Solar Photovoltaic Power Plant Research Articles

Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review

The world is moving forward to a transition in the form of increasing the contribution of renewable energy sources in the energy sector, and among these, solar photovoltaic-based power generation is catching pace. Several factors are responsible for the lowering of outputs due to different degradation causes such as hotspots, corrosion, humidity, ultraviolet (UV) irradiation, temperature effects, dust, aging, weathering, yellowing, snail trails, discoloration, junction box failure, delamination, cracks, and faults from the solar photovoltaic (PV) plants. This paper presents a comprehensive review of the various form of degradation and their implications on solar PV power plant performance. The review has been carried out considering the different degradation causes and their identification methods in solar PV plant. Further, the analysis has been done on the basis of the earlier studies to understand the rates of degradation for various solar PV power plants in various climatic conditions. The PV technologies used in solar power plants are also responsible for the change in the performance of power plants over time; therefore, degradation based on different solar PV cell technologies is also analyzed. The visual inspection tools like thermal imaging with IR cameras help identify areas with abnormal heat patterns, indicating potential issues like cell or interconnect failures, loose electrical connections, or bypass diode malfunctions while EL cameras are used to identify low-level electrical excitation and defects such as cracks, hotspots, and cell-level degradation.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring

Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering.

Effect of Platform Configurations and Environmental Conditions on the Performance of Floating Solar Photovoltaic Structures

The growth and development of floating solar photovoltaic (FPV) power plants is a prominent topic within renewable energy technology. One reason contributing to this desired technology design concept is the possibility of land acquisition issues, whereas the usage of the ocean provides a greater technical alternative area. The objective of the research is to present an innovative design for a floating structure, focusing on investigating and comparing the seakeeping performance of several hull configurations: catamaran, trimaran, quadrimaran and pentamaran. The final computational simulation results indicate a linear negative trend in the motion response graphs, particularly in specific significant response values for heave (Global Z), roll (Global RX), and pitch (Global RY), as the hull configuration increases.

Comparative Numerical Energy and Performance Analysis of Solar Photovoltaic and Solar Thermal Power Generation

This study analyses fixed and tracking solar photovoltaics and, solar thermal power. Performance in different locations was analysed for each configuration and technology using simulations based on a Typical Meteorological Year data. The study analysed a 25MWp solar photovoltaic and solar thermal power plants in each of the eleven selected locations in Zimbabwe. The performance of the solar photovoltaic and solar thermal power plants under different meteorological variables were assessed for all the selected locations. It was shown that different configurations together with different technologies have different conversion efficiencies. A high solar thermal conversion efficiency was found to be 18.718% in Gweru while it was 15.502% for solar photovoltaics in Mutare. The study also showed that highest insolation and clearness index values were found in Gweru. The average energy generated by the fixed photovoltaic collectors, tracking photovoltaic collectors and the Concentrating Solar Power plant were respectively 47.38GWh, 68.18GWh and 192.86GWh. There was a maximum percentage difference in the LCoE generated of 32.03% between the fixed PV collectors and the Concentrating Solar Power plant and a difference of 4.74% was realised between the tracking photovoltaics and Concentration Solar Power.

A GIS-based catastrophe approach for optimal site selection for installation of solar power plants, East Azerbaijan province case study, Iran.

Due to depletion of fossil fuels and environmental issues, renewable energy consumption is increasingly growing. Solar energy as the most abundant renewable energy source available is becoming more popular around the world. In the current study, the optimal sites for solar photovoltaic power plants in East Azerbaijan province, Northwest Iran, were investigated. A total of 17 variables were categorized into four groups: climatic, geomorphological, environmental, and access-economic. In order to integrate the variables, a model based on catastrophe theory in the context of GIS was applied. The relative importance and weight of the criteria are computed based on the internal mechanism of the catastrophic system, thus greatly reducing subjectivism and uncertainties of the decision-making process. Five optimal sites located in the western part of the province within the counties of Malekan, Bonab, Ajabshir, Shabestar, and Tabriz were identified as suitable sites for the construction of solar photovoltaic power plants, where there are ideal conditions in terms of many environmental-human variables such as high potential of solar energy, high sunshine hours, low relative humidity, suitable slope, poor vegetation, distance to protected areas, proximity to the population centers, excellent access to the roads and to the main power lines.

PVsyst and Homer for the Evaluation of a Project to Size a Photovoltaic Solar Power Plant in Reinforcement of the CEET Network in Lomé in Togo by Characterization of the Electrical Energy Consumed

PVsyst and Homer for the Evaluation of a Project to Size a Photovoltaic Solar Power Plant in Reinforcement of the CEET Network in Lomé in Togo by Characterization of the Electrical Energy Consumed

Jellyfish search algorithm for optimization operation of hybrid pumped storage-wind-thermal-solar photovoltaic systems

This study applies Jellyfish Search Algorithm and five other algorithms to minimize the electricity generation cost of two hybrid systems for one operating day. The first system comprises one pumped storage hydroelectric plant and two thermal power plants. The second system is expanded by integrating one wind and one solar photovoltaic power plant into the first system. For each system during one operating day, the pumped storage hydroelectric plant with only generation mode acts as a conventional hydroelectric plant in the first scenario. Still, it can run pumps to store water and produce electricity in the second scenario. As a result, JSA can reach smaller costs than all compared algorithms, from about 1 % to higher than 10 % for two scenarios in the two systems. The comparisons of generation cost indicate the second scenario with the pumped storage hydroelectric plant can reach a smaller cost than the first scenario with the conventional hydroelectric power plant by $53,359.7, corresponding to 7.4 % in the first system and $39,472.8, corresponding to 6.95 % in the second system. Therefore, the water storage function of the pumped storage hydroelectric plant is very effective in reducing the electricity generation costs for hybrid power systems.

PERANCANGAN JARINGAN DISTRIBUSI UNTUK PLTS TERAPUNG DAN PLTMH DI BENDUNGAN SIDAN

Blahkiuh Feeder is a 20 kV radial distribution network spanning 78.65 kms, which is the longest network in the Mengwi Network Unit. The length of the distribution lines also results in relatively high power losses. Therefore, the installation of distributed generation (DG) utilizing the potential of solar photovoltaic (PV) and micro-hydro power plants (MHP) available at the Sidan Dam has the potential to improve voltage drop and power loss values on the Blahkiuh Feeder. This research was conducted to analyze voltage and power losses due to DG installation at the Sidan Dam, with power flow simulation during peak daytime load at 14:00 and peak nighttime load at 19:00. Simulation results indicate that when interconnected with the daytime peak load at 14:00, DG interconnection results in a decrease in power loss values by 0,8% or 64 kW, and interconnection with the nighttime peak load at 19:00 also leads to a decrease in power loss values by 0.61% or 55 kW. Distributed Generation interconnection on the Blahkiuh Feeder also leads to improvements in voltage values, where interconnection during daytime at 14:00 results in voltage improvement at 41 busbars units under undervoltage condition, and interconnection during night time at 19:00 leads to voltage improvement at only 34 busbars units undervoltage condition out of a total of 50 undervoltage busbars.

Demarcation of suitable site for solar photovoltaic power plant installation in Bangladesh using geospatial techniques

The major goal of this research is to adopt analytical hierarchy process (AHP) based geospatial technique to select suitable zone for the solar photovoltaic (PV) power plants. Nine thematic layers altogether—slope, global horizontal irradiation (GHI), relative humidity, direct normal irradiation (DNI), elevation, distance from major roads, distance from protected areas, rainfall, and land use/land cover (LULC)—are combined through overlay analysis in ArcGIS to create the final map of suitability for the placement of solar photovoltaic (PV) power plants in Bangladesh. This map has been classified into five categories namely, restricted zone, less suitable zone, moderate suitable zone, good suitable zone, and excellent suitable zone. These categories are covered by 7.28%, 16.61%, 28.51%, 27.77%, 21.83% land of total area in Bangladesh respectively. The findings of this research have been presented that ‘the excellent suitable’ and ‘good suitable’ areas for the construction of solar power plants are in the western and northwestern part (Rajshahi, Pabna, Sirajganj, Natore, Naogaon, Chapainawabganj, Bogura, Faridpur, Jessore, Jehenaidha, Magura, Kushtia, Choudanga, Meherpur) of the study area which contain a high value of global horizontal irradiation, direct normal irradiation, elevation and low value of slope, rainfall, temperature, relative humidity. Besides the restricted and less suitable zone for installing solar photovoltaic (PV) power plants indicates a high value of rainfall, slope, temperature, relative humidity and low value of global horizontal irradiation, direct normal irradiation, and elevation. Bangladesh's currently operational solar plants were taken into consideration for this study's validation purposes. The proposed framework may potentially be used in different locales on a national and worldwide scale. This study offers a consistent GIS process for the accurate, inexpensive implementation of a solar energy plan to achieve environmentally friendly goals.

Techno-economic feasibility study of solar photovoltaic power plant using RETScreen to achieve Indonesia energy transition

Indonesia, a key player in the global energy transition, faces surging electricity demand and ambitious renewable energy goals. In response, the government introduced a new regulation about renewable energy tariffs, including tariffs for photovoltaic (PV). However, there remains a gap in the academic literature regarding PV power plant feasibility studies under these tariffs. To address this gap, this study investigates the feasibility of a utility-scale solar photovoltaic (PV) power plant in Indonesia, focusing on the newly implemented renewable energy tariffs based on Independent Power Producers (IPPs) and Indonesia's state-owned electricity company (PLN) perspectives. Five scenarios were developed based on the proposed 26 MW solar power plant on Nias Island utilizing RETScreen software. The results showed that based on the IPP perspective, the newly implemented renewable energy tariff was inadequate to make the project feasible, however, an introduction of a 10 USD/t CO2 emission incentive would make the project financially viable for IPPs. Therefore, it is recommended to introduce emission incentives as a strategic approach to attract investors and stimulate investment in Indonesia's PV power plants market, to accelerate Indonesia's energy transition. Conversely, the results also showed that the project is very profitable for PLN due to the significant cost-savings from the de-dieselization, leading to a reduction in the average generation cost for Nias.

Optimization of Optical Efficiency Modeling and Heliostat Field Layout of Tower Solar Thermal Power Station

The aim of this paper is to study and optimize the layout of heliostat mirror fields in solar photovoltaic power plants to improve energy efficiency. By analyzing the key factors of optical efficiency, the optical efficiency model is constructed. The optical efficiency of a single heliostat mirror is calculated using five efficiency factors, namely, shadow shading efficiency, cosine efficiency, atmospheric transmittance, collector truncation efficiency, and mirror reflectivity. The DELSOL layout is used as the initial mirror field, and the mirror field design is optimized by the particle swarm algorithm. The optimal design parameters of the heliostat mirror field including the position of the absorption tower, the size of the heliostat, and the installation height were calculated by adjusting the positions of the heliostat and the absorption tower many times. The results show that the annual average optical efficiency of the heliostat field is 0.6127, and the annual average thermal power output is 37.4508 MW. Through optimization, the annual average optical efficiency of the heliostat field is increased to 0.75, and the annual average thermal power output reaches 64.98 MW. In addition, the results show that the heliostat field has a seasonal characteristic. The optical efficiency and the annual average thermal power output change monthly.

Assessing the Performance of Off-Grid Solar Photovoltaic Power Plants in Supporting Aquaponics Systems

In an aquaponics system, the electricity produced by photovoltaic solar power (PLTS) is stored in batteries and used to power the water pumps. Using natural microorganisms to transform fish waste into plant nutrients, aquaponics is a cultivation method that blends fish and plant cultivation. The aquaponics system helps reduce the amount of trash released into the environment and is beneficial to the environment. The main goal of this study is to evaluate an off-grid PLTS system's functionality as a DC water pump power source in an aquaponics setting. This study's goal is to evaluate how well the PLTS operates when powering the DC water pump that circulates water in the aquaponics system.According to performance tests, the off-grid PLTS generates an average energy of 503.8 Wh, which is enough to power the DC pump for 20 hours a day of water circulation. The water pump uses only 382.6 Wh of electricity each day. With an average solar panel efficiency of 4.97%, an average solar charge controller (SCC) efficiency of 60.51%, and an average pump efficiency of 79.91%, the Off-grid PLTS demonstrates efficiency. With a daily solar irradiation of 7.8 kWh/m2, the average energy loss in the solar panel is 5.29 kWh/kWp, whereas the average energy loss in the system is 0.10 kWh/kWp. All things considered, these results provide insight into how well the Off-grid PLTS functions as a power source for the DC water pump in an aquaponics system.

Computer Aided Design and Simulation based development of floating solar resort

The presented article introduces Computer Aided Design (CAD) and Simulation to develop a Floating Solar Photovoltaic (FSPV) power plant based resort over Nasser Lake. This research framework aims to achieve multi criteria performance improvements in technical, economic, and environmental aspects. The first target is to improve the efficiency of the solar panels by decreasing the operating temperature especially in hot regions of south Egypt. In addition, this design is a protection technique against PV inverter’s fire and considers the prolongation of FSPV station life span. The third target is to decrease the evaporation and algae bloom of the lakes where it is proposed to install it over Nasser Lake in upper Egypt to protect the water reservoir behind the high dam. To avoid using lithium-ion battery for energy storage, we rely on Magnetic Energy Generators (MEG) to generate the electricity after the sundown which make this design a unique one. This design employs the Magnetic Energy Generators (MEGs) to cover the power generation at night instead of using expensive short life span lithium-ion battery storage for this hug power. This study is presented to support the renewable energy resources and water availability for Egypt compared with similar relative studies carried out worldwide.

Clustering-based assessment of solar irradiation and temperature attributes for PV power generation site selection: A case of Indonesia’s Java-Bali region

This study presents clustering-based assessments of solar attributes for locating potential solar photovoltaic (PV) power plant sites using k-means and density-based spatial clustering of applications with noise (DBSCAN) by examining the yearly average single-attribute and three-attribute clustering on a dataset of long-term hourly-based direct and diffuse irradiation, ambient temperature, and solar PV power output from 2005 to 2022. Three-attribute clustering enables stakeholders to better understand the characteristics of a cluster by collectively identifying three solar attributes and the magnitude of each attribute in an area or cluster. The presence of this information, which constitutes the clusters, suggests that these attributes have different effects on solar PV output power in different clusters. Although k-means is an effective method for investigating potential locations for PV power plant placements, DBSCAN offers users an alternative method for accomplishing a similar goal. In the case of three-attribute clustering of direct irradiation with k-means and DBSCAN, the 18-year mean value of clusters with the highest yearly average value is achieved at very similar values of 0.305 kW/m2 and 0.310 kW/m2, respectively. It turns out that only six years of direct irradiation had an annual mean value of less than 0.305 kW/m2. This finding implies that in the long run, the solar resources in terms of direct irradiation will typically surpass 0.3 kW/m2/MW installed capacity over all areas suitable for PV power plants. While focusing on the Java-Bali region, Indonesia, the findings, and methods appear to be of broader interest to policymakers, particularly in developing countries where solar PV is considered an option for sustainable energy generation.

Method for minor defect detection in electroluminescent solar cells based on CSR-YOLOv5s

The increasing production of solar cells, resulting from the rapid development of new energy sources, necessitates their inspection during both solar cell production and photovoltaic power plant inspection. Target detection algorithms are widely utilized for defect detection in solar cells. To achieve more accurate detection of minor defects in electroluminescent solar cells, an improved algorithm called CSR-YOLOv5s is proposed in this paper. The CSR-YOLOv5s combines Decoupled Head and CSRBlock with the YOLOv5s baseline model. The CSR-Y OLOv5s demonstrates a 1.1% increase in accuracy and a 2.1% increase in F1-score compared to the YOLOv5s baseline model, resulting in improved accuracy and recall. The algorithm effectively identifies minor defects in electroluminescent solar cells.

Comparison of Results from Two Multi-Criteria Decision-Making Methods for Solar Photovoltaic Plant Site Location: Case Study Rio De Janeiro

Photovoltaic (PV) energy has become a low-cost, renewable, and environmentally friendly alternative to meet increasing energy demand. Nevertheless, there is still a lack of projects in this field in Brazil. Therefore, this study compares the results of two studies on the optimal site selection of PV in the Brazilian state of Rio de Janeiro. These studies used different methodologies to reach the conclusions and the resulting map. First, the final map of both studies was divided into a grid, and then the results of each cell were weighted for PV site selection. To compare the results using the maps, an intersection of the 10% of the grid cells with the best results from each study was formed. The results showed an 83% similarity between the different Multi-Criteria Decision-Making (MCDM) methods. The other part of the comparison focused on the following rank similarity coefficients: Spearman Correlation Coefficient, WS Coefficient, Spearman Weighted Correlation Coefficient, and Blest Correlation Coefficient. All these coefficients had values greater than 0.9, indicating a high degree of correlation between the results of the studies. Therefore, the two studies have a high degree of similarity and a high potential for installing photovoltaic solar power plants in Rio de Janeiro, especially in its intersection zones.

The Potential of Renewable Energy to Replace Diesel Power Plants in Supporting Energy Transition in Indonesia

The Indonesian government is committed to reducing greenhouse gas emissions under the Paris Agreement. One form of this commitment is the program to replace diesel power plants with renewable energy plants in the electricity sector. Fossil-based diesel power plant operations have disadvantages, such as relatively expensive generation costs and greenhouse gas emissions. There are three renewable energy technology options as potential substitutes for diesel power plants: a solar photovoltaic (PV) power plant integrated with a battery energy storage system (BESS), a small-scale geothermal power plant, and a micro-hydropower plant. This paper will analyze the prospects of each technology option based on its economic and environmental impact. Various technical parameters, including renewable energy potential and intermittent factors, as well as economic parameters such as investment and generation costs, are important factors for selecting prospective renewable energy generators. The analysis was carried out by calculating the levelized cost of electricity (LCOE), CO2 emissions, and oil fuel consumption for four different scenarios. The results of the analysis show that replacing the diesel power plants with micro-hydropower plants can reduce LCOE by 0.24 USD/kWh to 0.12 USD/kWh. The use of a solar PV power plant integrated with a BESS has the most potential to reduce CO2 emissions, which account for 54% of emissions from diesel power plants. Meanwhile, the potential for oil fuel reduction from this program can reach an annual average of 0.85 million kl.

Soiling impact and cleaning techniques for optimizing photovoltaic and concentrated solar power power production: A state-of-the-art review

Nowadays, renewable energies are capturing the world's attention, particularly in light of the phenomenon of climate change and carbon dioxide emissions, which have caused major environmental damage. As a result, many investors have recently focused on developing investments in renewable energy projects worldwide, specifically photovoltaic and concentrated solar power plant projects. These solar technologies are considered among the most profitable solutions for generating power from a natural, free, and unlimited energy source. This review paper discusses one of the most significant issues affecting the performance of these solar systems, which is known as soiling. It has been supported by several studies in various nations with different climatic conditions, which offered accurate empirical data on the degradation rate of photovoltaic and concentrated solar power systems’ production due to the soiling effect. Furthermore, it provides various mitigating soiling ways, including manual and autonomous cleaning methods for both solar technologies. Ultimately, it summarizes each cleaning technique's main advantages and drawbacks, specifying its applicability according to the location characteristics and climatic conditions. Additionally, the review results reported in this work are intriguing enough to warrant further development of concentrated solar power and photovoltaic technologies.

Calculation of optical efficiency of the heliostat field based on Reverse Projection Box Counting Model

A more accurate and reasonable calculation model is proposed for the calculation of total optical efficiency, shadow blocking efficiency, cosine efficiency, atmospheric transmittance, collector truncation efficiency and output thermal power of heliostat field in tower solar photovoltaic power plant. In this paper, under the condition that the relevant parameters of heliostat field are determined, the Monte Carlo Ray Tracing method is used as the basis for the calculation of each optical efficiency. At the same time, the innovative Reverse Projection Box Counting Model is established to accurately identify whether there is a shadow blocking problem or not, and the differential idea is used to effectively solve the problem of repeated calculation of the blocking shadow area and the irregularity of the blocking area difficult to be calculated caused by one heliostat being blocked by multiple heliostats, so as to estimate the shadow blocking efficiency of the heliostat field more accurately. Finally, the heliostat field in Gansu, China, is taken as an example for calculation, and the results are compared with those of other scholars and the real value of the heliostat field to prove the superiority of the model constructed in this paper and the accuracy of the solution results.