Photovoltaic Plants Research Articles

Unveiling the Potential of Ultraviolet Fluorescence Imaging as a Versatile Inspection Tool: Insights from Extensive Photovoltaic Module Inspections in Multi‐MWp Photovoltaic Power Stations

UV fluorescence imaging (UVF) has the potential to grow into a powerful, informative, and economically attractive inspection method for photovoltaic (PV) power stations. UVF demonstrates the ability to indicate differences in polymer types and degradation states in backsheets and encapsulation materials of PV modules. The data acquisition rate of UVF measurements is 10 to 15 times higher than that achieved by state‐of‐the‐art near‐infrared spectroscopy, enabling the mapping of the bill of materials (BoMs) and degradation state for every module in large PV arrays. Combinations of UVF with vibrational spectroscopies allow the polymer BoMs to be recognized and correlated with aging phenomena such as metal corrosion or potential induced degradation. UVF imaging can also be used for conventional visualization of nonmaterial‐related anomalies, such as hot cells or cell cracks. The low purchase cost of the equipment makes UVF an affordable and high‐throughput diagnostic method yielding comprehensive information that would otherwise only be accessible by combining several slower and more laborious methods. Automated UVF image analysis and refinements of correlations between different BoMs and UVF pattern geometry can further unlock the potential of UVF as a straightforward tool accessible to all stakeholders and promote proactive strategies for the optimization of the reliability and performance of PV power stations.

Extended Failure Mode and Effects Analysis for Development of Hot Desert Test Cycle Proposal

ABSTRACTA growing number of gigawatt‐scale photovoltaic (PV) power plants are being established in hot desert regions worldwide, which are favored for their vast available land, high solar irradiance, long sunshine hours, and relatively low maintenance needs. This study combines insights from global PV experts on degradation rates, failure mode and effects analysis (FMEA), and desert weather conditions to develop a hot desert test cycle (HDTC). The average degradation rate for PV modules installed in hot desert regions over the past 10 years is estimated to be around 1.63% per year. Eighteen failure modes were identified and analyzed using FMEA. According to the results, the main degradation mechanisms include UV light‐induced degradation (UVLID), thermomechanical failures in interconnects and fingers, light‐elevated temperature‐induced degradation (LeTID), and abrasion of the glass and antireflection coatings. A radar map comparison shows that desert environments experience UV exposure, ambient, and module temperatures that are more than twice as high as those in moderate climates. The HDTC protocol was developed based on FMEA results and desert‐specific weather conditions. It includes tests for desert UV exposure, temperature cycles, mechanical loads, and sand/brush abrasions. To ensure consistency across countries, there is a plan to establish an internationally recognized standard that complements existing IEC standards. As the industry grows, desert regions are expected to place greater emphasis on adopting desert‐specific testing standards for the qualification and evaluation of PV modules.

Sizing large-scale industrial heat pump for heat recovery from treated municipal sewage in coal-fired district heating system

Electrification of district heating and deep integration of sectors of national economies are fundamental elements of the future smart energy systems. This paper discusses the problem of optimal sizing of large-scale high-temperature heat pumps using treated sewage water as a heat source in a coal-fired district heating system. The study presents an approach to modelling of heat pump system that enables techno-economic analysis for investment decision making. Such analysis is enabled by a black-box-type identification model of the selected industrial heat pump. The model was developed based on the data generated by physical modelling of the heat pump using Ebsilon Professional software. In addition, it is proposed that the heat pump system is integrated with a dedicated photovoltaic power plant. The case study takes into consideration site-specific technical, economic, ecological, and legal constraints, weather conditions, hydraulic performance of the heat-ing network, and variability of loads within the sewage and the district heating systems. The results revealed that the proposed modelling approach is effective regarding multiple simulations and system optimisation. In addition, it was found that large-scale heat pump projects can be technically feasible and profitable if the heat pump is appropriately sized and operated. In the given case, the optimum size of the heat pump for a city of around 180 000 inhabitants is around 12 MW under maximum winter load.

Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques

Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plant located in Italy and used the relevant climate series to determine the quantity of energy produced. We forecast the quantity of energy as well as income through machine learning techniques and more traditional statistical and econometric models. We evaluate the results by splitting the dataset into estimation windows and test windows, and using a backtesting technique. In particular, we found evidence that ML regression techniques outperform results obtained with traditional econometric models. Regarding the models used to achieve this goal, the objective is not to propose original models but to verify the effectiveness of the most recent machine learning models for this important application, and to compare them with more classic linear regression techniques.

Photovoltaic Power Station Impacts on the Benthic Ecosystem and Sediment Carbon Storage in Tidal Flats in China.

Photovoltaic power is a rapidly growing component of the renewable energy sector. Photovoltaic power stations (PVPSs) on coastal tidal flats offer benefits, but the lack of information on the effects of PVPSs on benthic ecosystems and sediment carbon storage can hamper the development of eco-friendly renewable energy. We sampled the macrobenthos and sediment cores at a PVPS on a coastal tidal flat in eastern China. The biodiversity indicators and benthic ecological quality based on macrobenthos were mostly higher under the photovoltaic panels than elsewhere. These variations were primarily driven by pH, sediment grain size, and chlorophyll-a content. However, the PVPS had exerted a considerable influence on the macrobenthic community structure. Furthermore, the carbon stocks in the sediment cores from under the photovoltaic panels were similar to those in the reference sites. These results suggest that this PVPS has not had discernible short-term adverse effects on the benthic ecosystems or sediment carbon storage of the tidal flat. Nevertheless, the potentially long-term and cascading risks throughout the ecosystem warrant caution. Therefore, we recommend that policymakers adopt a cautious development strategy and implement long-term, high-frequency monitoring to ensure the sustainability of renewable energy production on coastal tidal flats.

Fault Section Identification for Hybrid Transmission Lines Considering the Weak-Feed Characteristics of Floating Photovoltaic Power Plant Inverters

The overhead line (OHL)–cable hybrid transmission line, which connects floating photovoltaic (PV) power plants, needs to be considered regarding whether to block reclosing operations or not. However, due to the weak-feed characteristics of PV inverters, existing methods are difficult to apply in this scenario. This paper proposes a criterion for fault section identification in the transmission lines of floating PV power plants based on traveling wave power and the zero-sequence impedance angle. Firstly, the fault current characteristics of photovoltaic inverters under dual-vector control are analyzed, and the applicability of the sequence component impedance directional criterion in this scenario is discussed. Then, the transmission, refraction, and reflection processes of traveling waves in OHL–cable hybrid lines are analyzed, and a traveling wave energy criterion is designed to determine the fault section. Finally, based on the scope of application of the zero-sequence impedance angle and traveling wave energy criterion, a fault section identification method for the hybrid lines of floating PV power plants is established. A deployment method for the proposed method, based on feeder terminal units (FTUs) at the connection points between the OHL and cable is proposed. This method identifies fault sections through traveling waves and zero-sequence impedance angles, which are unaffected by PV week feed characteristics, can be applied to all the AC fault types, and do not rely on multi-terminal synchronous sampling. The proposed method is verified on a 1MW PV system built in the PSCAD.

A refined method for optimising inverter loading ratio in utility-scale photovoltaic power plant

This paper proposes a novel approach for designing the inverter loading ratio (ILR) for utility-scale PV systems. As the first of its kind, a deterministic approach is proposed for dealing with such a design issue. The design objective is to maximise the economic reliability of the photovoltaic (PV) Plants. The problem is reformulated using a linear programming (LP) technique. The proposed solution allows dealing with each system individually and requires only the information of the DC/AC power conversion unit system. The results are tailored for utility-scale PV systems but can also be easily revamped for residential and private/industrial installations. The results were validated using a real-world case study of a utility-scale PV power plant in Ireland, operating under the Renewable Electricity Support Scheme (REFIT) feed-in tariff. These results were compared with the design method from the commercial PVsys software. In the proposed case study, reducing the ILR value from 1.4656 (as designed by PVsys) to 1.4528 led to an increase in annual profit annuities, demonstrating the effectiveness of the proposed approach.

Iterative Methods for Nonlinear Systems Applied to Grid Impedance Estimation: A Comparative Study

In this work, a comparative study is presented to evaluate the performance of iterative methods to solve nonlinear systems applied to grid impedance estimation. The iterative methods of Newton-Raphson, Potra-Pták, and Chun were embedded in the control system of a three-phase inverter supported by a photovoltaic plant connected to the grid. The adopted impedance estimation technique consists of successive variation of the power injected into the grid, more precisely, at three different levels. The voltage and current amplitudes at the point of common coupling~(PCC) are monitored and serve as input for the iterative methods, which, after processing them, provide an estimate of the grid impedance. To compare the performance between the methods, the following merit figures were listed: execution time, number of iterations required to deliver the estimates, percentage error, efficiency index, computational efficiency, computational efficiency index, and stability of the iterative method. The results presented were obtained through real-time simulations. From that, it was possible to conclude about the method with the best performance, thus contributing to greater assertiveness on the part of designers when choosing the most efficient iterative method to be embedded in a microcontroller for grid impedance estimation purposes.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Shedding light on biodiversity: reviewing existing knowledge and exploring hypothesised impacts of agrophotovoltaics.

The growing demand for energy and the shift towards green energy solutions have led to the conversion of open spaces and agricultural fields into photovoltaic (PV) power plants, exacerbating the "food-energy-environment" trilemma. Agrophotovoltaics (APVs), a dual-use system combining agriculture and energy production on the same land, presents a potential solution to this challenge. While the environmental impacts of ground-mounted utility-scale PV (USPV) power plants and the effects of APV systems on agricultural yields have been extensively studied and reviewed, the implications for wildlife and biodiversity remain largely unexplored. This knowledge gap is pressing, given the accelerated global adoption of APV systems and the urgency of understanding their broader ecological consequences. In this concise review, we synthesise existing literature on the impacts of USPV installations on biodiversity and the effects of APV on crop production. Building on these foundations, we propose novel hypotheses concerning the potential pathways and mechanisms through which APV systems may influence biodiversity. We explore the complex interactions between agroecosystems and natural ecosystems, examining both direct and indirect effects. Our review culminates in a set of key research questions designed to guide future studies on the biodiversity outcomes of APV deployment. Future research should comprehensively address factors such as habitat type, climate, spatial scale, technology, and agricultural practices, as well as the overarching impacts of climate change. By highlighting the importance of these variables, we aim to facilitate a nuanced understanding of how APV systems can either support or undermine biodiversity. This work not only underscores the critical need for empirical studies in this emerging field but also sets the stage for more informed and sustainable implementation of APV technologies.

Employing battery energy storage systems for flexible ramping products in a fully renewable energy power grid: A market mechanism and strategy analysis through multi-Agent Markov games

In high-proportion renewable energy power systems, flexible ramping products (FRPs) are critical for mitigating the volatility of renewable energy outputs and enhancing the adaptability of power systems. This paper aims to explore a fully renewable energy power system, with a battery energy storage system (BESS) as the sole provider of FRPs. An innovative market mechanism and new approaches to market equilibrium analysis are introduced. Initially, a common day-ahead (DA) market is established, covering both electricity and ancillary services with FRPs. The setup allows FRP providers to make bidding decisions and get rewarded based on actual ramping mileages. A bi-level multi-agent Markov game (MG) model is formulated. The upper level integrates the proposed strategies of wind power plants (WPP), photovoltaic power plants (PVP), and BESS, while the lower level encompasses the joint market clearing mechanism. An iterative solution algorithm, using reinforcement learning, is also introduced to address the model, effectively circumventing the privacy concerns associated with traditional mathematical programming methods. The proposed solution models and strategies are validated and demonstrated to be effective through two illustrative examples, highlighting their application and relevance in fully renewable energy power systems.

Small-Sample Short-Term Photovoltaic Output Prediction Model Based on GRA-SSA-GNNM Method

The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the short-term prediction of small-sample PV power generation and the low prediction accuracy in the previous research. A hybrid prediction model based on grey relation analysis (GRA) combined with the sparrow search algorithm (SSA) and the grey neural network model (GNNM) is proposed. In this paper, GRA is utilized to reduce the dimension of meteorological features of the samples. Then, the GNNM is used to perform regression analysis on the input features after reducing the dimension of meteorological features of the samples, and the parameters of the GNNM are optimized via SSA. A limited dataset was used to compare several models in different seasons and weather conditions. The prediction results agree well with the data from the PV power plant in Xinjiang, indicating that the GRA-SSA-GNNM model developed in this work effectively achieves a high precision estimation in short-term PV power generation output prediction and has a promising application in this field.

Energy Efficiency and Economic Survivance Appraisal of a 375 kWp Rooftop Solar PV System Under Hot and Dry Indian Climate

ABSTRACTIndia's rooftop solar photovoltaic (PV) installations are experiencing rapid growth due to favorable regulations. As climate change becomes a growing concern, researchers are turning their attention to the effects of weather patterns on the performance of rooftop solar panels, and also to optimize their efficiency in a changing environment. Consequently, industry players in the solar sector have been conducting performance validation and feasibility assessments of these plants. A 375 kWp rooftop PV plant is studied as a case example from April 1, 2022 to March 31, 2023, generating 543,666 kWh annually for the grid. The NMBE and MBE were assessed using simulation tools like PVGIS and PV Watts. In addition, a cost–benefit analysis of carbon credits was conducted with and without their inclusion. The energy payback time is calculated at 4.5 years post‐inclusion. Over a 25‐year lifespan, the embodied energy of the PV plant amounts to 2,552,265 kWh. This plant can mitigate CO2 emissions annually by 10,173.57 tons which is equivalent to INR 5,464,925. The current study highlights both environmental and economic benefits by incorporating carbon credits into the project. Further advancement in simulation tools, PV technologies, climate change adaptations are expected which will improve the rooftop system efficiency with shorten pay pabck periods and maximum reductions in CO2 emissions.

Trend‐Based Predictive Maintenance and Fault Detection Analytics for Photovoltaic Power Plants

Optimized predictive maintenance in photovoltaic (PV) systems is crucial for ensuring prolonged operational performance and cost‐effective operation and maintenance (O&M). Even though failure detection methods have already been developed, the main challenge remains the lack of predictive maintenance strategies to accurately forecast underperformance conditions. The scope of this work is to develop a predictive maintenance and failure detection routine for assessing the health status of PV systems. The workflow consists of the eXtreme gradient boosting algorithm for modeling the PV performance, the one‐class support vector machine algorithm for fault detection, and the Facebook Prophet algorithm for forecasting PV performance trends and generating maintenance alerts. The developed data‐driven routine analyzes performance trend deviations and it is validated using a historical dataset from a utility‐scale PV power plant in Greece. The obtained results show the effectiveness of the developed workflow in detecting fault conditions, achieving a sensitivity of 96.9%. Additionally, the results demonstrate the workflow's ability to generate predictive maintenance alerts up to 7 days in advance, yielding a sensitivity of 92.9%. Finally, the study provides useful insights that enhance operators’ efficiency in conducting O&M activities.

Exploration of the application of solar energy technology in the field of new energy vehicles

Due to the harsh effects of fossil fuels on the world, researchers are paying more attention to the application of new energy sources. This paper explores the possibility to support electric vehicles with solar energy by demonstrating the design of a solar cooling system and a solar parking lot in a large flat area and workspace. The use of solar energy in electric vehicles can help control some systems such as cooling systems. Solar panels are used in some large flat parking area to collect and invert solar energy for parking electric vehicles’ charging. Solar panels are able to help charge electric vehicles during the workday by setting them on the surface of working area. However, due to the limitations of solar energy, solar panels are very much in need of good weather and direct sunlight. Therefore, solar energy cannot be the only source to support other powered vehicles. In the future, developments in other areas such as inverters and converters, solar panels, and nanotechnology may make the use of solar energy in green energy vehicles possible.

A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China’s 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.

Renewable Solar Energy Facilities in South America—The Road to a Low-Carbon Sustainable Energy Matrix: A Systematic Review

South America is a place on the planet that stands out with enormous potential linked to renewable energies. Countries in this region have developed private investment projects to carry out an energy transition from fossil energies to clean energies and contribute to climate change mitigation. The sun resource is one of the more abundant sources of renewable energies that stands out in South America, especially in the Atacama Desert. In this context, South American countries are developing sustainable actions/strategies linked to implementing solar photovoltaic (PV) and concentrated solar power (CSP) facilities and achieving carbon neutrality for the year 2050. As a result, this systematic review presents the progress, new trends, and the road to a sustainable paradigm with disruptive innovations like artificial intelligence, robots, and unmanned aerial vehicles (UAVs) for solar energy facilities in the region. According to the findings, solar energy infrastructure was applied in South America during the global climate change crisis era. Different levels of implementation in solar photovoltaic (PV) facilities have been reached in each country, with the region being a worldwide research and development (R&D) hotspot. Also, high potential exists for concentrated solar power (CSP) facilities considering the technology evolution, and for the implementation of the hybridization of solar photovoltaic (PV) facilities with onshore wind farm infrastructures, decreasing the capital/operation costs of the projects. Finally, synergy between solar energy infrastructures with emerging technologies linked with low-carbon economies like battery energy storage systems (BESSs) and the use of floating solar PV plants looks like a promising sustainable solution.

Optimal spatial arrangement of modules for large‐scale photovoltaic farms in complex topography

AbstractThe existing methods for determining the module arrangement in photovoltaic (PV) farms are considered insufficient as they are generally limited to the environment of flat ground without considering both physical and electrical factors. The orientations of PV modules may be very diverse when installed in places with complex topography, e.g. mountains and abandoned mine sites. Thus, the received irradiance by the modules is inconsistent directly results in current differences and hence leads to significant mismatch loss in the PV arrays. This paper proposes a solution to determine the most appropriate combination of tilts and orientations of PV modules as well as the arrangement of PV arrays. The complex topographies are fully considered to minimize the mismatch loss phenomenon, and hence the power generation degradation. The solution adopts a set of models, i.e. the irradiation model for the calculation of optimal module tilts and orientations, the shadow model for shadow effect analysis, and the mismatch model for mismatch condition analysis. A two‐layer multi‐objective optimization is implemented for the optimal arrangement. The proposed solution is assessed through the case study of a 30 MW PV farm. The result confirms the effectiveness of the proposed solution for the optimal spatial module placement.

Energy Flexibility in Aluminium Smelting: A Long-Term Feasibility Study Based on the Prospects of Electricity Load and Photovoltaic Production

This paper investigates the economic feasibility of utilising energy flexibility in aluminium production as a viable solution to leverage electricity surpluses arising from the increasing number of photovoltaic (PV) system installations. Future trends suggest that the generation capacity of PV systems will soon surpass consumption, leading to significant electricity surpluses, particularly during the summer. This surplus electricity, which is anticipated to be available at low prices, offers a unique opportunity to evaluate different investment and utilisation scenarios for aluminium production while simultaneously decreasing its environmental impact. The results demonstrate that, despite their high initial investment cost, large-scale PV power plants can potentially deliver maximum economic gains over a ten-year period. Conversely, the direct utilisation of surpluses without substantial investment can yield savings of up to EUR 17 million within the same time frame for Slovenia’s case with an aluminium smelter, which has a maximum power usage of 60 MW. The findings of this study have significant implications in terms of shaping future energy strategies and policies, emphasizing the value of integrating renewable energy sources and industrial processes for enhanced economic and environmental outcomes.

Assessment of Insulation Coordination and Overvoltage for Utility Girds Integrated with Solar Farms

Due to the economic and environmental concerns associated with fossil fuels, many government and private organizations are progressively shifting towards the integration of solar farms with Utility Grids. However, these systems are facing insulation failure issues due to internal and external transient overvoltage’s, in which their shape, magnitude, and duration are unpredictable, and consequently, the insulation stress also becomes unpredictable. To ensure the safety and integrity of the system against any transient overvoltage event, it is important to carry out an insulation coordination analysis. The primary goal of this research work is to achieve this optimization in an economically viable manner, ensuring both operational stability and cost-effectiveness in the design of electrical equipment like surge Arresters. The research work presented in the literature does not fully evaluate all International Electrotechnical Commission (IEC) overvoltage classes as specified in the insulation coordination standards for Utility Grids integrated with solar farms. Therefore, this research paper investigates the impact of various transient and switching overvoltage conditions, as defined in the IEC 60071.4 Insulation Coordination Standard at the Solar and Utility Grid Electrical power system using PSCAD 4.6/EMTP Software. Five distinct simulation scenarios were developed to assess the systems’ resilience against insulation stress events. The proposed system was also examined with and without the application of a lightning surge arrester.