Photovoltaic Energy Generation Research Articles

Scientific Mapping of Machine Learning Methods in Predicting Power Output of Solar Photovoltaic Power Systems

Objective: This study aimed to conduct a scientometric mapping of the scientific literature on prediction models in photovoltaic solar energy generation, with a special focus on grid-connected photovoltaic systems (GCPV), aiming to provide important insights for researchers, policymakers, and professionals interested in advancing the integration of photovoltaic solar energy into the current energy distribution system. Theoretical Framework: In this section, the main concepts and theories underpinning the research are presented, focusing on prediction models in photovoltaic solar energy generation, as well as grid-connected photovoltaic systems (GCPV). Method: The methodology adopted comprised a bibliometric approach, analyzing publications indexed in the Scopus and Web of Science databases over the last decade, using the Biblioshiny software from RStudio. Results and Discussion: The results revealed a significant growth in academic production, identifying key authors, leading research countries, and influential journals in the field. Central and emerging themes were also mapped, along with research gaps and opportunities in the field of photovoltaic solar energy. Research Implications: The practical and theoretical implications of this research include insights into how the results may influence the integration of photovoltaic solar energy into the energy distribution system, impacting areas such as scientific research, policy development, and professional practice. Originality/Value: This study contributes to the literature by offering a comprehensive mapping of research on prediction models in photovoltaic solar energy generation, highlighting gaps and opportunities to advance the field, as well as providing valuable insights for various stakeholders interested in this area.

Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

Numerical study on heat transfer and evaporation vaporization performance of solar assisted heat pump regenerative evaporator based on dual-phase change coupled heat transfer

Using phase-change slurry (PCS) as the working medium for solar energy systems, for example, in solar-assisted heat pump (SAHP), offers numerous advantages such as photovoltaic (PV) and photothermal (PT) energy generation and heat supply. The PCS experiences a dual-phase change coupled heat transfer during the heat release and refrigerant vaporization, as investigated through simulation. This study confirmed a coupled heat transfer with dual-phase change. It was found that there is more vapor at the upper coupling interface than at the lower coupling interface, and the difference is obviously enhanced when the flow rate decreases from 0.4 m s−1 to 0.1 m s−1 or the temperature increases from 12 °C to 16 °C. The increased rate of flow or decrease in refrigerant temperature is associated with a reduced equilibrium concentration at the lower coupling interface. Nevertheless, an increase in the flow rate at the lower coupling interface causes a decrease in the equilibrium concentration, while raising the temperature increases the concentration. When the flow rate increases from 0.1 m s−1 to 0.4 m s−1, the heat transfer coefficient at the upper/lower coupling interface increases from 159.77 W m−2 K−1/292.58 W m−2 K−1 to 593.54 W m−2 K−1/654.36 W m−2 K−1, and the heat transfer enhancement ratio reaches 45.39% and 9.29%, respectively. When the refrigerant temperature increases from 12 °C to 16 °C, the heat transfer coefficient at the upper/lower coupling interface decreases from 579.16 W m−2 K−1/572.91 W m−2 K−1 to 329.44 W m−2 K−1/365.67 W m−2 K−1, respectively. Increasing tilt angle of the pipe within a moderate range is a potential solution to enhance heat transfer and improve heat transfer uniformity.

Green synthesis of gold nanoparticles via Artocarpus odoratissimus peel extract for potential applications of optical filter and catalytic degradation

Gold nanoparticles (Au NPs) were synthesized via a green synthesis method, utilizing the peel extract of Artocarpus odoratissimus as both the reducing and capping agent. This study investigates the impact of peel extract concentration (ranging from 0.002 to 0.012 g/mL) and reaction temperature (including room temperature, 45 °C, and 75 °C) on the properties of the synthesized Au NPs. Various characterization techniques, such as UV–Visible spectrophotometry, transmission electron microscopy (TEM), Fourier Transform infrared spectroscopy (FTIR), X-ray diffractometry (XRD), and zeta potential analysis, were employed to analyze the synthesized samples. UV–Visible spectroscopy revealed absorption peaks of the Au NPs around 533–537 nm, varying with synthesis parameters, while TEM analysis indicated particle sizes ranging from 12 to 25 nm. XRD analysis confirmed the formation of Au NPs, with diffraction peaks aligning well with the standard. FTIR analysis suggested interactions between phytochemicals and Au NPs, contributing to their reduction and size control. The synthesized Au NPs exhibited remarkable optical filtering capability, with up to 87 % transmittance in the 751–1126 nm spectral range and significant absorbance in the lower wavelength region (300–751 nm). Additionally, catalytic studies revealed that Au NPs accelerated the degradation of rhodamine B dye, 0.0795 min−1, compared to 0.0445 min−1 without Au NPs. These findings underscore the potential of Au NPs for use in hybrid photovoltaic-thermal systems as filters, where transmitted light can be harvested for photovoltaic energy generation while absorbed light can be utilized for the thermal aspect. Moreover, Au NPs offer promise for improving wastewater quality by enhancing pollutant degradation.

Agrivoltaic system design tools for managing trade-offs between energy production, crop productivity and water consumption

Agrivoltaic systems that locate crop production and photovoltaic energy generation on the same land have the potential to aid the transition to renewable energy by reducing the competition between food, habitat, and energy needs for land while reducing irrigation requirements. Experimental efforts to date have not adequately developed an understanding of the interaction among local climate, array design and crop selection sufficient to manage trade-offs in system design. This study simulates the energy production, crop productivity and water consumption impacts of agrivoltaic array design choices in arid and semi-arid environments in the Southwestern region of the United States. Using the Penman–Monteith evapotranspiration model, we predict agrivoltaics can reduce crop water consumption by 30%–40% of the array coverage level, depending on local climate. A crop model simulating productivity based on both light level and temperature identifies afternoon shading provided by agrivoltaic arrays as potentially beneficial for shade tolerant plants in hot, dry settings. At the locations considered, several designs and crop combinations exceed land equivalence ratio values of 2, indicating a doubling of the output per acre for the land resource. These results highlight key design axes for agrivoltaic systems and point to a decision support tool for their development.

Comparison of economic viability between photovoltaic generation on rural properties and soybean production

The main objective of this study was to conduct a comparative analysis of the feasibility of implementing rural photovoltaic farms in relation to soybean cultivation in different states of Brazil. The study aims to assist rural producers in making decisions regarding the occupation of rural areas with photovoltaic generation systems, considering that these areas could otherwise be used for crop production. Given the agricultural vocation and the need for food supply, these areas would be unavailable for crop production during the lifespan of the photovoltaic system. Therefore, data on soybean production costs and costs for implementing photovoltaic systems were collected. This allowed for the calculation of the profitability of each activity by subtracting the production costs from the revenues obtained through the sale of inputs. Based on these data, cash flows were elaborated, and the Net Present Value (NPV) was calculated for each activity. Photovoltaic energy generation resulted in the highest NPV in all states, with Paraná standing out at NPV R$ 4,340,184.85. Regarding soybean production, the state of Goiás achieved the best result for the activity, with NPV R$ 75,166.01. The analyzed activities demonstrated financial viability as they resulted in NPVs greater than zero.

Paradigms of environmental sustainability in photovoltaic energy generation

Paradigms of environmental sustainability in photovoltaic energy generation

Impact of stratospheric aerosol injection on photovoltaic energy potential over Nigeria

This study evaluates the impact of the stratospheric aerosol injection (SAI) technique for solar radiation management (SRM) on the potential of photovoltaic energy in four climatic regions throughout Nigeria. The photovoltaic energy potential for the SRM scenario (PVEsrm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PVE_{srm}$$\\end{document}) and the reference database (PVEref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PVE_{ref}$$\\end{document}) were evaluated using solar radiation and temperature data from the ARISE-SAI-1.5 model and from the MERRA-2 climate data repository, respectively. Before projecting the impact of the SAI approach on photovoltaic energy generation, the agreement between PVEsrm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PVE_{srm}$$\\end{document} and PVEref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PVE_{ref}$$\\end{document} was evaluated using the index of agreement metric. The analysis showed that the index of agreement had values of 0.90 in the Sahel, 0.98 in the Guinea Savannah, 0.97 in the rainforest, and 0.82 in the coastal regions. Other validation metrics used also showed similar trends across the climatic regions in Nigeria. The projected analysis of the impact on photovoltaic energy generation between 2035 and 2069 indicated potential gains of + 5.20 in the Sahel, + 3.60 in the Guinea Savannah, and + 3.40 in the rainforest, but a decline of − 3.20 in the coastal region, all values in watts per square meters. In conclusion, this study reveals that the implementation of the SAI approach for solar radiation management would have a relatively gainful influence on solar power generation in the Sahel, the Guinea Savannah, the rainforest but declined effect in the coastal region. The results of this study provide valuable insights into the influence of solar radiation management and renewable energy generation in different climatic zones across Nigeria.

Area Day‐Ahead Photovoltaic Energy Generation Forecasting by Auto‐Encoder Inputting Images of Multiple Meteorological Elements

Photovoltaics (PV), which is one of variable renewable energies, have been installed largely. In the environment of largely installed PV, the output fluctuates of PV influences on the operation of power gird network adversely. Therefore, PV energy generation forecasting is valid for power grid operation and management and needs the reduction of large forecast error as well as average error. Due to a day‐ahead forecast of the total PV energy generation in the power grid area, we developed the forecasting method by using meteorological forecast data of many points in the target area. In order to forecast the regional PV energy generation, the proposed method uses auto‐encoder and convolutional neural network (CNN) for extracting requirements and valid data, and forecast with neural network (NN) by means of extracted information. In this paper, we improved the forecasting error by using multiple meteorological elements as input data. As a result, we indicated that using both of global horizontal irradiance and low cloud cover is required for the reginal PV energy generation forecasting, and large forecasting error can be reduced by adding either dew point depression, or cumulative precipitation as input data. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Design and management of photovoltaic energy in uninterruptible power supplies

The growing demand for sustainable systems due to climate change has led to increased reliance on renewable energy sources. However, this transition has raised concerns about power quality in power systems due to climate variations and the intermittent nature of renewables, photovoltaic energy generation in particular. In this context, uninterruptible power supply systems play a crucial role in ensuring reliable and high-quality energy supply. As an added benefit, photovoltaic energy generation may be integrated into uninterruptible power supply systems by sharing the inverter already present and storing generated energy in the batteries. In this paper, it is presented the design and management of photovoltaic energy, integrated into double-conversion uninterruptible power supplies. A method for selecting the suitable number of panels to be used in series in a given application is developed, considering the panel model used, and the environmental mission profile. A management algorithm for tracking the maximum power point of the photovoltaic generation while ensuring the hold-up time is proposed, introducing the dynamic hold-up time concept. Using the point for maximum power instead of the dynamic hold-up time at two different locations leads to voltage sags of 12.6% in the first and 24.3% in the second. Using the method to select the number of panels in series, 13 panels are selected for the first location, resulting in an annual generation that is only 2.3% smaller than the maximum power point. The results are validated using a hardware-in-the-loop platform and the dSpace Microlabbox 1202.

Remote monitoring and analysis of productivity indicators of photovoltaic energy generation systems

Remote monitoring and analysis of productivity indicators of photovoltaic energy generation systems

Hybrid KNN-SVM machine learning approach for solar power forecasting

Predictions about solar power will have a significant impact on large-scale renewable energy plants. Photovoltaic (PV) power generation forecasting is particularly sensitive to measuring the uncertainty in weather conditions. Although several conventional techniques like long short-term memory (LSTM), support vector machine (SVM), etc. are available, but due to some restrictions, their application is limited. To enhance the precision of forecasting solar power from solar farms, a hybrid machine learning model that includes blends of the K-Nearest Neighbor (KNN) machine learning technique with the SVM to increase reliability for power system operators is proposed in this investigation. The conventional LSTM technique is also implemented to compare the performance of the proposed hybrid technique. The suggested hybrid model is improved by the use of structural diversity and data diversity in KNN and SVM, respectively. For the solar power predictions, the suggested method was tested on the Jodhpur real-time series dataset obtained from the data centers of weather stations using Meteonorm. The data set includes metrics such as Hourly Average Temperature (HAT), Hourly Total Sunlight Duration (HTSD), Hourly Total Global Solar Radiation (HTGSR), and Hourly Total Photovoltaic Energy Generation (HTPEG). The collated data has been segmented into training data, validation data, and testing data. Furthermore, the proposed technique performed better when evaluated on the three performance indices, viz., accuracy, sensitivity, and specificity. Compared with the conventional LSTM technique, the hybrid technique improved the prediction with 98 % accuracy.

Annual Forecast of Photovoltaic Power Generation Based on MLP Artificial Neural Networks

The intermittency of solar energy resources presents a significant challenge in balancing power generation and load demand. To enhance system consistency, forecasting photovoltaic solar energy is crucial. Among numerous techniques, Artificial Neural Network (ANN) is an efficient tool that can help simplify this problem and predict photovoltaic power generation based on various inputs such as weather data and panel characteristics. In this paper, we present the results of an annual forecast of photovoltaic power generation based on Multilayer Perceptrons (MLP), which provides valuable insights into the potential of MLP ANN for accurate and reliable prediction of photovoltaic power generation, thereby improving the efficiency and reliability of photovoltaic systems. The results were obtained based on data collected over a year and validated with data from the following year. Mean Squared Error (MSE) was utilized to quantify the error between the predicted and measured photovoltaic solar energy generation. The analysis demonstrated that this annual forecast of photovoltaic power generation is highly accurate.

Prototype of a sustainable photovoltaic energy generation system in the Manzanillos Zitácuaro community

Prototype of a sustainable photovoltaic energy generation system in the Manzanillos Zitácuaro community

Framework of Maximum Solar Energy Conversion Using Lyapunov‐Based Model Reference Adaptive Control Technique in Stochastic Weather Conditions

The solar photovoltaic (PV) energy generation technology has grown rapidly. Herein, a novel continuous‐time Lyapunov‐based model reference adaptive control (LB‐MRAC) technique is suggested for determining the duty cycle of boost converter to maintain maximum power point (MPP) of the solar PV panel. For ensuring the fast‐tracking speed, the reference voltage of, respectively, MPP is calculated using perturb and observe (PO) and input to the MRAC technique. Using MATLAB/Simulink, the performances of the LB‐MRAC maximum power point tracking are comparatively analyzed with PO, incremental conductance, and adaptive neuro‐fuzzy inference system under diverse testing modes such as stand‐alone, partial shading, grid integrated, and real time. The proposed technique tracks MPP in just 3.7 ms having tracking efficacy between 99.15% and 99.59% under highly fluctuating irradiance and temperature with load uncertainties. In partial shading mode, four patterns (3 × 1 PV string) are devised and determined where that the suggested approach monitors the global MPP in merely 0.04 s with the highest power tracked and the least amount of shading loss. Next, the efficacy is checked in a three‐phase 50 kW grid‐integrated mode under realistic weather. Finally, the performance of the suggested continuous‐time LB‐MRAC technique is experimentally corroborated employing OPAL‐RT(OP4510) in the real‐time mode.

Evaluating the potential of wind and solar energy in achieving zero energy ratings in residential homes: A Nottingham case study

This research explores the potential for reducing reliance on fossil fuels through the combined utilisation of photovoltaic energy and local Vertical Axis Wind Turbines (VAWTs). In urban settings, VAWTs offer advantages such as lower noise emissions, independence from wind direction, operational efficiency at both low and high wind speeds, and enhanced stability due to their unique helical blade design. These characteristics make VAWTs a suitable and effective option for generating sustainable energy in urban environments. A Vertical Axis Wind Turbine (VAWT) will be virtually introduced to 2050 Homes Development in Nottingham, a cluster of 27 houses configured to use a micro–Low Temperature District Heating (LTDH) network.This paper investigates the possibility to move 2050 Homes scheme into zero energy level by using Quite Revolution (QR6) helical blade VAWT along with photovoltaic energy generation. The preliminary results show that two QR6 VAWT can bring the 27 terrace homes from the 2050 Homes scheme into zero energy class performance.

Agrivoltaic arrays can maintain semi-arid grassland productivity and extend the seasonality of forage quality

The co-location of photovoltaic energy generation and agricultural land use (Agrivoltaics, AV) has become increasingly popular in recent years. Although the benefits of AV in croplands have great promise, the development of AV systems has primarily occurred in former grasslands and sites now managed as grasslands, because of their relatively flat topography and consistently high solar irradiation. Evidence is accumulating that grassland productivity can be maintained within solar arrays, but how grassland productivity responds to grazing within solar arrays is largely unknown, despite the prevalence of grazing as a vegetation management option. Here, we report the results of a study aimed at quantifying how a semi-arid C3 grassland growing beneath an AV system in Colorado (USA) responded to simulated grazing treatments (canopy removal in June or July). In the absence of simulated grazing, there were no differences between aboveground primary production in the AV grassland vs. an adjacent control grassland. However, simulated grazing in June and July had a compensatory effect and, in some cases, annual productivity exceeded that in the control grassland. Additionally, we found that simulated grazing increased forage protein content later into the growing season compared to un-grazed AV and control sites. Overall, our results indicate that grazing within a grassland AV array is unlikely to negatively impact forage production, and that forage quality in this semi-arid region may even be increased later into the growing season with grazing.

A Comparative Study of the Performances of the LQR Regulator versus the PI Regulator for the Control of a Battery Storage System

Background: This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a linear quadratic regulator (LQR) combined with an artificial neural network (ANN) algorithm. A state representation of the Buck-boost converter is performed. Then the ANN-LQR control strategy is compared to a classical control based on the proportional-integral controller combined with an ANN algorithm. The ANN algorithm generates the reference charging or discharging current based on a comparison between the power generated and the power consumed. In order to obtain an accurate comparison, two identical systems are designed, each consisting of a photovoltaic system optimized by an incremental conductance algorithm (INC) that powers a dynamic load and a backup storage system consisting of a lithium-ion battery. A management and protection algorithm is developed to protect the batteries from overcharge and deep discharge and to manage the load availability on the DC bus. The simulation results show an improvement in the performances of the storage system by the ANN-LQR control compared to the ANN-PI method and an increase in the stability, accuracy, efficiency of the system is observed. : Photovoltaic (PV) energy is one of the most promising technologies for combating climate change and meeting the urgent need for green renewable energy and long-term development. PV energy generation has a number of advantages: Solar energy is limitless and available anywhere on the planet. However, photovoltaic energy is intermittent and depends on meteorological conditions; also, the energy consumed is unpredictable. For this reason, a storage system is necessary to overcome these problems. Objective: The objective of this study is to develop an optimal control using a Linear Quadratic Regulator (LQR) combined with a neural network algorithm (ANN) to improve the performance of an electrical energy storage system and compare the results obtained with the classical control based on the PI regulator. Methods: The state representation of the bidirectional Buck-boost converter was performed in order to apply the optimal control and determine the gain K and the ANN algorithm allowed to determine the charge and discharge current according to a comparison between the power produced and consumed. Results: The simulation results obtained by two control methods can be used to compare and select the appropriate control method to achieve optimal efficiency of the storage system. Conclusion: The combined ANN-LQR technique offer better performances and stability of the installation compared to the ANN-PI controller.

Optimal Photovoltaic Array Layout of Agrivoltaic Systems Based on Vertical Bifacial Photovoltaic Modules

An agrivoltaic (APV) plant is a complex system, where photovoltaic (PV) energy generation is concomitant with agricultural production. These activities can be antagonistic as the presence of PV installation might reduce the favorable conditions for agriculture and vice versa. In this context, vertical bifacial PV (AbPV) farms represent a fascinating perspective to optimize the land's dual‐use request. This means finding geometrical configurations for the AbPV plant to produce the same energy as ground‐mounted PV farms, not hinder agricultural activities, and limit the photosynthetically active radiation deficit. This study evaluates the performances of a vertical bifacial APV plant, located in Sicily, as a function of the PV modules’ plan allocation. The analyses developed through two software tools, SAM and PVsyst, compare the relative capabilities of monofacial and vertical bifacial farms in terms of acceptable PAR debit, and energy yield as a function of the array density. The land equivalent ratios of vertical AbPVs from a maximum value of 1.93 (p/h = 2.5) and a minimum of 1.56 (p/h = 5.0) for crops which have low sensitive response to the drop of solar radiation, while from 1.71 and 1.42 for crops which have higher sensitive response to the drop of solar radiation.

Towards Efficient Energy Usage at Ain Shams University Campus

In the light of global energy transition to renewable resources and energy efficiency usage, Ain Shams University (ASU) developed an ambitious plan to transform its campus into Green Campus. From an energy perspective, energy consumption data were continuously collected and audited to calculate the university campus carbon footprint. An energy usage strategy was established to tackle various pillars such as electrifying the campuses’ transportation system, improving energy efficiency usage, generating Renewable Energy (RE) for self-consumption, etc. Extensive research has been initiated on electric vehicles, wind and solar Photovoltaic (PV) energy generation with students’ activities/competitions. Thus, electric cars and buses were manufactured at the Faculty of Engineering (FoE) for elderly people and staff movement in ASU campus. Solar PV lighting poles with batteries were installed in the main campus. A small-scale Wind Turbine (WT) is manufactured and installed at the FoE and a pilot solar PV system is installed as well. Currently, an energy efficiency project is under implementation in various buildings/faculties and a parking lot that targets energy efficiency and solar PV energy generation. An energy efficiency measure is under implementation through replacing lamps with LED lamps, installing motion sensors, setting up a control center for monitoring and operation that is supported by Artificial Intelligence decision making algorithms. Rooftop solar PV energy systems are under design with smart meters. The project is targeting energy saving and bill reduction by at least 30% and as a result a reduction of carbon footprint will be achieved following the COP27 recommendations.