Photovoltaic Plants Research Articles

Advancing photovoltaic panel temperature forecasting: A comparative study of numerical simulation and machine learning in two types of PV power plant

Photovoltaic (PV) panel temperature dynamic monitoring and forecasting is important for managing and maintaining of PV power plant. However, it is uncommon to use a variety of methods to predict and evaluate the panel temperature of different types of PV power plants. Therefore, this study aims to advance PV panel temperature forecasting through a comparative analysis of numerical simulation and machine learning models in two types of PV power plants: land- and water-mounted. The results indicate that PV panel temperature condition for two types of PV power plants can be well captured by the numerical simulation (NS) and machine learning, except for the NS in water-mounted PV power plant (R2 with 0.66). Models perform better in land-mounted PV power plants, with Random Forest Regression (RFR) and ResNet models demonstrating superior accuracy across both environments. While all models exhibit some degree of deviation from normal distribution in residuals, RFR and ResNet show the least deviation and lowest prediction errors, highlighting their robustness in forecasting PV panel temperatures. This study can provide valuable insights for predicting PV panel temperatures and for the management and maintenance of PV power plants.

Fatigue Behavior of H-Section Piles under Lateral Loads in Cohesive Soil

Most structures supporting solar panels are found on thin-walled metal piles partially driven into the ground, optimizing costs and construction time. These pile foundations are subjected to repetitive lateral loads from various external forces, such as wind, which can compromise the integrity of the pile-soil system. Given that the expected operational lifespan of photovoltaic solar plants is generally 20–30 years, predicting their service life under fatigue loads is crucial. This research analyzes the response of H-section piles to lateral fatigue loads in cohesive rigid soils through four field tests, subjected to load cycles of 55%, 72%, and 77% of the static failure load, corresponding to maximum loads of 25 kN, 32 kN, and 35 kN, respectively. Additionally, the effect of load cycles on the degradation of pile-soil adhesion is studied through two pull-out tests following cyclic tests. This study reveals that soil fatigue does not occur under repetitive loads and that soil stiffness remains constant once the cycles causing soil compaction have been overcome. Nevertheless, the accumulated plastic deflection of the soil increases steadily once soil compaction occurs due to cyclic loading. The implications of these results on the fatigue life of photovoltaic solar panel foundations are discussed.

Medium- and Long-Term Distributed Photovoltaic Power Prediction Based on Multiple Time Series Feature and Multiple-Model Fusion

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R2, and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust.

Combining observations and simulations to investigate the small-scale variability of surface solar irradiance under continental cumulus clouds

Abstract. The amount of solar radiation reaching the Earth surface (surface solar irradiance, SSI) is critical for a variety of applications, ranging from surface–atmosphere interactions to solar energy. SSI is characterized by a large spatiotemporal variability, in particular in the presence of cumulus clouds. This results in complex spatial patterns of shadows and sunlight directly related to clouds' geometry and physical properties. Although key in many respects, the instantaneous spatial distribution of SSI remains largely unexplored. Here, we use unique observations from a dense network of pyranometers deployed during the HOPE field campaign to investigate the SSI spatial distribution. For cumulus scenes, bimodal distributions are found, with one mode corresponding to cloud shadows and the other to sunlit areas with enhanced SSI exceeding clear-sky values. Combining large-eddy simulations of cumulus clouds with Monte Carlo ray tracing, we demonstrate the capability of advanced numerical tools to reproduce the observed distributions and quantify the impact of cloud geometrical and physical properties on both modes. In particular, cloud cover strongly modulates their amplitudes, in addition to their position and width, which are also sensitive to cloud height, geometrical depth, and liquid water content. Combining observations and simulations, we also explore sampling strategies to estimate the SSI spatial distribution with a limited number of sensors, suggesting that 10 pyranometers integrated over 10 min can capture most details of the full distribution. Such a strategy could be used for future campaigns to further investigate SSI distributions and their impact on land–atmosphere exchanges or photovoltaic farm management.

Modular Microgrid Technology with a Single Development Environment Per Life Cycle

The life cycle of a microgrid covers all the stages from idea to implementation, through exploitation until the end of its life, with a lifespan of around 25 years. Covering them usually requires several software tools, which can make the integration of results from different stages difficult and may imply costs being hard to estimate from the beginning of a project. This paper proposes a unified platform composed of four modules developed in MATLAB 2022b, designed to assist all the processes a microgrid passes through during its lifetime. This entire platform can be used by a user with low IT knowledge, because it is completed with fill-in-the-blank alone, as a major advantage. The authors detail the architecture, functions and development of the platform, either by highlighting the novel integration of existing MATLAB tools or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. By consolidating processes into a single platform, the proposed solution enhances integration, reduces complexity and provides better cost predictability throughout the project’s duration. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting of a photovoltaic plant, and an office building as the consumer and energy storage units. This platform has also been developed by involving students within summer internships, as a process strengthening the cooperation between industry and academia. Being an open-source application, the platform will be used within the educational process, where the students will have the possibility to add functionalities, improve the graphical representation, create new reports, etc.

Photovoltaic Maximum Power Point Tracking Technology Based on Improved Perturbation Observation Method and Backstepping Algorithm

Photovoltaic power generation systems mainly use the maximum power tracking (MPPT) controller to adjust the voltage and current of the solar cells in the photovoltaic array, so that the photovoltaic array runs at the maximum power point (MPP) to achieve the purpose of maximum power output. At present, photovoltaic power stations mainly adopt the traditional method to track the maximum power point, but this fixed step method easily causes output power oscillation of the photovoltaic array when tracking the maximum power point, and it easily falls into the local extreme point under partial shadow conditions. In order to solve these problems, this paper proposes an improved perturbation observation method and backstepping method (IP&O-backstepping) to replace the traditional method applied to the MPPT controller to optimize the operating state of the solar cell, thereby improving the output power point of the photovoltaic array and increasing the output power of the photovoltaic array. The algorithm first uses the improved perturbation and observation (IP&O) method to search the maximum power point of the photovoltaic array and output the reference voltage. Secondly, the reference voltage is input into the backstepping algorithm for voltage tracking. Finally, the algorithm tracks the reference voltage and makes the photovoltaic array operate at the maximum power point. The simulation is carried out by using MATLAB/Simulink. The IP&O-backstepping algorithm is compared with the intelligent algorithm and the traditional method, and the results show that compared to the above algorithm, the IP&O-backstepping algorithm can not only track the maximum power point of the photovoltaic array, but also has a faster tracking speed, and the output power has almost no oscillation when the photovoltaic array runs at the maximum power point.

Steps for the Realization of a Medium-power Photovoltaic Park

The design of photovoltaic parks has become in Romania, now, one of the most profitable and safe investments in the field of renewable energy sources, not only at present, but even more so in the future. Solar photovoltaic parks are favored by maximum exposure to solar radiation, a minimal impact on the environment, allowing the production of energy with a cost per kWp lower than that of other installations. The execution period of a photovoltaic park varies between 3-6 months depending on location and weather conditions. Romania has a high degree of solar radiation, suitable for investment in photovoltaic parks. There are three categories of documents required for the establishment of a photovoltaic park: those related of the land on which the park is build, the solution study for obtaining the technical approval for connection (ATR) to the electrical grid and obtaining the establishment authorization. The realization costs involved in building and designing a photovoltaic park are between 1.1-1.9 Euro/Watt. The quality of the equipment used in construction, the shape of the land, the costs of connection to the national electrical grid, are factors that can constitute the cost of realization. Another important factor in terms of income is the level of operational expenses, which is between 2% and 8% of the annual income and includes security, equipment and land maintenance, system monitoring. The paper will present a technical and economic calculation for a photovoltaic park with a power of 400 kW.

Deep Learning-Based Dust Detection on Solar Panels: A Low-Cost Sustainable Solution for Increased Solar Power Generation

The world is shifting towards renewable energy sources due to the harmful effects of fossils fuel-based power generation in the form of global warming and climate change. When it comes to renewable energy sources, solar-based power generation remains on top of the list as a clean and carbon cutting alternative to the fossil fuels. Naturally, the sites chosen for installing solar parks to generate electricity are the ones that get maximum solar radiance throughout the year. Consequently, such sites offer challenges for the solar panels such as increased temperature, humidity and high dust levels that negatively affect their power generation capability. In this work, we are more concerned with the detection of dust from the images of the solar panels so that the cleaning process can be done in time to avoid power loses due to dust accumulation on the surface of solar panels. To this end, we utilize state-of-art deep learning-based image classification models and evaluate them on a publicly available dataset to identify the one that gives maximum classification accuracy for dusty solar panel detection. We utilize pre-trained models of 20 deep learning models to encode the images that are then used to train and validate four variants of a support vector machine. Among the 20 models, we get the maximum classification of 86.79% when the images are encoded with the pre-trained model of DenseNet169 and then use these encodings with a linear SVM for image classification.

Investigating the performance of water-mounted solar photo-voltaic systems using different simulation tools

Water-mounted solar photovoltaics plants, which include canal-top and floating systems, offer several advantages over traditional ground mounted systems. However, studying their feasibility can be challenging due to limitations in commercial simulation tools. These tools often rely on unrealistic parameters, and studies comparing them have concluded that they may not be suitable for simulating these newer systems. This research addresses the above-mentioned gap by developing improved simulation methods for water–mounted solar plants. The study compared the actual performance of a 1 MW canal-top system and a 732 kW floating system in India with simulations from six different commercial tools. The analysis revealed that PVsyst and SolarGIS were the most accurate in predicting performance for the canal-top system, while PVsol and SolarGIS–Prospect performed best for the floating system. By following comprehensive methodology outlined in this study, minimal Root–Mean–Square error values ranging from 0.74 to 3.33 for the canal-top system and 2.13 to 5.20 for the floating system were achieved for different parameters. These results highlight that accurate performance predictions depend on three factors: the precision of meteorological data, the capabilities of the simulation tool for water-mounted systems, and the tool’s ability to be customized for specific system details. This study helps solar developers and researchers choose the most suitable simulation tools and customize them for feasibility studies. It also helps stakeholders predict the energy output of existing water–mounted systems more accurately, reducing the risk of penalties due to deviations from predicted output.

Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model

Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the accuracy of short-term power prediction forecasts using a model called Transformer Bi-LSTM (Bidirectional Long Short-Term Memory). This model, which combines elements from the transformer architecture and bidirectional LSTM (Long–Short-Term Memory), is evaluated using two strategies: the first strategy makes a direct prediction using meteorological data, while the second employs a chain of deep learning models based on transfer learning, thus simulating the traditional physical chain model. The proposed approach improves performance and allows you to incorporate physical models to refine forecasts. The results outperform existing methods on metrics such as mean absolute error, specifically by around 24%, which could positively impact power grid operation and solar adoption.

Combined cycle gas turbine (CCGT) plants utilizing methane-hydrogen blends represent a significant element in Australia’s journey toward achieving net-zero emissions

To deliver 570 TWh of dispatchable electricity to the grid in Australia, it is necessary to build up wind and solar photovoltaic power plants generating capacity of 325 GW, and a long-term hydrogen energy storage system comprising 50–150 GW of electrolyzers, 50 TWh of hydrogen storage, and 165 GW of hydrogen power generation. While the addition of other renewable energy producers and energy storage technologies effective on shorter scales are certainly welcome and positively impact the above demand, the inclusion of inter-annual variability and safety negatively impacts these numbers. Fuel cells are currently less advanced compared to electrolyzers and hydrogen storage in salt caverns. Expanding hydrogen power generation will require exploring additional opportunities. Here we advocate for methane-hydrogen Combined Cycle Gas Turbine (CCGT) plants. Their adaptability to effortlessly integrate methane and green hydrogen mixtures positions them as a key player in the evolution toward net zero. This transition will be complete once a fully established wind and solar photovoltaic generation, coupled with an efficient hydrogen energy storage system, is in place. Notably, these CCGT plants consistently yield the lowest Life Cycle Analysis (LCA) carbon dioxide (CO2) emissions during the grid’s progression toward net zero.

The Implementation of A Mini-Generation Photovoltaic Power Plant in A Brazilian Federal University

The Implementation of A Mini-Generation Photovoltaic Power Plant in A Brazilian Federal University

Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants.

Investigation of the cooling effect of wind on rooftop PV power plants

This study investigates the cooling of PV panels installed on the roof of a 5.9 MW power plant in Bursa, Turkey, under varying wind conditions. Meteorological measurements were conducted during the summer period to analyse the cooling effects. When wind speed differences were below 0.5 m/s, the cooling effect on the PV panels ranged between 2-3°C. However, when the wind speed differences approached 1 m/s, the cooling effect increased up to 7°C. At lower wind speeds, winds blowing from behind the panels provided better cooling, whereas side winds offered improved performance as wind speed increased. Due to the roof slope and the minimal gap between the panels and the roof, winds from behind were less effective, as they could not penetrate the back of the PV panels. Side winds, on the other hand, faced no difficulty in flowing over the panel surfaces but lost their cooling effect as they passed along the long rows of panels. These findings suggest that during the design phase of PV power plants, high wind speed locations should be prioritized, and optimal configurations should be implemented to ensure uniform wind access to all PV arrays.

Predictive control technique for solar photovoltaic power forecasting

An accurate estimation of photovoltaic (PV) power production is crucial for organising and regulating solar PV power plants. The suitable prediction is often affected by the variable nature of solar resources, system location and some internal/external disturbances, such as system effectiveness, climatic factors, etc. This paper develops a novel strategy for applying a predictive control technique to PV power forecasting applications in a smart grid environment. The strategy develops the model predictive control (MPC) under demand response (DR) and some data-driven methods. It has been found that it is challenging to model an MPC for solar power forecasting regardless of its robustness and ability to handle constraints and disturbance. Thus, an optimal quadratic performance index-based MPC scheme is formulated to model a forecasting method for a PV power prediction. This strategy is then compared with some machine learning approaches. The developed strategies solve the problem of accurately estimating the direct current (DC) power yielded from the PV plant in a real-world implementation. The study also considers external disturbances to evaluate the significance of the developed methods for a suitable forecast. Therefore, this study optimally demonstrates that an accurate solar PV DC power prediction can relatively be estimated with an appropriate strategy, such as MPC and MLs, considering the system disturbances. This study also offers promising results for intelligent and real-time energy resource estimation that assist in developing the solar power sector.

Floating body motion coupling study and economic evaluation of different floating photovoltaic arrangement schemes under hybrid solar-wind power generation

Abstract Reducing the number of anchor points and moorings for floating platforms is one of the ways to reduce the integration and maintenance costs of floating offshore wind and solar farms. This paper focuses on the technical feasibility and economic viability of reducing the number of anchor points and moorings for floating offshore PV platforms by using a monopile of a stationary wind turbine as a mooring anchor point and introducing a shared line system. In this paper, three different floating solar layouts and their corresponding mooring systems are numerically evaluated and optimized under different environmental loading conditions using the commercial software OrcaFlex. The results of the study show that the implementation of a shared mooring solution maximizes the cost-effectiveness economically while technically ensuring stable operation.

Use of satellite data to determine the cloud optical depths present during overirradiance conditions

Abstract Overirradiance conditions can negatively impact the operation of photovoltaic systems if no protective measures have been implemented, leading to potential damages and economic losses in photovoltaic generation plants. Current simulation models attempt to understand the mechanism of overirradiance conditions. However, their observations still differ significantly from experimental ones, emphasizing the need to better understand the two main hypotheses that account for overirradiance events: reflection at the edges of thick clouds and Mie scattering in thin clouds. This paper studies the qualitative correlation between the global tilted irradiance measured by a spectroradiometer on the surface and the optical depth of the clouds measured by the GOES-16 satellite to shed more light on this phenomenon. Our results show a good qualitative correlation between the global tilted irradiance and the optical depth of the clouds present during overirradiance events. We also show that all overirradiance conditions occurred when thick clouds were present. These results indicate that the overirradiance events analyzed have been produced predominantly by reflections at the edges of thick clouds, supporting the hypothesis that the increase in global irradiance is mainly due to a substantial increase in direct irradiance.

Life cycle assessment of electricity from wind, photovoltaic and biogas from maize in combination with area-specific energy yields – a case study for Germany

Abstract Due to the massive expansion of renewable energy-based production capacity, the benefits and drawbacks of wind turbines, ground-mounted photovoltaic (PV) and biogas plants are currently being discussed in Germany and elsewhere. The expansion of renewable energy-based plants is competing with other uses for land area. In addition to area-specific energy yields, the environmental impact of renewables is increasingly gaining awareness. Existing research on the area-specific energy yields and environmental impact for wind, PV and biogas lacks comparability due to factors including time, location, and scale. This study addresses this research gap by combining life cycle assessment (LCA) to compare potential environmental impacts with an area-specific energy yield assessment of wind, ground-mounted PV and biogas from maize in Germany. The LCA includes an assessment of eleven midpoint and three endpoint impact indicators, while the area-specific energy yield is assessed on the basis of both gross and net area-specific energy yield. The LCA results indicate the lowest impact for wind, followed by PV and biogas. This ranking is consistent across all three endpoint and nine out of eleven midpoint categories. The same ranking also applies to the area-specific energy yield, with wind producing the most and biogas the least gross and net energy per area. These results indicate that the current political support in Germany for biogas from maize for electricity generation should be thoroughly re-evaluated in view of the more favourable alternatives, wind and PV. The findings also provide relevant insights for other regions with similar boundary conditions.

Annual relative performance degradation in photovoltaic solar plants

This study aims to assess annual relative performance degradation σ to be used in the computations of the Levelised Cost of Energy (LCOE) of new plants based on the standard year for weather and solar resource. The assessment is based on the experimental data of power generation, weather, and resource for different years, and simulations of power generation by using the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) software. The raw data of power generation for 53 different plants spanning about one decade show a σ assessed at less than 0.29%. This makes it reasonable to assume in SAM σ=0% to σ=0.29%. This is less than the default σ=0.5% currently used in SAM based on limited outdated data. The updated performance degradation factor diminished to σ=0-0.29% reduces the computed LCOE for typical new projects in the United States from 2.86 to 2.74-2.80 ¢/kWh. Correction of the σ trends for interannual variability of weather and resource is unnecessary, providing enough years of operation are covered, given the inaccuracies in the model and the supporting data, the mitigation by management of the performance changes, and the complex phenomena correlated to the change of weather and irradiance affecting the power output in different directions. The reduced σ is the result of a significant product improvement over the last decades, especially for large power plants, compared to the plant which provided data for the prior correlation of performance degradation, and much better management and maintenance of the plants.

A novel data-driven state evaluation approach for photovoltaic arrays in uncertain shading scenarios

Accurately determining the operating state of photovoltaic (PV) power-generation equipment and providing accurate support for maintenance management requires evaluation of the states of PV arrays under partial shading conditions. Results of such evaluation enable the optimization of maintenance measures to reduce maintenance costs and extend the lifespan of PV modules, thereby increasing the overall revenue of the PV power station. Therefore, this study proposes a novel state-evaluation approach based on current-voltage (I-V) curves under partial shading conditions. Based on the size of the pixel values in this curve, the proposed novel state-evaluation was performed using the proportion of the values of the normal and shaded curves under the same environmental conditions. Specifically, the feature variables (open-circuit voltage, short-circuit current) and pixel values within the I-V curve were analyzed and calculated using a sensitivity algorithm and computer vision algorithms (Canny edge-detection algorithm and Green's theorem). Based on the mapping relationship between feature variables and pixels, an improved Gaussian-process regression method with a combined kernel function (dot product and Matérn function) was proposed to predict the pixel values under the corresponding normal curve. This combined kernel function effectively captured causal relationships and handled outliers in the test dataset to improve the robustness of the model. Finally, the ratios of the pixel values were calculated using the predicted pixels within the normal curve and the calculated pixels within the shading curve. Experiments demonstrated that the root mean square error (RMSE), mean absolute error (MAE), R squared (R2) and log likelihood of the improved Gaussian-process regression method reached 0.011, 0.0086, 0.9996, and 24.7096, respectively.