Photovoltaic Plants Research Articles

Modelling and experimental investigation of cooling of field-operating PV panels using thermoelectric devices for enhanced power generation by industrial solar plants

The performance of commercial solar power plants degrades due to an increase in module temperatures for which standard PV-T air or water-cooling techniques are mostly used. In this study, a thermoelectric cooling system is studied for improving photovoltaic cell power efficiency and hence solar power generation. The cooling optimization requires solar cell temperature prediction of field operating PV modules, for which analysis of six models, is presented. The experimentation results show that TEC cooling maintains PV cell at 25 °C whereas PV cell without TEC operates at 55–63 °C, a higher temperature range, showing the effectiveness of the thermoelectric cooling system in precisely controlling PV cell temperature to operate at or near STC conditions in the field creating a temperature difference of 30–38 °C. The NOCT and Faiman model results are found close to the experimental values in comparison to other models. The potential for cooling and a corresponding increase in solar plant energy production is assessed using PV Syst modeling and simulation for three practical PV installation scenarios for 31 different climatic zone locations worldwide showing 6–27 % power loss due to elevated temperatures, which is not studied in previous studies adding novelty to the analysis. The results show that PV-TECS is an effective system to control the temperature of field operating PV modules, which can be used in future photovoltaic power plants. Field results and analysis of PV temperature models is crucial for the optimization and future development of PV-thermoelectric systems deployed under actual outdoor conditions as well as the expected cooling gains in different climatic locations. These aspects are collectively studied in the current work adding to the novelty of the study.

Thermographic inspections of solar photovoltaic plants in India using Unmanned Aerial Vehicles: Analysing the gap between theory and practice

Aerial inspection of solar PV plants using Unmanned Aerial Vehicles (UAVs) is gaining traction due to benefits such as no downtime and cost-effectiveness. This technology is proven to be the low-cost alternative to conventional approaches involving visual inspection and I-V curve tracing to identify physical damages and underperforming strings, respectively. Though the use of UAVs for thermographic solar PV inspection is a popular alternative in developed countries, its use in developing economies experience various challenges. Studies emphasizing these challenges especially in the context of rapid evolution of drones are limited. To overcome this limitation, literature scoping, a one-on-one survey, focus group discussion, and a flight campaign using a UAV with a thermal payload is conducted in India to identify the limitations. These are further categorized into Technical, Behavioural, Implementation, Pre-deployment, Deployment, and Post-deployment categories. The relevance and significance of each challenge are analysed using a hybrid multi-criteria framework developed in this study. Findings of this study highlight the importance of drone regulations, technology readiness, and workshops for drone pilots, industry professionals, and solar developers in India. This study aid developing economies in devising strategies that can promote the use of UAVs for solar PV plant commissioning activities.

Generated power forecast of dye-sensitized solar plant with deep neural network

ABSTRACT The enormous amount of solar power and its state-of-the-art capturing technology that produces electricity, increases the grid interconnection rate of photovoltaic (PV) plants. Prior knowledge of the aperiodic characteristics of solar energy received by the Earth’s surface is essential for PV plants to ensure reliable operation and stable, secure grid connections. Power generation under diffused sunlight by PV plants with the most widely used first-generation solar cells has significant limitations. A dye-sensitized solar plant (DSSP), utilizes dye-sensitized solar cell (DSSC) technology, remains active in low light conditions. Though the activeness enables such plants to generate electricity throughout the year while receiving diffuse sunlight, unlike conventional solar technologies the plants have a requirement of knowledge of future power generation. This could be the first paper to report on the generated power forecast for such plants. The study has utilized a machine learning approach with a proposed DSSP model for the forecast. Spyder, an open-source integrated development environment, is the test workbench for the experimentation. The results show the robustness and reliability of the method, regardless of the weather conditions in the test area. The DSSP, along with the prediction model, will moderately overcome the shortcomings of power generation under diffuse daylight with intermittent solar radiation and grid connections.

Using GIS Spatial Analysis for Examination the Utility of the Suggested Suitability Energy Farms Over Kirkuk City

Global electricity demand is rising, prompting increased attention to renewable energy sources like solar power. This research focuses on identifying suitable areas for power lines connecting solar farms to distribution stations in Kirkuk City. Data types, including urban areas, industrial zones, farms, water bodies, and archaeological sites, were analyzed using GIS-based spatial tools. The study highlights accessible and inaccessible regions of Kirkuk's electricity transmission infrastructure, providing insights for decision-makers. The analysis categorizes areas as either benefitted or unbenefited in the context of solar farm suitability. An important consideration is the need for additional electrical distribution stations to accommodate energy transfer from these solar farms.

Reducing Data Uncertainties: Fuzzy Real-Time Safety Level Methodology for Socio-Technical Systems

This paper presents the fuzzy real-time safety level (Fuzzy RealTSL) methodology. It aims to address the data uncertainties resulting from a lack of sensors in complex sociotechnical systems and reduce the need for the determination of their safety level in real-time during their operation. To achieve this, the methodology utilizes: (1) safety constraints from STPA (systems theoretic process analysis) analysis and EWaSAP (early-warning-signs analysis process), (2) fuzzy logic as the mathematical backbone to identify the degree of confidence about the occurrence of unsafe system states, (3) a modified centroid point and spread ordering to enable ordering sequences of unsafe system states that can lead to accidents according to how detrimental they are to the system safety. The RealTSL methodology is presented through its step-by-step application to the panel alignment system of a solar park utilizing rotating solar arrays. This paper aims to open a new perspective on the STAMP literature for discussions of uncertainties from a lack of information about the system’s state and to make it easier to measure its safety level. Knowing the safety level of a system in real-time is crucial for the systems in question as it enables proactive risk management and enhances decision-making by providing immediate insights into potential hazards, thus safeguarding against accidents.

Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.

Electrothermal Modeling of Photovoltaic Modules for the Detection of Hot-Spots Caused by Soiling

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots.

Automatic Classification of Defective Photovoltaic Module Cells Based on a Novel CNN-PCA-SVM Deep Hybrid Model in Electroluminescence Images

In today’s world, the rapid development of photovoltaic (PV) power plants has facilitated sustainable energy production. Maintenance and fault detection play a crucial role in ensuring the continuity of energy production. Manual inspection of electroluminescence (EL) images of PV modules requires significant human resources and time investment. This study presents a method for automatic fault detection of PV cells in EL images using hybrid deep features optimized with the principal component analysis (PCA) feature selection algorithm. A lightweight and high-performance model that combines the strengths of convolutional neural network (CNN) architectures is proposed. Firstly, data augmentation techniques are employed due to the imbalance between defective and functional classes in the dataset containing EL images. In experimental studies conducted by integrating the PCA algorithm into MobileNetV2, DenseNet201, and InceptionV3 CNN models, accuracy, precision, recall, and F1-score values of 92.19%, 92%, 90% and 91%, respectively, were achieved. When the results were analyzed, it was observed that the proposed method had an effective performance in detecting faults in PV panel cells.

Short-Term Hydro-Wind-PV peak shaving scheduling using approximate hydropower output characters

With the massive construction of wind and photovoltaic (PV) power plants, the uncertainty of their output poses challenges for grid peak regulation. Hydropower, characterized by convenient regulation, fast response speed, and low cost, is an ideal choice for compensating for wind and PV energy generation. Mixed-Integer Linear Programming (MILP) is employed to work out short-term scheduling of hydro-wind-PV power. However, considering hydropower scheduling alone is already highly complex, and incorporating the deviations in wind and PV energy further reduces the solution efficiency. In practical scheduling, operators often rely on empirical scheduling methods that can yield satisfactory results. To enhance solution quality and improve the scalability and efficiency of the MILP method, a short-term optimization scheduling model for hydropower plants has been proposed and applied to the Hongshui River cascade system. The case study shows that the model can perform calculations in just 2 s, demonstrating extremely high computational efficiency. This can significantly enhance decision-making in cascaded hydropower scheduling. At the same time, the model leaves sufficient reserve capacity to address the uncertainty of wind and solar power output while reducing the residual load by 0.8 %, which is beneficial for the stable operation of the grid.

Assessment of the ecological and environmental effects of large-scale photovoltaic development in desert areas

Photovoltaic development has played a crucial role in mitigating the energy crisis and addressing global climate change. However, it has also had significant impacts on the ecological environment. To ensure the sustainable growth of the photovoltaic industry, it is essential to establish an indicator system to assess the ecological and environmental effects of photovoltaic development. This study utilizes the Driving-Pressure–Status–Impact-Response (DPSIR) framework to create an indicator system for evaluating the ecological and environmental effects of desert photovoltaic development. The study evaluates the ecological and environmental effects at the on-site (WPS), transitional zone (TPS), and off-site (OPS) areas of the Qinghai Gonghe Photovoltaic Park in China. The entropy weight method was utilized to calculate indicator weights, while the evaluation model and indicators were transformed uniformly to obtain standardized scores for ecological and environmental effects. and conducting a thorough analysis of the distribution characteristics and factors influencing the evaluation indicators’ scores. Overall, the large-scale development of desert photovoltaics in Gonghe County has had a positive impact on the ecological environment. The WPS had better ecological and environmental conditions than did the TPS and OPS, and the ecological and environmental evaluation levels of the WPS were categorized as “general” (0.439), while the ecological and environmental effect evaluation levels of the TPS (0.286) and OPS (0.28) were both “poor”, indicating significant room for improvement. Moreover, all indicators in the scheme layer, which are used to evaluate ecological and environmental quality, yielded higher scores for the WPS than for the TPS and OPS, demonstrating that photovoltaic development has a positive effect on desert area ecology and the environment.

Operating Limits and Control Variables in Photovoltaic Solar Plants with a Net Effective Capacity of 5 MW Connected to the SIN in Colombia

Operating Limits and Control Variables in Photovoltaic Solar Plants with a Net Effective Capacity of 5 MW Connected to the SIN in Colombia

Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies

Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies

Coordinated control algorithm of hydrogen production-battery based hybrid energy storage system for suppressing fluctuation of PV power

Photovoltaic (PV) power generation has issues of volatility and intermittency. Currently, PV plants are generally equipped with 10% rated capacity lithium-ion (Li) battery energy storage systems in China, who often fail to suppress fluctuation in the output power of PV plants effectively and meet the grid-connected standard. The hybrid energy storage system (HESS) combining with hydrogen production and Li battery system can produce hydrogen by water electrolysis during the peak period of PV power generation, effectively improving PV utilization efficiency, while smoothing PV power fluctuation and improving grid connection electricity quality. Firstly, models of the solid oxide electrolysis cell (SOEC) and alkaline electrolysis cell (AEC) systems for hydrogen production, and Li battery energy storage system are established, and the transient response characteristics of each system are analyzed. Secondly, an adaptive wavelet packet decomposition (AWPD) method for PV power signal decomposition is proposed based on the wavelet packet decomposition (WPD) method. Thirdly, a capacity configuration method for HESS are proposed based on the APWD method. Fourthly, a coordinated control strategy for HESS is proposed with the transient response characteristics of different energy storage systems and the state of charge for Li battery system. Finally, the proposed method is validated through simulation experiments based on the actual power data of the PV plant. The results show that the developed methods can effectively utilize partial PV power generation to produce hydrogen, improve PV utilization, and the combined output power of PV plant and HESS can fulfill the grid-connected standard.

Multi‐Criteria Decision Analysis‐Based Solutions for the Installation of Photovoltaic (PV) Solar Power Plants in an Energy Deficit State of India: An Effort Toward SDG‐7 (Affordable and Sustainable Energy)

ABSTRACTSustainable Development Goal‐7 (SDG‐7) of the United Nations promotes the use of renewable and affordable energy. Solar energy holds significant promise as an effective and affordable renewable source in tropical countries. GIS‐based Multi‐Criteria Decision Analysis (MCDA) approach has been applied to evaluate land suitability for installing ground‐mounted and grid‐connected solar photovoltaic power plants in Purulia district of West Bengal (India) to satisfy the growing demand of energy in an eco‐friendly manner. The study reveals that variation in Global Horizontal Irradiance (GHI) is the most relevant parameter for installing solar PV power plants in the district among the 13 physical parameters, followed by the aspect, slope, and proximity to the grid. The site‐suitable map created using the weights of these factors has revealed that about 33.4% of the district's total land area is ideal for installing solar PV power plants, with most of these areas being in the western and central regions.

Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability.

Design Of PV Plant to Improve a Voltage Profile Of 11 KV Grid in The South West of Libya

The use of photovoltaic (PV) system for electricity generation is growing up in the last decades. Thesouthern part of Libya suffers from voltage fluctuation and intermittence of electricity during thesummer time. The aim of this paper is firstly to study and analysis a distribution grid of small villagelocated in the southern west region of Libya. We analyzed the existed 11KV distribution overheadline supplied electricity of the village called Takarkibah using Power-World Simulator in order tointegrate it with PV plant. The results obtained show that injection of PV power about 70 % of thegrid power capacity gives good grid performance. After that, we use SAM simulation program todesign PV plant to improve the voltage profile in the distribution grid. Six designs (scenarios) aresimulated using different solar modules and inverters. The simulation results obtained from SAMillustrated the power capacity, energy yield, and losses of the designed PV plant.

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

Long-term performance analysis of a large-scale photoVoltaic plant in extreme desert conditions

This study comprehensively evaluates the performance and operational challenges of a 9 MW grid-connected photovoltaic (PV) system in Timmimoun, southern Algeria, after eight years of operation. We identified a significant linear correlation (R2 = 0.73) between increased ambient temperatures and decreased PV module efficiency, underscoring the need for effective thermal management strategies. The investigation also examines potential solutions, such as cooling mechanisms, optimization of panel orientation and tilt angles, and development of temperature-resistant materials. Adhering to the International Energy Agency IEC 61724 standard, our analysis reveals a yield factor of 5.04 h/day, a yield ratio of 7.04 h/day, a performance ratio of 73 %, and a capacity factor of 21 % in 2023. Over its operational lifespan, the system has generated 133.43 GW h of energy, mitigating 1.01 105 tons of CO2 emissions. This long-term study provides critical insights into the performance and reliability of PV systems in hot desert climates, offering valuable guidance for future large-scale solar installations and contributing to the transition towards a sustainable energy future.

Iterative sizing methodology for photovoltaic plants coupled with battery energy storage systems to ensure smooth power output and power availability

Photovoltaic (PV) solar energy is a fundamental technology that will help transition from a fossil fuel–based energy mix to a future with high shares of renewable energy. To do so, PV plants coupled with energy storage systems can accumulate excess power and dispatch it when PV generation changes, performing PV smoothing. While coupling PV plants with battery energy storage systems (BESS) offers a solution, current methodologies often need to thoroughly describe the interplay between BESS energy capacity, power rating, and the long–term impacts of battery degradation. This paper addresses this gap by proposing a four–step methodology that optimizes BESS sizing for PV plants, accounting for both cycling and calendar aging effects on system performance and the economic implications of battery replacements. We use a model that considers the degradation of PV modules and two Li–ion battery types (LiFePO4, LFP, and LiNiMnCoO2, NMC). A 16.3 MW PV plant is simulated along with the BESS to test the methodology. The results indicate that an LFP–based BESS of 2.5 MWh with a rated power of 1.25 MW ensures a stable output power with variation below 10% of the rated PV plant 98 % of the time. In contrast, an NMC–based BESS, while effective in reducing non–compliance, incurs higher costs due to more frequent battery replacements, making it less economically viable. The methodology and results presented in this paper provide valuable insights for designing cost–effective and reliable energy storage solutions in PV plants, ensuring compliance with set power availability.

Potentials of Green Hydrogen Production in P2G Systems Based on FPV Installations Deployed on Pit Lakes in Former Mining Sites by 2050 in Poland

Green hydrogen production is expected to play a major role in the context of the shift towards sustainable energy stipulated in the Fit for 55 package. Green hydrogen and its derivatives have the capacity to act as effective energy storage vectors, while fuel cell-powered vehicles will foster net-zero emission mobility. This study evaluates the potential of green hydrogen production in Power-to-Gas (P2G) systems operated in former mining sites where sand and gravel aggregate has been extracted from lakes and rivers under wet conditions (below the water table). The potential of hydrogen production was assessed for the selected administrative unit in Poland, the West Pomerania province. Attention is given to the legal and organisational aspects of operating mining companies to identify the sites suitable for the installation of floating photovoltaic facilities by 2050. The method relies on the use of GIS tools, which utilise geospatial data to identify potential sites for investments. Basing on the geospatial model and considering technical and organisational constraints, the schedule was developed, showing the potential availability of the site over time. Knowing the surface area of the water reservoir, the installed power of the floating photovoltaic plant, and the production capacity of the power generation facility and electrolysers, the capacity of hydrogen production in the P2G system can be evaluated. It appears that by 2050 it should be feasible to produce green fuel in the P2G system to support a fleet of city buses for two of the largest urban agglomerations in the West Pomerania province. Simulations revealed that with a water coverage ratio increase and the planned growth of green hydrogen generation, it should be feasible to produce fuel for net-zero emission urban mobility systems to power 200 buses by 2030, 550 buses by 2040, and 900 buses by 2050 (for the bus models Maxi (40 seats) and Mega (60 seats)). The results of the research can significantly contribute to the development of projects focused on the production of green hydrogen in a decentralised system. The disclosure of potential and available locations over time can be compared with competitive solutions in terms of spatial planning, environmental and societal impact, and the economics of the undertaking.