and on the net energy generated by a plant on an annual basis. The latter was determined by a steady-state 1D model that solved the energy balance along the receiver axis. This model required computing the incident solar power and heat losses. While the solar power was calculated by an optical ray-tracing model, heat losses were computed by a lumped-parameter model developed along the radial direction of the tube. Since the LFC adopted a secondary concentrator, no conventional correlation was applicable for the convective heat transfer from the glass cover to the environment. Therefore, a 2D steady-state CFD model was also developed to investigate this phenomenon. The results showed that the PTC could generate a higher net annual energy compared to the LFC due to a better optical performance ensured by the parabolic solar collector. Nevertheless, the difference between the PTC and the LFC was lower in the non-evacuated tubes because of lower heat losses from the LFC receiver tube. The economic analysis revealed that the PTC with the evacuated tube also achieved the lowest LCOE, since the higher cost with respect to both the LFC system and the non-evacuated PTC was compensated by the higher net energy yield. However, the non-evacuated LFC demonstrated a slightly lower LCOE compared to the non-evacuated PTC since the lower capital cost of the non-evacuated LFC outweighed its lower net annual energy yield. Finally, a sensitivity analysis was conducted to assess the impact on the LCOE of the annual optical efficiency and of the economic parameters. This study introduces key technical parameters in LFC technology requiring improvement to achieve the level of productivity of the PTC from a techno-economic viewpoint, and consequently, to fill the gap between the two technologies. Full article

This study investigates the technical and economic feasibility of a 20 $\mathrm{M}\mathrm{W}$ parabolic trough solar thermal power plant (PTSTPP) located in Kenitra, Morocco, characterized by an annual average direct normal irradiance (DNI) exceeding 5.3 $\mathrm{k}\mathrm{W}\mathrm{h}/{\mathrm{m}}^2/\mathrm{d}\mathrm{a}\mathrm{y}$. Utilizing System Advisor Model (SAM) 2012.12.02 software, the plant is designed with Therminol VP-1 as the heat transfer fluid (HTF) throughout the solar field, coupled with a dry cooling system to reduce water consumption. The proposed thermal energy storage (TES) system employs HITEC solar salt as the storage medium, allowing for six full load hours of thermal energy storage. With a solar multiple (SM) of 2, the simulated plant demonstrates the capability to generate an annual electricity output of 50.51 $\mathrm{G}\mathrm{W}\mathrm{h}$. The economic viability of the plant is further assessed, revealing a Levelized Cost of Electricity (LCOE) of 0.1717 $\mathrm{\$}/\mathrm{k}\mathrm{W}\mathrm{h}$ and a capacity factor (CF) of 32%. This comprehensive analysis provides valuable insights into the performance, economic viability, and sustainability of a parabolic trough solar power plant in the specific climatic conditions of Kenitra, Morocco. Full article

A new cost-effective radiometer has been designed, built, and tested to measure direct normal solar irradiance (DNI). The proposed instrument for solar irradiance measurement is based on an optical fiber as the light beam collector, a semiconductor photodiode to measure the optical power, and a calibration algorithm to convert the optical power into solar irradiance. The proposed radiometer offers the advantage of separating the measurement point, where the optical fiber collects the solar irradiation, from the place where the optical power is measured. A calibration factor is mandatory because the semiconductor photodiode is only spectrally responsive to a limited part of the spectral irradiance. Experimental tests have been conducted under different conditions to evaluate the performance of the proposed device. The measurements confirm that the proposed instrument performs similarly to the expensive high-accuracy pyrheliometer used as a reference. Full article